SageMaker ********* Client ====== class SageMaker.Client A low-level client representing Amazon SageMaker Service Provides APIs for creating and managing SageMaker resources. Other Resources: * SageMaker Developer Guide * Amazon Augmented AI Runtime API Reference import boto3 client = boto3.client('sagemaker') These are the available methods: * add_association * add_tags * associate_trial_component * attach_cluster_node_volume * batch_add_cluster_nodes * batch_delete_cluster_nodes * batch_describe_model_package * can_paginate * close * create_action * create_algorithm * create_app * create_app_image_config * create_artifact * create_auto_ml_job * create_auto_ml_job_v2 * create_cluster * create_cluster_scheduler_config * create_code_repository * create_compilation_job * create_compute_quota * create_context * create_data_quality_job_definition * create_device_fleet * create_domain * create_edge_deployment_plan * create_edge_deployment_stage * create_edge_packaging_job * create_endpoint * create_endpoint_config * create_experiment * create_feature_group * create_flow_definition * create_hub * create_hub_content_presigned_urls * create_hub_content_reference * create_human_task_ui * create_hyper_parameter_tuning_job * create_image * create_image_version * create_inference_component * create_inference_experiment * create_inference_recommendations_job * create_labeling_job * create_mlflow_tracking_server * create_model * create_model_bias_job_definition * create_model_card * create_model_card_export_job * create_model_explainability_job_definition * create_model_package * create_model_package_group * create_model_quality_job_definition * create_monitoring_schedule * create_notebook_instance * create_notebook_instance_lifecycle_config * create_optimization_job * create_partner_app * create_partner_app_presigned_url * create_pipeline * create_presigned_domain_url * create_presigned_mlflow_tracking_server_url * create_presigned_notebook_instance_url * create_processing_job * create_project * create_space * create_studio_lifecycle_config * create_training_job * create_training_plan * create_transform_job * create_trial * create_trial_component * create_user_profile * create_workforce * create_workteam * delete_action * delete_algorithm * delete_app * delete_app_image_config * delete_artifact * delete_association * delete_cluster * delete_cluster_scheduler_config * delete_code_repository * delete_compilation_job * delete_compute_quota * delete_context * delete_data_quality_job_definition * delete_device_fleet * delete_domain * delete_edge_deployment_plan * delete_edge_deployment_stage * delete_endpoint * delete_endpoint_config * delete_experiment * delete_feature_group * delete_flow_definition * delete_hub * delete_hub_content * delete_hub_content_reference * delete_human_task_ui * delete_hyper_parameter_tuning_job * delete_image * delete_image_version * delete_inference_component * delete_inference_experiment * delete_mlflow_tracking_server * delete_model * delete_model_bias_job_definition * delete_model_card * delete_model_explainability_job_definition * delete_model_package * delete_model_package_group * delete_model_package_group_policy * delete_model_quality_job_definition * delete_monitoring_schedule * delete_notebook_instance * delete_notebook_instance_lifecycle_config * delete_optimization_job * delete_partner_app * delete_pipeline * delete_project * delete_space * delete_studio_lifecycle_config * delete_tags * delete_trial * delete_trial_component * delete_user_profile * delete_workforce * delete_workteam * deregister_devices * describe_action * describe_algorithm * describe_app * describe_app_image_config * describe_artifact * describe_auto_ml_job * describe_auto_ml_job_v2 * describe_cluster * describe_cluster_event * describe_cluster_node * describe_cluster_scheduler_config * describe_code_repository * describe_compilation_job * describe_compute_quota * describe_context * describe_data_quality_job_definition * describe_device * describe_device_fleet * describe_domain * describe_edge_deployment_plan * describe_edge_packaging_job * describe_endpoint * describe_endpoint_config * describe_experiment * describe_feature_group * describe_feature_metadata * describe_flow_definition * describe_hub * describe_hub_content * describe_human_task_ui * describe_hyper_parameter_tuning_job * describe_image * describe_image_version * describe_inference_component * describe_inference_experiment * describe_inference_recommendations_job * describe_labeling_job * describe_lineage_group * describe_mlflow_tracking_server * describe_model * describe_model_bias_job_definition * describe_model_card * describe_model_card_export_job * describe_model_explainability_job_definition * describe_model_package * describe_model_package_group * describe_model_quality_job_definition * describe_monitoring_schedule * describe_notebook_instance * describe_notebook_instance_lifecycle_config * describe_optimization_job * describe_partner_app * describe_pipeline * describe_pipeline_definition_for_execution * describe_pipeline_execution * describe_processing_job * describe_project * describe_reserved_capacity * describe_space * describe_studio_lifecycle_config * describe_subscribed_workteam * describe_training_job * describe_training_plan * describe_transform_job * describe_trial * describe_trial_component * describe_user_profile * describe_workforce * describe_workteam * detach_cluster_node_volume * disable_sagemaker_servicecatalog_portfolio * disassociate_trial_component * enable_sagemaker_servicecatalog_portfolio * get_device_fleet_report * get_lineage_group_policy * get_model_package_group_policy * get_paginator * get_sagemaker_servicecatalog_portfolio_status * get_scaling_configuration_recommendation * get_search_suggestions * get_waiter * import_hub_content * list_actions * list_algorithms * list_aliases * list_app_image_configs * list_apps * list_artifacts * list_associations * list_auto_ml_jobs * list_candidates_for_auto_ml_job * list_cluster_events * list_cluster_nodes * list_cluster_scheduler_configs * list_clusters * list_code_repositories * list_compilation_jobs * list_compute_quotas * list_contexts * list_data_quality_job_definitions * list_device_fleets * list_devices * list_domains * list_edge_deployment_plans * list_edge_packaging_jobs * list_endpoint_configs * list_endpoints * list_experiments * list_feature_groups * list_flow_definitions * list_hub_content_versions * list_hub_contents * list_hubs * list_human_task_uis * list_hyper_parameter_tuning_jobs * list_image_versions * list_images * list_inference_components * list_inference_experiments * list_inference_recommendations_job_steps * list_inference_recommendations_jobs * list_labeling_jobs * list_labeling_jobs_for_workteam * list_lineage_groups * list_mlflow_tracking_servers * list_model_bias_job_definitions * list_model_card_export_jobs * list_model_card_versions * list_model_cards * list_model_explainability_job_definitions * list_model_metadata * list_model_package_groups * list_model_packages * list_model_quality_job_definitions * list_models * list_monitoring_alert_history * list_monitoring_alerts * list_monitoring_executions * list_monitoring_schedules * list_notebook_instance_lifecycle_configs * list_notebook_instances * list_optimization_jobs * list_partner_apps * list_pipeline_execution_steps * list_pipeline_executions * list_pipeline_parameters_for_execution * list_pipeline_versions * list_pipelines * list_processing_jobs * list_projects * list_resource_catalogs * list_spaces * list_stage_devices * list_studio_lifecycle_configs * list_subscribed_workteams * list_tags * list_training_jobs * list_training_jobs_for_hyper_parameter_tuning_job * list_training_plans * list_transform_jobs * list_trial_components * list_trials * list_ultra_servers_by_reserved_capacity * list_user_profiles * list_workforces * list_workteams * put_model_package_group_policy * query_lineage * register_devices * render_ui_template * retry_pipeline_execution * search * search_training_plan_offerings * send_pipeline_execution_step_failure * send_pipeline_execution_step_success * start_edge_deployment_stage * start_inference_experiment * start_mlflow_tracking_server * start_monitoring_schedule * start_notebook_instance * start_pipeline_execution * start_session * stop_auto_ml_job * stop_compilation_job * stop_edge_deployment_stage * stop_edge_packaging_job * stop_hyper_parameter_tuning_job * stop_inference_experiment * stop_inference_recommendations_job * stop_labeling_job * stop_mlflow_tracking_server * stop_monitoring_schedule * stop_notebook_instance * stop_optimization_job * stop_pipeline_execution * stop_processing_job * stop_training_job * stop_transform_job * update_action * update_app_image_config * update_artifact * update_cluster * update_cluster_scheduler_config * update_cluster_software * update_code_repository * update_compute_quota * update_context * update_device_fleet * update_devices * update_domain * update_endpoint * update_endpoint_weights_and_capacities * update_experiment * update_feature_group * update_feature_metadata * update_hub * update_hub_content * update_hub_content_reference * update_image * update_image_version * update_inference_component * update_inference_component_runtime_config * update_inference_experiment * update_mlflow_tracking_server * update_model_card * update_model_package * update_monitoring_alert * update_monitoring_schedule * update_notebook_instance * update_notebook_instance_lifecycle_config * update_partner_app * update_pipeline * update_pipeline_execution * update_pipeline_version * update_project * update_space * update_training_job * update_trial * update_trial_component * update_user_profile * update_workforce * update_workteam Paginators ========== Paginators are available on a client instance via the "get_paginator" method. For more detailed instructions and examples on the usage of paginators, see the paginators user guide. The available paginators are: * CreateHubContentPresignedUrls * ListActions * ListAlgorithms * ListAliases * ListAppImageConfigs * ListApps * ListArtifacts * ListAssociations * ListAutoMLJobs * ListCandidatesForAutoMLJob * ListClusterEvents * ListClusterNodes * ListClusterSchedulerConfigs * ListClusters * ListCodeRepositories * ListCompilationJobs * ListComputeQuotas * ListContexts * ListDataQualityJobDefinitions * ListDeviceFleets * ListDevices * ListDomains * ListEdgeDeploymentPlans * ListEdgePackagingJobs * ListEndpointConfigs * ListEndpoints * ListExperiments * ListFeatureGroups * ListFlowDefinitions * ListHumanTaskUis * ListHyperParameterTuningJobs * ListImageVersions * ListImages * ListInferenceComponents * ListInferenceExperiments * ListInferenceRecommendationsJobSteps * ListInferenceRecommendationsJobs * ListLabelingJobs * ListLabelingJobsForWorkteam * ListLineageGroups * ListMlflowTrackingServers * ListModelBiasJobDefinitions * ListModelCardExportJobs * ListModelCardVersions * ListModelCards * ListModelExplainabilityJobDefinitions * ListModelMetadata * ListModelPackageGroups * ListModelPackages * ListModelQualityJobDefinitions * ListModels * ListMonitoringAlertHistory * ListMonitoringAlerts * ListMonitoringExecutions * ListMonitoringSchedules * ListNotebookInstanceLifecycleConfigs * ListNotebookInstances * ListOptimizationJobs * ListPartnerApps * ListPipelineExecutionSteps * ListPipelineExecutions * ListPipelineParametersForExecution * ListPipelineVersions * ListPipelines * ListProcessingJobs * ListResourceCatalogs * ListSpaces * ListStageDevices * ListStudioLifecycleConfigs * ListSubscribedWorkteams * ListTags * ListTrainingJobs * ListTrainingJobsForHyperParameterTuningJob * ListTrainingPlans * ListTransformJobs * ListTrialComponents * ListTrials * ListUltraServersByReservedCapacity * ListUserProfiles * ListWorkforces * ListWorkteams * Search Waiters ======= Waiters are available on a client instance via the "get_waiter" method. For more detailed instructions and examples on the usage or waiters, see the waiters user guide. The available waiters are: * EndpointDeleted * EndpointInService * ImageCreated * ImageDeleted * ImageUpdated * ImageVersionCreated * ImageVersionDeleted * NotebookInstanceDeleted * NotebookInstanceInService * NotebookInstanceStopped * ProcessingJobCompletedOrStopped * TrainingJobCompletedOrStopped * TransformJobCompletedOrStopped SageMaker / Waiter / ImageUpdated ImageUpdated ************ class SageMaker.Waiter.ImageUpdated waiter = client.get_waiter('image_updated') wait(**kwargs) Polls "SageMaker.Client.describe_image()" every 60 seconds until a successful state is reached. An error is raised after 60 failed checks. See also: AWS API Documentation **Request Syntax** waiter.wait( ImageName='string', WaiterConfig={ 'Delay': 123, 'MaxAttempts': 123 } ) Parameters: * **ImageName** (*string*) -- **[REQUIRED]** The name of the image to describe. * **WaiterConfig** (*dict*) -- A dictionary that provides parameters to control waiting behavior. * **Delay** *(integer) --* The amount of time in seconds to wait between attempts. Default: 60 * **MaxAttempts** *(integer) --* The maximum number of attempts to be made. Default: 60 Returns: None SageMaker / Waiter / ImageVersionCreated ImageVersionCreated ******************* class SageMaker.Waiter.ImageVersionCreated waiter = client.get_waiter('image_version_created') wait(**kwargs) Polls "SageMaker.Client.describe_image_version()" every 60 seconds until a successful state is reached. An error is raised after 60 failed checks. See also: AWS API Documentation **Request Syntax** waiter.wait( ImageName='string', Version=123, Alias='string', WaiterConfig={ 'Delay': 123, 'MaxAttempts': 123 } ) Parameters: * **ImageName** (*string*) -- **[REQUIRED]** The name of the image. * **Version** (*integer*) -- The version of the image. If not specified, the latest version is described. * **Alias** (*string*) -- The alias of the image version. * **WaiterConfig** (*dict*) -- A dictionary that provides parameters to control waiting behavior. * **Delay** *(integer) --* The amount of time in seconds to wait between attempts. Default: 60 * **MaxAttempts** *(integer) --* The maximum number of attempts to be made. Default: 60 Returns: None SageMaker / Waiter / NotebookInstanceStopped NotebookInstanceStopped *********************** class SageMaker.Waiter.NotebookInstanceStopped waiter = client.get_waiter('notebook_instance_stopped') wait(**kwargs) Polls "SageMaker.Client.describe_notebook_instance()" every 30 seconds until a successful state is reached. An error is raised after 60 failed checks. See also: AWS API Documentation **Request Syntax** waiter.wait( NotebookInstanceName='string', WaiterConfig={ 'Delay': 123, 'MaxAttempts': 123 } ) Parameters: * **NotebookInstanceName** (*string*) -- **[REQUIRED]** The name of the notebook instance that you want information about. * **WaiterConfig** (*dict*) -- A dictionary that provides parameters to control waiting behavior. * **Delay** *(integer) --* The amount of time in seconds to wait between attempts. Default: 30 * **MaxAttempts** *(integer) --* The maximum number of attempts to be made. Default: 60 Returns: None SageMaker / Waiter / ProcessingJobCompletedOrStopped ProcessingJobCompletedOrStopped ******************************* class SageMaker.Waiter.ProcessingJobCompletedOrStopped waiter = client.get_waiter('processing_job_completed_or_stopped') wait(**kwargs) Polls "SageMaker.Client.describe_processing_job()" every 60 seconds until a successful state is reached. An error is raised after 60 failed checks. See also: AWS API Documentation **Request Syntax** waiter.wait( ProcessingJobName='string', WaiterConfig={ 'Delay': 123, 'MaxAttempts': 123 } ) Parameters: * **ProcessingJobName** (*string*) -- **[REQUIRED]** The name of the processing job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account. * **WaiterConfig** (*dict*) -- A dictionary that provides parameters to control waiting behavior. * **Delay** *(integer) --* The amount of time in seconds to wait between attempts. Default: 60 * **MaxAttempts** *(integer) --* The maximum number of attempts to be made. Default: 60 Returns: None SageMaker / Waiter / ImageCreated ImageCreated ************ class SageMaker.Waiter.ImageCreated waiter = client.get_waiter('image_created') wait(**kwargs) Polls "SageMaker.Client.describe_image()" every 60 seconds until a successful state is reached. An error is raised after 60 failed checks. See also: AWS API Documentation **Request Syntax** waiter.wait( ImageName='string', WaiterConfig={ 'Delay': 123, 'MaxAttempts': 123 } ) Parameters: * **ImageName** (*string*) -- **[REQUIRED]** The name of the image to describe. * **WaiterConfig** (*dict*) -- A dictionary that provides parameters to control waiting behavior. * **Delay** *(integer) --* The amount of time in seconds to wait between attempts. Default: 60 * **MaxAttempts** *(integer) --* The maximum number of attempts to be made. Default: 60 Returns: None SageMaker / Waiter / NotebookInstanceInService NotebookInstanceInService ************************* class SageMaker.Waiter.NotebookInstanceInService waiter = client.get_waiter('notebook_instance_in_service') wait(**kwargs) Polls "SageMaker.Client.describe_notebook_instance()" every 30 seconds until a successful state is reached. An error is raised after 60 failed checks. See also: AWS API Documentation **Request Syntax** waiter.wait( NotebookInstanceName='string', WaiterConfig={ 'Delay': 123, 'MaxAttempts': 123 } ) Parameters: * **NotebookInstanceName** (*string*) -- **[REQUIRED]** The name of the notebook instance that you want information about. * **WaiterConfig** (*dict*) -- A dictionary that provides parameters to control waiting behavior. * **Delay** *(integer) --* The amount of time in seconds to wait between attempts. Default: 30 * **MaxAttempts** *(integer) --* The maximum number of attempts to be made. Default: 60 Returns: None SageMaker / Waiter / ImageVersionDeleted ImageVersionDeleted ******************* class SageMaker.Waiter.ImageVersionDeleted waiter = client.get_waiter('image_version_deleted') wait(**kwargs) Polls "SageMaker.Client.describe_image_version()" every 60 seconds until a successful state is reached. An error is raised after 60 failed checks. See also: AWS API Documentation **Request Syntax** waiter.wait( ImageName='string', Version=123, Alias='string', WaiterConfig={ 'Delay': 123, 'MaxAttempts': 123 } ) Parameters: * **ImageName** (*string*) -- **[REQUIRED]** The name of the image. * **Version** (*integer*) -- The version of the image. If not specified, the latest version is described. * **Alias** (*string*) -- The alias of the image version. * **WaiterConfig** (*dict*) -- A dictionary that provides parameters to control waiting behavior. * **Delay** *(integer) --* The amount of time in seconds to wait between attempts. Default: 60 * **MaxAttempts** *(integer) --* The maximum number of attempts to be made. Default: 60 Returns: None SageMaker / Waiter / NotebookInstanceDeleted NotebookInstanceDeleted *********************** class SageMaker.Waiter.NotebookInstanceDeleted waiter = client.get_waiter('notebook_instance_deleted') wait(**kwargs) Polls "SageMaker.Client.describe_notebook_instance()" every 30 seconds until a successful state is reached. An error is raised after 60 failed checks. See also: AWS API Documentation **Request Syntax** waiter.wait( NotebookInstanceName='string', WaiterConfig={ 'Delay': 123, 'MaxAttempts': 123 } ) Parameters: * **NotebookInstanceName** (*string*) -- **[REQUIRED]** The name of the notebook instance that you want information about. * **WaiterConfig** (*dict*) -- A dictionary that provides parameters to control waiting behavior. * **Delay** *(integer) --* The amount of time in seconds to wait between attempts. Default: 30 * **MaxAttempts** *(integer) --* The maximum number of attempts to be made. Default: 60 Returns: None SageMaker / Waiter / TrainingJobCompletedOrStopped TrainingJobCompletedOrStopped ***************************** class SageMaker.Waiter.TrainingJobCompletedOrStopped waiter = client.get_waiter('training_job_completed_or_stopped') wait(**kwargs) Polls "SageMaker.Client.describe_training_job()" every 120 seconds until a successful state is reached. An error is raised after 180 failed checks. See also: AWS API Documentation **Request Syntax** waiter.wait( TrainingJobName='string', WaiterConfig={ 'Delay': 123, 'MaxAttempts': 123 } ) Parameters: * **TrainingJobName** (*string*) -- **[REQUIRED]** The name of the training job. * **WaiterConfig** (*dict*) -- A dictionary that provides parameters to control waiting behavior. * **Delay** *(integer) --* The amount of time in seconds to wait between attempts. Default: 120 * **MaxAttempts** *(integer) --* The maximum number of attempts to be made. Default: 180 Returns: None SageMaker / Waiter / EndpointDeleted EndpointDeleted *************** class SageMaker.Waiter.EndpointDeleted waiter = client.get_waiter('endpoint_deleted') wait(**kwargs) Polls "SageMaker.Client.describe_endpoint()" every 30 seconds until a successful state is reached. An error is raised after 60 failed checks. See also: AWS API Documentation **Request Syntax** waiter.wait( EndpointName='string', WaiterConfig={ 'Delay': 123, 'MaxAttempts': 123 } ) Parameters: * **EndpointName** (*string*) -- **[REQUIRED]** The name of the endpoint. * **WaiterConfig** (*dict*) -- A dictionary that provides parameters to control waiting behavior. * **Delay** *(integer) --* The amount of time in seconds to wait between attempts. Default: 30 * **MaxAttempts** *(integer) --* The maximum number of attempts to be made. Default: 60 Returns: None SageMaker / Waiter / EndpointInService EndpointInService ***************** class SageMaker.Waiter.EndpointInService waiter = client.get_waiter('endpoint_in_service') wait(**kwargs) Polls "SageMaker.Client.describe_endpoint()" every 30 seconds until a successful state is reached. An error is raised after 120 failed checks. See also: AWS API Documentation **Request Syntax** waiter.wait( EndpointName='string', WaiterConfig={ 'Delay': 123, 'MaxAttempts': 123 } ) Parameters: * **EndpointName** (*string*) -- **[REQUIRED]** The name of the endpoint. * **WaiterConfig** (*dict*) -- A dictionary that provides parameters to control waiting behavior. * **Delay** *(integer) --* The amount of time in seconds to wait between attempts. Default: 30 * **MaxAttempts** *(integer) --* The maximum number of attempts to be made. Default: 120 Returns: None SageMaker / Waiter / ImageDeleted ImageDeleted ************ class SageMaker.Waiter.ImageDeleted waiter = client.get_waiter('image_deleted') wait(**kwargs) Polls "SageMaker.Client.describe_image()" every 60 seconds until a successful state is reached. An error is raised after 60 failed checks. See also: AWS API Documentation **Request Syntax** waiter.wait( ImageName='string', WaiterConfig={ 'Delay': 123, 'MaxAttempts': 123 } ) Parameters: * **ImageName** (*string*) -- **[REQUIRED]** The name of the image to describe. * **WaiterConfig** (*dict*) -- A dictionary that provides parameters to control waiting behavior. * **Delay** *(integer) --* The amount of time in seconds to wait between attempts. Default: 60 * **MaxAttempts** *(integer) --* The maximum number of attempts to be made. Default: 60 Returns: None SageMaker / Waiter / TransformJobCompletedOrStopped TransformJobCompletedOrStopped ****************************** class SageMaker.Waiter.TransformJobCompletedOrStopped waiter = client.get_waiter('transform_job_completed_or_stopped') wait(**kwargs) Polls "SageMaker.Client.describe_transform_job()" every 60 seconds until a successful state is reached. An error is raised after 60 failed checks. See also: AWS API Documentation **Request Syntax** waiter.wait( TransformJobName='string', WaiterConfig={ 'Delay': 123, 'MaxAttempts': 123 } ) Parameters: * **TransformJobName** (*string*) -- **[REQUIRED]** The name of the transform job that you want to view details of. * **WaiterConfig** (*dict*) -- A dictionary that provides parameters to control waiting behavior. * **Delay** *(integer) --* The amount of time in seconds to wait between attempts. Default: 60 * **MaxAttempts** *(integer) --* The maximum number of attempts to be made. Default: 60 Returns: None SageMaker / Paginator / ListEdgePackagingJobs ListEdgePackagingJobs ********************* class SageMaker.Paginator.ListEdgePackagingJobs paginator = client.get_paginator('list_edge_packaging_jobs') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_edge_packaging_jobs()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( CreationTimeAfter=datetime(2015, 1, 1), CreationTimeBefore=datetime(2015, 1, 1), LastModifiedTimeAfter=datetime(2015, 1, 1), LastModifiedTimeBefore=datetime(2015, 1, 1), NameContains='string', ModelNameContains='string', StatusEquals='STARTING'|'INPROGRESS'|'COMPLETED'|'FAILED'|'STOPPING'|'STOPPED', SortBy='NAME'|'MODEL_NAME'|'CREATION_TIME'|'LAST_MODIFIED_TIME'|'STATUS', SortOrder='Ascending'|'Descending', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **CreationTimeAfter** (*datetime*) -- Select jobs where the job was created after specified time. * **CreationTimeBefore** (*datetime*) -- Select jobs where the job was created before specified time. * **LastModifiedTimeAfter** (*datetime*) -- Select jobs where the job was updated after specified time. * **LastModifiedTimeBefore** (*datetime*) -- Select jobs where the job was updated before specified time. * **NameContains** (*string*) -- Filter for jobs containing this name in their packaging job name. * **ModelNameContains** (*string*) -- Filter for jobs where the model name contains this string. * **StatusEquals** (*string*) -- The job status to filter for. * **SortBy** (*string*) -- Use to specify what column to sort by. * **SortOrder** (*string*) -- What direction to sort by. * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'EdgePackagingJobSummaries': [ { 'EdgePackagingJobArn': 'string', 'EdgePackagingJobName': 'string', 'EdgePackagingJobStatus': 'STARTING'|'INPROGRESS'|'COMPLETED'|'FAILED'|'STOPPING'|'STOPPED', 'CompilationJobName': 'string', 'ModelName': 'string', 'ModelVersion': 'string', 'CreationTime': datetime(2015, 1, 1), 'LastModifiedTime': datetime(2015, 1, 1) }, ], } **Response Structure** * *(dict) --* * **EdgePackagingJobSummaries** *(list) --* Summaries of edge packaging jobs. * *(dict) --* Summary of edge packaging job. * **EdgePackagingJobArn** *(string) --* The Amazon Resource Name (ARN) of the edge packaging job. * **EdgePackagingJobName** *(string) --* The name of the edge packaging job. * **EdgePackagingJobStatus** *(string) --* The status of the edge packaging job. * **CompilationJobName** *(string) --* The name of the SageMaker Neo compilation job. * **ModelName** *(string) --* The name of the model. * **ModelVersion** *(string) --* The version of the model. * **CreationTime** *(datetime) --* The timestamp of when the job was created. * **LastModifiedTime** *(datetime) --* The timestamp of when the edge packaging job was last updated. SageMaker / Paginator / ListSpaces ListSpaces ********** class SageMaker.Paginator.ListSpaces paginator = client.get_paginator('list_spaces') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_spaces()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( SortOrder='Ascending'|'Descending', SortBy='CreationTime'|'LastModifiedTime', DomainIdEquals='string', SpaceNameContains='string', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **SortOrder** (*string*) -- The sort order for the results. The default is "Ascending". * **SortBy** (*string*) -- The parameter by which to sort the results. The default is "CreationTime". * **DomainIdEquals** (*string*) -- A parameter to search for the domain ID. * **SpaceNameContains** (*string*) -- A parameter by which to filter the results. * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'Spaces': [ { 'DomainId': 'string', 'SpaceName': 'string', 'Status': 'Deleting'|'Failed'|'InService'|'Pending'|'Updating'|'Update_Failed'|'Delete_Failed', 'CreationTime': datetime(2015, 1, 1), 'LastModifiedTime': datetime(2015, 1, 1), 'SpaceSettingsSummary': { 'AppType': 'JupyterServer'|'KernelGateway'|'DetailedProfiler'|'TensorBoard'|'CodeEditor'|'JupyterLab'|'RStudioServerPro'|'RSessionGateway'|'Canvas', 'RemoteAccess': 'ENABLED'|'DISABLED', 'SpaceStorageSettings': { 'EbsStorageSettings': { 'EbsVolumeSizeInGb': 123 } } }, 'SpaceSharingSettingsSummary': { 'SharingType': 'Private'|'Shared' }, 'OwnershipSettingsSummary': { 'OwnerUserProfileName': 'string' }, 'SpaceDisplayName': 'string' }, ], } **Response Structure** * *(dict) --* * **Spaces** *(list) --* The list of spaces. * *(dict) --* The space's details. * **DomainId** *(string) --* The ID of the associated domain. * **SpaceName** *(string) --* The name of the space. * **Status** *(string) --* The status. * **CreationTime** *(datetime) --* The creation time. * **LastModifiedTime** *(datetime) --* The last modified time. * **SpaceSettingsSummary** *(dict) --* Specifies summary information about the space settings. * **AppType** *(string) --* The type of app created within the space. * **RemoteAccess** *(string) --* A setting that enables or disables remote access for a SageMaker space. When enabled, this allows you to connect to the remote space from your local IDE. * **SpaceStorageSettings** *(dict) --* The storage settings for a space. * **EbsStorageSettings** *(dict) --* A collection of EBS storage settings for a space. * **EbsVolumeSizeInGb** *(integer) --* The size of an EBS storage volume for a space. * **SpaceSharingSettingsSummary** *(dict) --* Specifies summary information about the space sharing settings. * **SharingType** *(string) --* Specifies the sharing type of the space. * **OwnershipSettingsSummary** *(dict) --* Specifies summary information about the ownership settings. * **OwnerUserProfileName** *(string) --* The user profile who is the owner of the space. * **SpaceDisplayName** *(string) --* The name of the space that appears in the Studio UI. SageMaker / Paginator / ListResourceCatalogs ListResourceCatalogs ******************** class SageMaker.Paginator.ListResourceCatalogs paginator = client.get_paginator('list_resource_catalogs') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_resource_catalogs()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( NameContains='string', CreationTimeAfter=datetime(2015, 1, 1), CreationTimeBefore=datetime(2015, 1, 1), SortOrder='Ascending'|'Descending', SortBy='CreationTime', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **NameContains** (*string*) -- A string that partially matches one or more "ResourceCatalog``s names. Filters ``ResourceCatalog" by name. * **CreationTimeAfter** (*datetime*) -- Use this parameter to search for >>``<>``<>``<"", where "" is a metric name. For example, the following filter searches for training jobs with an ""accuracy"" metric greater than ""0.9"": "{" ""Name": "Metrics.accuracy"," ""Operator": "GreaterThan"," ""Value": "0.9"" "}" HyperParameters To define a hyperparameter filter, enter a value with the form ""HyperParameters."". Decimal hyperparameter values are treated as a decimal in a comparison if the specified "Value" is also a decimal value. If the specified "Value" is an integer, the decimal hyperparameter values are treated as integers. For example, the following filter is satisfied by training jobs with a ""learning_rate"" hyperparameter that is less than ""0.5"": "{" ""Name": "HyperParameters.learning_rate"," ""Operator": "LessThan"," ""Value": "0.5"" "}" Tags To define a tag filter, enter a value with the form "Tags.". * **Name** *(string) --* **[REQUIRED]** A resource property name. For example, "TrainingJobName". For valid property names, see SearchRecord. You must specify a valid property for the resource. * **Operator** *(string) --* A Boolean binary operator that is used to evaluate the filter. The operator field contains one of the following values: Equals The value of "Name" equals "Value". NotEquals The value of "Name" doesn't equal "Value". Exists The "Name" property exists. NotExists The "Name" property does not exist. GreaterThan The value of "Name" is greater than "Value". Not supported for text properties. GreaterThanOrEqualTo The value of "Name" is greater than or equal to "Value". Not supported for text properties. LessThan The value of "Name" is less than "Value". Not supported for text properties. LessThanOrEqualTo The value of "Name" is less than or equal to "Value". Not supported for text properties. In The value of "Name" is one of the comma delimited strings in "Value". Only supported for text properties. Contains The value of "Name" contains the string "Value". Only supported for text properties. A "SearchExpression" can include the "Contains" operator multiple times when the value of "Name" is one of the following: * "Experiment.DisplayName" * "Experiment.ExperimentName" * "Experiment.Tags" * "Trial.DisplayName" * "Trial.TrialName" * "Trial.Tags" * "TrialComponent.DisplayName" * "TrialComponent.TrialComponentName" * "TrialComponent.Tags" * "TrialComponent.InputArtifacts" * "TrialComponent.OutputArtifacts" A "SearchExpression" can include only one "Contains" operator for all other values of "Name". In these cases, if you include multiple "Contains" operators in the "SearchExpression", the result is the following error message: " "'CONTAINS' operator usage limit of 1 exceeded."" * **Value** *(string) --* A value used with "Name" and "Operator" to determine which resources satisfy the filter's condition. For numerical properties, "Value" must be an integer or floating-point decimal. For timestamp properties, "Value" must be an ISO 8601 date-time string of the following format: "YYYY-mm-dd'T'HH:MM:SS". * **NestedFilters** *(list) --* A list of nested filter objects. * *(dict) --* A list of nested Filter objects. A resource must satisfy the conditions of all filters to be included in the results returned from the Search API. For example, to filter on a training job's "InputDataConfig" property with a specific channel name and "S3Uri" prefix, define the following filters: * "'{Name:"InputDataConfig.ChannelName", "Operator":"Equals", "Value":"train"}'," * "'{Name:"InputDataConfig.DataSource.S3DataSource.S3U ri", "Operator":"Contains", "Value":"mybucket/catdata"}'" * **NestedPropertyName** *(string) --* **[REQUIRED]** The name of the property to use in the nested filters. The value must match a listed property name, such as "InputDataConfig". * **Filters** *(list) --* **[REQUIRED]** A list of filters. Each filter acts on a property. Filters must contain at least one "Filters" value. For example, a "NestedFilters" call might include a filter on the "PropertyName" parameter of the "InputDataConfig" property: "InputDataConfig.DataSource.S3DataSource.S3Uri". * *(dict) --* A conditional statement for a search expression that includes a resource property, a Boolean operator, and a value. Resources that match the statement are returned in the results from the Search API. If you specify a "Value", but not an "Operator", SageMaker uses the equals operator. In search, there are several property types: Metrics To define a metric filter, enter a value using the form ""Metrics."", where "" is a metric name. For example, the following filter searches for training jobs with an ""accuracy"" metric greater than ""0.9"": "{" ""Name": "Metrics.accuracy"," ""Operator": "GreaterThan"," ""Value": "0.9"" "}" HyperParameters To define a hyperparameter filter, enter a value with the form ""HyperParameters."". Decimal hyperparameter values are treated as a decimal in a comparison if the specified "Value" is also a decimal value. If the specified "Value" is an integer, the decimal hyperparameter values are treated as integers. For example, the following filter is satisfied by training jobs with a ""learning_rate"" hyperparameter that is less than ""0.5"": "{" ""Name": "HyperParameters.learning_rate"," ""Operator": "LessThan"," ""Value": "0.5"" "}" Tags To define a tag filter, enter a value with the form "Tags.". * **Name** *(string) --* **[REQUIRED]** A resource property name. For example, "TrainingJobName". For valid property names, see SearchRecord. You must specify a valid property for the resource. * **Operator** *(string) --* A Boolean binary operator that is used to evaluate the filter. The operator field contains one of the following values: Equals The value of "Name" equals "Value". NotEquals The value of "Name" doesn't equal "Value". Exists The "Name" property exists. NotExists The "Name" property does not exist. GreaterThan The value of "Name" is greater than "Value". Not supported for text properties. GreaterThanOrEqualTo The value of "Name" is greater than or equal to "Value". Not supported for text properties. LessThan The value of "Name" is less than "Value". Not supported for text properties. LessThanOrEqualTo The value of "Name" is less than or equal to "Value". Not supported for text properties. In The value of "Name" is one of the comma delimited strings in "Value". Only supported for text properties. Contains The value of "Name" contains the string "Value". Only supported for text properties. A "SearchExpression" can include the "Contains" operator multiple times when the value of "Name" is one of the following: * "Experiment.DisplayName" * "Experiment.ExperimentName" * "Experiment.Tags" * "Trial.DisplayName" * "Trial.TrialName" * "Trial.Tags" * "TrialComponent.DisplayName" * "TrialComponent.TrialComponentName" * "TrialComponent.Tags" * "TrialComponent.InputArtifacts" * "TrialComponent.OutputArtifacts" A "SearchExpression" can include only one "Contains" operator for all other values of "Name". In these cases, if you include multiple "Contains" operators in the "SearchExpression", the result is the following error message: " "'CONTAINS' operator usage limit of 1 exceeded."" * **Value** *(string) --* A value used with "Name" and "Operator" to determine which resources satisfy the filter's condition. For numerical properties, "Value" must be an integer or floating-point decimal. For timestamp properties, "Value" must be an ISO 8601 date-time string of the following format: "YYYY- mm-dd'T'HH:MM:SS". * **SubExpressions** *(list) --* A list of search expression objects. * *(dict) --* A multi-expression that searches for the specified resource or resources in a search. All resource objects that satisfy the expression's condition are included in the search results. You must specify at least one subexpression, filter, or nested filter. A "SearchExpression" can contain up to twenty elements. A "SearchExpression" contains the following components: * A list of "Filter" objects. Each filter defines a simple Boolean expression comprised of a resource property name, Boolean operator, and value. * A list of "NestedFilter" objects. Each nested filter defines a list of Boolean expressions using a list of resource properties. A nested filter is satisfied if a single object in the list satisfies all Boolean expressions. * A list of "SearchExpression" objects. A search expression object can be nested in a list of search expression objects. * A Boolean operator: "And" or "Or". * **Operator** *(string) --* A Boolean operator used to evaluate the search expression. If you want every conditional statement in all lists to be satisfied for the entire search expression to be true, specify "And". If only a single conditional statement needs to be true for the entire search expression to be true, specify "Or". The default value is "And". * **SortBy** (*string*) -- The name of the resource property used to sort the "SearchResults". The default is "LastModifiedTime". * **SortOrder** (*string*) -- How "SearchResults" are ordered. Valid values are "Ascending" or "Descending". The default is "Descending". * **CrossAccountFilterOption** (*string*) -- A cross account filter option. When the value is ""CrossAccount"" the search results will only include resources made discoverable to you from other accounts. When the value is ""SameAccount"" or "null" the search results will only include resources from your account. Default is "null". For more information on searching for resources made discoverable to your account, see Search discoverable resources in the SageMaker Developer Guide. The maximum number of >>``<". * **Value** *(string) --* The value for the tag that you're using to filter the search results. * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** # This section is too large to render. # Please see the AWS API Documentation linked below. AWS API Documentation **Response Structure** # This section is too large to render. # Please see the AWS API Documentation linked below. AWS API Documentation SageMaker / Paginator / ListOptimizationJobs ListOptimizationJobs ******************** class SageMaker.Paginator.ListOptimizationJobs paginator = client.get_paginator('list_optimization_jobs') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_optimization_jobs()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( CreationTimeAfter=datetime(2015, 1, 1), CreationTimeBefore=datetime(2015, 1, 1), LastModifiedTimeAfter=datetime(2015, 1, 1), LastModifiedTimeBefore=datetime(2015, 1, 1), OptimizationContains='string', NameContains='string', StatusEquals='INPROGRESS'|'COMPLETED'|'FAILED'|'STARTING'|'STOPPING'|'STOPPED', SortBy='Name'|'CreationTime'|'Status', SortOrder='Ascending'|'Descending', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **CreationTimeAfter** (*datetime*) -- Filters the results to only those optimization jobs that were created after the specified time. * **CreationTimeBefore** (*datetime*) -- Filters the results to only those optimization jobs that were created before the specified time. * **LastModifiedTimeAfter** (*datetime*) -- Filters the results to only those optimization jobs that were updated after the specified time. * **LastModifiedTimeBefore** (*datetime*) -- Filters the results to only those optimization jobs that were updated before the specified time. * **OptimizationContains** (*string*) -- Filters the results to only those optimization jobs that apply the specified optimization techniques. You can specify either "Quantization" or "Compilation". * **NameContains** (*string*) -- Filters the results to only those optimization jobs with a name that contains the specified string. * **StatusEquals** (*string*) -- Filters the results to only those optimization jobs with the specified status. * **SortBy** (*string*) -- The field by which to sort the optimization jobs in the response. The default is "CreationTime" * **SortOrder** (*string*) -- The sort order for results. The default is "Ascending" * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'OptimizationJobSummaries': [ { 'OptimizationJobName': 'string', 'OptimizationJobArn': 'string', 'CreationTime': datetime(2015, 1, 1), 'OptimizationJobStatus': 'INPROGRESS'|'COMPLETED'|'FAILED'|'STARTING'|'STOPPING'|'STOPPED', 'OptimizationStartTime': datetime(2015, 1, 1), 'OptimizationEndTime': datetime(2015, 1, 1), 'LastModifiedTime': datetime(2015, 1, 1), 'DeploymentInstanceType': 'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge', 'OptimizationTypes': [ 'string', ] }, ], } **Response Structure** * *(dict) --* * **OptimizationJobSummaries** *(list) --* A list of optimization jobs and their properties that matches any of the filters you specified in the request. * *(dict) --* Summarizes an optimization job by providing some of its key properties. * **OptimizationJobName** *(string) --* The name that you assigned to the optimization job. * **OptimizationJobArn** *(string) --* The Amazon Resource Name (ARN) of the optimization job. * **CreationTime** *(datetime) --* The time when you created the optimization job. * **OptimizationJobStatus** *(string) --* The current status of the optimization job. * **OptimizationStartTime** *(datetime) --* The time when the optimization job started. * **OptimizationEndTime** *(datetime) --* The time when the optimization job finished processing. * **LastModifiedTime** *(datetime) --* The time when the optimization job was last updated. * **DeploymentInstanceType** *(string) --* The type of instance that hosts the optimized model that you create with the optimization job. * **OptimizationTypes** *(list) --* The optimization techniques that are applied by the optimization job. * *(string) --* SageMaker / Paginator / ListApps ListApps ******** class SageMaker.Paginator.ListApps paginator = client.get_paginator('list_apps') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_apps()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( SortOrder='Ascending'|'Descending', SortBy='CreationTime', DomainIdEquals='string', UserProfileNameEquals='string', SpaceNameEquals='string', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **SortOrder** (*string*) -- The sort order for the results. The default is Ascending. * **SortBy** (*string*) -- The parameter by which to sort the results. The default is CreationTime. * **DomainIdEquals** (*string*) -- A parameter to search for the domain ID. * **UserProfileNameEquals** (*string*) -- A parameter to search by user profile name. If "SpaceNameEquals" is set, then this value cannot be set. * **SpaceNameEquals** (*string*) -- A parameter to search by space name. If "UserProfileNameEquals" is set, then this value cannot be set. * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'Apps': [ { 'DomainId': 'string', 'UserProfileName': 'string', 'SpaceName': 'string', 'AppType': 'JupyterServer'|'KernelGateway'|'DetailedProfiler'|'TensorBoard'|'CodeEditor'|'JupyterLab'|'RStudioServerPro'|'RSessionGateway'|'Canvas', 'AppName': 'string', 'Status': 'Deleted'|'Deleting'|'Failed'|'InService'|'Pending', 'CreationTime': datetime(2015, 1, 1), 'ResourceSpec': { 'SageMakerImageArn': 'string', 'SageMakerImageVersionArn': 'string', 'SageMakerImageVersionAlias': 'string', 'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge', 'LifecycleConfigArn': 'string' } }, ], } **Response Structure** * *(dict) --* * **Apps** *(list) --* The list of apps. * *(dict) --* Details about an Amazon SageMaker AI app. * **DomainId** *(string) --* The domain ID. * **UserProfileName** *(string) --* The user profile name. * **SpaceName** *(string) --* The name of the space. * **AppType** *(string) --* The type of app. * **AppName** *(string) --* The name of the app. * **Status** *(string) --* The status. * **CreationTime** *(datetime) --* The creation time. * **ResourceSpec** *(dict) --* Specifies the ARN's of a SageMaker AI image and SageMaker AI image version, and the instance type that the version runs on. Note: When both "SageMakerImageVersionArn" and "SageMakerImageArn" are passed, "SageMakerImageVersionArn" is used. Any updates to "SageMakerImageArn" will not take effect if "SageMakerImageVersionArn" already exists in the "ResourceSpec" because "SageMakerImageVersionArn" always takes precedence. To clear the value set for "SageMakerImageVersionArn", pass "None" as the value. * **SageMakerImageArn** *(string) --* The ARN of the SageMaker AI image that the image version belongs to. * **SageMakerImageVersionArn** *(string) --* The ARN of the image version created on the instance. To clear the value set for "SageMakerImageVersionArn", pass "None" as the value. * **SageMakerImageVersionAlias** *(string) --* The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format. * **InstanceType** *(string) --* The instance type that the image version runs on. Note: **JupyterServer apps** only support the "system" value.For **KernelGateway apps**, the "system" value is translated to "ml.t3.medium". KernelGateway apps also support all other values for available instance types. * **LifecycleConfigArn** *(string) --* The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource. SageMaker / Paginator / ListAssociations ListAssociations **************** class SageMaker.Paginator.ListAssociations paginator = client.get_paginator('list_associations') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_associations()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( SourceArn='string', DestinationArn='string', SourceType='string', DestinationType='string', AssociationType='ContributedTo'|'AssociatedWith'|'DerivedFrom'|'Produced'|'SameAs', CreatedAfter=datetime(2015, 1, 1), CreatedBefore=datetime(2015, 1, 1), SortBy='SourceArn'|'DestinationArn'|'SourceType'|'DestinationType'|'CreationTime', SortOrder='Ascending'|'Descending', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **SourceArn** (*string*) -- A filter that returns only associations with the specified source ARN. * **DestinationArn** (*string*) -- A filter that returns only associations with the specified destination Amazon Resource Name (ARN). * **SourceType** (*string*) -- A filter that returns only associations with the specified source type. * **DestinationType** (*string*) -- A filter that returns only associations with the specified destination type. * **AssociationType** (*string*) -- A filter that returns only associations of the specified type. * **CreatedAfter** (*datetime*) -- A filter that returns only associations created on or after the specified time. * **CreatedBefore** (*datetime*) -- A filter that returns only associations created on or before the specified time. * **SortBy** (*string*) -- The property used to sort results. The default value is "CreationTime". * **SortOrder** (*string*) -- The sort order. The default value is "Descending". * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'AssociationSummaries': [ { 'SourceArn': 'string', 'DestinationArn': 'string', 'SourceType': 'string', 'DestinationType': 'string', 'AssociationType': 'ContributedTo'|'AssociatedWith'|'DerivedFrom'|'Produced'|'SameAs', 'SourceName': 'string', 'DestinationName': 'string', 'CreationTime': datetime(2015, 1, 1), 'CreatedBy': { 'UserProfileArn': 'string', 'UserProfileName': 'string', 'DomainId': 'string', 'IamIdentity': { 'Arn': 'string', 'PrincipalId': 'string', 'SourceIdentity': 'string' } } }, ], } **Response Structure** * *(dict) --* * **AssociationSummaries** *(list) --* A list of associations and their properties. * *(dict) --* Lists a summary of the properties of an association. An association is an entity that links other lineage or experiment entities. An example would be an association between a training job and a model. * **SourceArn** *(string) --* The ARN of the source. * **DestinationArn** *(string) --* The Amazon Resource Name (ARN) of the destination. * **SourceType** *(string) --* The source type. * **DestinationType** *(string) --* The destination type. * **AssociationType** *(string) --* The type of the association. * **SourceName** *(string) --* The name of the source. * **DestinationName** *(string) --* The name of the destination. * **CreationTime** *(datetime) --* When the association was created. * **CreatedBy** *(dict) --* Information about the user who created or modified a SageMaker resource. * **UserProfileArn** *(string) --* The Amazon Resource Name (ARN) of the user's profile. * **UserProfileName** *(string) --* The name of the user's profile. * **DomainId** *(string) --* The domain associated with the user. * **IamIdentity** *(dict) --* The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only. * **Arn** *(string) --* The Amazon Resource Name (ARN) of the IAM identity. * **PrincipalId** *(string) --* The ID of the principal that assumes the IAM identity. * **SourceIdentity** *(string) --* The person or application which assumes the IAM identity. SageMaker / Paginator / ListCandidatesForAutoMLJob ListCandidatesForAutoMLJob ************************** class SageMaker.Paginator.ListCandidatesForAutoMLJob paginator = client.get_paginator('list_candidates_for_auto_ml_job') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_candidates_for_auto_ml_job()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( AutoMLJobName='string', StatusEquals='Completed'|'InProgress'|'Failed'|'Stopped'|'Stopping', CandidateNameEquals='string', SortOrder='Ascending'|'Descending', SortBy='CreationTime'|'Status'|'FinalObjectiveMetricValue', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **AutoMLJobName** (*string*) -- **[REQUIRED]** List the candidates created for the job by providing the job's name. * **StatusEquals** (*string*) -- List the candidates for the job and filter by status. * **CandidateNameEquals** (*string*) -- List the candidates for the job and filter by candidate name. * **SortOrder** (*string*) -- The sort order for the results. The default is "Ascending". * **SortBy** (*string*) -- The parameter by which to sort the results. The default is "Descending". * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'Candidates': [ { 'CandidateName': 'string', 'FinalAutoMLJobObjectiveMetric': { 'Type': 'Maximize'|'Minimize', 'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'BalancedAccuracy'|'R2'|'Recall'|'RecallMacro'|'Precision'|'PrecisionMacro'|'MAE'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss', 'Value': ..., 'StandardMetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'BalancedAccuracy'|'R2'|'Recall'|'RecallMacro'|'Precision'|'PrecisionMacro'|'MAE'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss' }, 'ObjectiveStatus': 'Succeeded'|'Pending'|'Failed', 'CandidateSteps': [ { 'CandidateStepType': 'AWS::SageMaker::TrainingJob'|'AWS::SageMaker::TransformJob'|'AWS::SageMaker::ProcessingJob', 'CandidateStepArn': 'string', 'CandidateStepName': 'string' }, ], 'CandidateStatus': 'Completed'|'InProgress'|'Failed'|'Stopped'|'Stopping', 'InferenceContainers': [ { 'Image': 'string', 'ModelDataUrl': 'string', 'Environment': { 'string': 'string' } }, ], 'CreationTime': datetime(2015, 1, 1), 'EndTime': datetime(2015, 1, 1), 'LastModifiedTime': datetime(2015, 1, 1), 'FailureReason': 'string', 'CandidateProperties': { 'CandidateArtifactLocations': { 'Explainability': 'string', 'ModelInsights': 'string', 'BacktestResults': 'string' }, 'CandidateMetrics': [ { 'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'BalancedAccuracy'|'R2'|'Recall'|'RecallMacro'|'Precision'|'PrecisionMacro'|'MAE'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss', 'StandardMetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'MAE'|'R2'|'BalancedAccuracy'|'Precision'|'PrecisionMacro'|'Recall'|'RecallMacro'|'LogLoss'|'InferenceLatency'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss'|'Rouge1'|'Rouge2'|'RougeL'|'RougeLSum'|'Perplexity'|'ValidationLoss'|'TrainingLoss', 'Value': ..., 'Set': 'Train'|'Validation'|'Test' }, ] }, 'InferenceContainerDefinitions': { 'string': [ { 'Image': 'string', 'ModelDataUrl': 'string', 'Environment': { 'string': 'string' } }, ] } }, ], } **Response Structure** * *(dict) --* * **Candidates** *(list) --* Summaries about the "AutoMLCandidates". * *(dict) --* Information about a candidate produced by an AutoML training job, including its status, steps, and other properties. * **CandidateName** *(string) --* The name of the candidate. * **FinalAutoMLJobObjectiveMetric** *(dict) --* The best candidate result from an AutoML training job. * **Type** *(string) --* The type of metric with the best result. * **MetricName** *(string) --* The name of the metric with the best result. For a description of the possible objective metrics, see AutoMLJobObjective$MetricName. * **Value** *(float) --* The value of the metric with the best result. * **StandardMetricName** *(string) --* The name of the standard metric. For a description of the standard metrics, see Autopilot candidate metrics. * **ObjectiveStatus** *(string) --* The objective's status. * **CandidateSteps** *(list) --* Information about the candidate's steps. * *(dict) --* Information about the steps for a candidate and what step it is working on. * **CandidateStepType** *(string) --* Whether the candidate is at the transform, training, or processing step. * **CandidateStepArn** *(string) --* The ARN for the candidate's step. * **CandidateStepName** *(string) --* The name for the candidate's step. * **CandidateStatus** *(string) --* The candidate's status. * **InferenceContainers** *(list) --* Information about the recommended inference container definitions. * *(dict) --* A list of container definitions that describe the different containers that make up an AutoML candidate. For more information, see ContainerDefinition. * **Image** *(string) --* The Amazon Elastic Container Registry (Amazon ECR) path of the container. For more information, see ContainerDefinition. * **ModelDataUrl** *(string) --* The location of the model artifacts. For more information, see ContainerDefinition. * **Environment** *(dict) --* The environment variables to set in the container. For more information, see ContainerDefinition. * *(string) --* * *(string) --* * **CreationTime** *(datetime) --* The creation time. * **EndTime** *(datetime) --* The end time. * **LastModifiedTime** *(datetime) --* The last modified time. * **FailureReason** *(string) --* The failure reason. * **CandidateProperties** *(dict) --* The properties of an AutoML candidate job. * **CandidateArtifactLocations** *(dict) --* The Amazon S3 prefix to the artifacts generated for an AutoML candidate. * **Explainability** *(string) --* The Amazon S3 prefix to the explainability artifacts generated for the AutoML candidate. * **ModelInsights** *(string) --* The Amazon S3 prefix to the model insight artifacts generated for the AutoML candidate. * **BacktestResults** *(string) --* The Amazon S3 prefix to the accuracy metrics and the inference results observed over the testing window. Available only for the time-series forecasting problem type. * **CandidateMetrics** *(list) --* Information about the candidate metrics for an AutoML job. * *(dict) --* Information about the metric for a candidate produced by an AutoML job. * **MetricName** *(string) --* The name of the metric. * **StandardMetricName** *(string) --* The name of the standard metric. Note: For definitions of the standard metrics, see Autopilot candidate metrics. * **Value** *(float) --* The value of the metric. * **Set** *(string) --* The dataset split from which the AutoML job produced the metric. * **InferenceContainerDefinitions** *(dict) --* The mapping of all supported processing unit (CPU, GPU, etc...) to inference container definitions for the candidate. This field is populated for the AutoML jobs V2 (for example, for jobs created by calling "CreateAutoMLJobV2") related to image or text classification problem types only. * *(string) --* Processing unit for an inference container. Currently Autopilot only supports "CPU" or "GPU". * *(list) --* Information about the recommended inference container definitions. * *(dict) --* A list of container definitions that describe the different containers that make up an AutoML candidate. For more information, see ContainerDefinition. * **Image** *(string) --* The Amazon Elastic Container Registry (Amazon ECR) path of the container. For more information, see ContainerDefinition. * **ModelDataUrl** *(string) --* The location of the model artifacts. For more information, see ContainerDefinition. * **Environment** *(dict) --* The environment variables to set in the container. For more information, see ContainerDefinition. * *(string) --* * *(string) --* SageMaker / Paginator / ListTrainingJobsForHyperParameterTuningJob ListTrainingJobsForHyperParameterTuningJob ****************************************** class SageMaker.Paginator.ListTrainingJobsForHyperParameterTuningJob paginator = client.get_paginator('list_training_jobs_for_hyper_parameter_tuning_job') paginate(**kwargs) Creates an iterator that will paginate through responses from " SageMaker.Client.list_training_jobs_for_hyper_parameter_tuning_ job()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( HyperParameterTuningJobName='string', StatusEquals='InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped', SortBy='Name'|'CreationTime'|'Status'|'FinalObjectiveMetricValue', SortOrder='Ascending'|'Descending', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **HyperParameterTuningJobName** (*string*) -- **[REQUIRED]** The name of the tuning job whose training jobs you want to list. * **StatusEquals** (*string*) -- A filter that returns only training jobs with the specified status. * **SortBy** (*string*) -- The field to sort results by. The default is "Name". If the value of this field is "FinalObjectiveMetricValue", any training jobs that did not return an objective metric are not listed. * **SortOrder** (*string*) -- The sort order for results. The default is "Ascending". * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'TrainingJobSummaries': [ { 'TrainingJobDefinitionName': 'string', 'TrainingJobName': 'string', 'TrainingJobArn': 'string', 'TuningJobName': 'string', 'CreationTime': datetime(2015, 1, 1), 'TrainingStartTime': datetime(2015, 1, 1), 'TrainingEndTime': datetime(2015, 1, 1), 'TrainingJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped', 'TunedHyperParameters': { 'string': 'string' }, 'FailureReason': 'string', 'FinalHyperParameterTuningJobObjectiveMetric': { 'Type': 'Maximize'|'Minimize', 'MetricName': 'string', 'Value': ... }, 'ObjectiveStatus': 'Succeeded'|'Pending'|'Failed' }, ], } **Response Structure** * *(dict) --* * **TrainingJobSummaries** *(list) --* A list of TrainingJobSummary objects that describe the training jobs that the "ListTrainingJobsForHyperParameterTuningJob" request returned. * *(dict) --* The container for the summary information about a training job. * **TrainingJobDefinitionName** *(string) --* The training job definition name. * **TrainingJobName** *(string) --* The name of the training job. * **TrainingJobArn** *(string) --* The Amazon Resource Name (ARN) of the training job. * **TuningJobName** *(string) --* The HyperParameter tuning job that launched the training job. * **CreationTime** *(datetime) --* The date and time that the training job was created. * **TrainingStartTime** *(datetime) --* The date and time that the training job started. * **TrainingEndTime** *(datetime) --* Specifies the time when the training job ends on training instances. You are billed for the time interval between the value of "TrainingStartTime" and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when SageMaker detects a job failure. * **TrainingJobStatus** *(string) --* The status of the training job. * **TunedHyperParameters** *(dict) --* A list of the hyperparameters for which you specified ranges to search. * *(string) --* * *(string) --* * **FailureReason** *(string) --* The reason that the training job failed. * **FinalHyperParameterTuningJobObjectiveMetric** *(dict) --* The FinalHyperParameterTuningJobObjectiveMetric object that specifies the value of the objective metric of the tuning job that launched this training job. * **Type** *(string) --* Select if you want to minimize or maximize the objective metric during hyperparameter tuning. * **MetricName** *(string) --* The name of the objective metric. For SageMaker built-in algorithms, metrics are defined per algorithm. See the metrics for XGBoost as an example. You can also use a custom algorithm for training and define your own metrics. For more information, see Define metrics and environment variables. * **Value** *(float) --* The value of the objective metric. * **ObjectiveStatus** *(string) --* The status of the objective metric for the training job: * Succeeded: The final objective metric for the training job was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process. * Pending: The training job is in progress and evaluation of its final objective metric is pending. * Failed: The final objective metric for the training job was not evaluated, and was not used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric. SageMaker / Paginator / ListNotebookInstances ListNotebookInstances ********************* class SageMaker.Paginator.ListNotebookInstances paginator = client.get_paginator('list_notebook_instances') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_notebook_instances()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( SortBy='Name'|'CreationTime'|'Status', SortOrder='Ascending'|'Descending', NameContains='string', CreationTimeBefore=datetime(2015, 1, 1), CreationTimeAfter=datetime(2015, 1, 1), LastModifiedTimeBefore=datetime(2015, 1, 1), LastModifiedTimeAfter=datetime(2015, 1, 1), StatusEquals='Pending'|'InService'|'Stopping'|'Stopped'|'Failed'|'Deleting'|'Updating', NotebookInstanceLifecycleConfigNameContains='string', DefaultCodeRepositoryContains='string', AdditionalCodeRepositoryEquals='string', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **SortBy** (*string*) -- The field to sort results by. The default is "Name". * **SortOrder** (*string*) -- The sort order for results. * **NameContains** (*string*) -- A string in the notebook instances' name. This filter returns only notebook instances whose name contains the specified string. * **CreationTimeBefore** (*datetime*) -- A filter that returns only notebook instances that were created before the specified time (timestamp). * **CreationTimeAfter** (*datetime*) -- A filter that returns only notebook instances that were created after the specified time (timestamp). * **LastModifiedTimeBefore** (*datetime*) -- A filter that returns only notebook instances that were modified before the specified time (timestamp). * **LastModifiedTimeAfter** (*datetime*) -- A filter that returns only notebook instances that were modified after the specified time (timestamp). * **StatusEquals** (*string*) -- A filter that returns only notebook instances with the specified status. * **NotebookInstanceLifecycleConfigNameContains** (*string*) -- A string in the name of a notebook instances lifecycle configuration associated with this notebook instance. This filter returns only notebook instances associated with a lifecycle configuration with a name that contains the specified string. * **DefaultCodeRepositoryContains** (*string*) -- A string in the name or URL of a Git repository associated with this notebook instance. This filter returns only notebook instances associated with a git repository with a name that contains the specified string. * **AdditionalCodeRepositoryEquals** (*string*) -- A filter that returns only notebook instances with associated with the specified git repository. * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'NotebookInstances': [ { 'NotebookInstanceName': 'string', 'NotebookInstanceArn': 'string', 'NotebookInstanceStatus': 'Pending'|'InService'|'Stopping'|'Stopped'|'Failed'|'Deleting'|'Updating', 'Url': 'string', 'InstanceType': 'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge', 'CreationTime': datetime(2015, 1, 1), 'LastModifiedTime': datetime(2015, 1, 1), 'NotebookInstanceLifecycleConfigName': 'string', 'DefaultCodeRepository': 'string', 'AdditionalCodeRepositories': [ 'string', ] }, ] } **Response Structure** * *(dict) --* * **NotebookInstances** *(list) --* An array of "NotebookInstanceSummary" objects, one for each notebook instance. * *(dict) --* Provides summary information for an SageMaker AI notebook instance. * **NotebookInstanceName** *(string) --* The name of the notebook instance that you want a summary for. * **NotebookInstanceArn** *(string) --* The Amazon Resource Name (ARN) of the notebook instance. * **NotebookInstanceStatus** *(string) --* The status of the notebook instance. * **Url** *(string) --* The URL that you use to connect to the Jupyter notebook running in your notebook instance. * **InstanceType** *(string) --* The type of ML compute instance that the notebook instance is running on. * **CreationTime** *(datetime) --* A timestamp that shows when the notebook instance was created. * **LastModifiedTime** *(datetime) --* A timestamp that shows when the notebook instance was last modified. * **NotebookInstanceLifecycleConfigName** *(string) --* The name of a notebook instance lifecycle configuration associated with this notebook instance. For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance. * **DefaultCodeRepository** *(string) --* The Git repository associated with the notebook instance as its default code repository. This can be either the name of a Git repository stored as a resource in your account, or the URL of a Git repository in Amazon Web Services CodeCommit or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see Associating Git Repositories with SageMaker AI Notebook Instances. * **AdditionalCodeRepositories** *(list) --* An array of up to three Git repositories associated with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in Amazon Web Services CodeCommit or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with SageMaker AI Notebook Instances. * *(string) --* SageMaker / Paginator / ListMonitoringAlerts ListMonitoringAlerts ******************** class SageMaker.Paginator.ListMonitoringAlerts paginator = client.get_paginator('list_monitoring_alerts') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_monitoring_alerts()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( MonitoringScheduleName='string', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **MonitoringScheduleName** (*string*) -- **[REQUIRED]** The name of a monitoring schedule. * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'MonitoringAlertSummaries': [ { 'MonitoringAlertName': 'string', 'CreationTime': datetime(2015, 1, 1), 'LastModifiedTime': datetime(2015, 1, 1), 'AlertStatus': 'InAlert'|'OK', 'DatapointsToAlert': 123, 'EvaluationPeriod': 123, 'Actions': { 'ModelDashboardIndicator': { 'Enabled': True|False } } }, ], } **Response Structure** * *(dict) --* * **MonitoringAlertSummaries** *(list) --* A JSON array where each element is a summary for a monitoring alert. * *(dict) --* Provides summary information about a monitor alert. * **MonitoringAlertName** *(string) --* The name of a monitoring alert. * **CreationTime** *(datetime) --* A timestamp that indicates when a monitor alert was created. * **LastModifiedTime** *(datetime) --* A timestamp that indicates when a monitor alert was last updated. * **AlertStatus** *(string) --* The current status of an alert. * **DatapointsToAlert** *(integer) --* Within "EvaluationPeriod", how many execution failures will raise an alert. * **EvaluationPeriod** *(integer) --* The number of most recent monitoring executions to consider when evaluating alert status. * **Actions** *(dict) --* A list of alert actions taken in response to an alert going into "InAlert" status. * **ModelDashboardIndicator** *(dict) --* An alert action taken to light up an icon on the Model Dashboard when an alert goes into "InAlert" status. * **Enabled** *(boolean) --* Indicates whether the alert action is turned on. SageMaker / Paginator / ListEndpointConfigs ListEndpointConfigs ******************* class SageMaker.Paginator.ListEndpointConfigs paginator = client.get_paginator('list_endpoint_configs') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_endpoint_configs()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( SortBy='Name'|'CreationTime', SortOrder='Ascending'|'Descending', NameContains='string', CreationTimeBefore=datetime(2015, 1, 1), CreationTimeAfter=datetime(2015, 1, 1), PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **SortBy** (*string*) -- The field to sort results by. The default is "CreationTime". * **SortOrder** (*string*) -- The sort order for results. The default is "Descending". * **NameContains** (*string*) -- A string in the endpoint configuration name. This filter returns only endpoint configurations whose name contains the specified string. * **CreationTimeBefore** (*datetime*) -- A filter that returns only endpoint configurations created before the specified time (timestamp). * **CreationTimeAfter** (*datetime*) -- A filter that returns only endpoint configurations with a creation time greater than or equal to the specified time (timestamp). * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'EndpointConfigs': [ { 'EndpointConfigName': 'string', 'EndpointConfigArn': 'string', 'CreationTime': datetime(2015, 1, 1) }, ], } **Response Structure** * *(dict) --* * **EndpointConfigs** *(list) --* An array of endpoint configurations. * *(dict) --* Provides summary information for an endpoint configuration. * **EndpointConfigName** *(string) --* The name of the endpoint configuration. * **EndpointConfigArn** *(string) --* The Amazon Resource Name (ARN) of the endpoint configuration. * **CreationTime** *(datetime) --* A timestamp that shows when the endpoint configuration was created. SageMaker / Paginator / ListMonitoringAlertHistory ListMonitoringAlertHistory ************************** class SageMaker.Paginator.ListMonitoringAlertHistory paginator = client.get_paginator('list_monitoring_alert_history') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_monitoring_alert_history()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( MonitoringScheduleName='string', MonitoringAlertName='string', SortBy='CreationTime'|'Status', SortOrder='Ascending'|'Descending', CreationTimeBefore=datetime(2015, 1, 1), CreationTimeAfter=datetime(2015, 1, 1), StatusEquals='InAlert'|'OK', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **MonitoringScheduleName** (*string*) -- The name of a monitoring schedule. * **MonitoringAlertName** (*string*) -- The name of a monitoring alert. * **SortBy** (*string*) -- The field used to sort results. The default is "CreationTime". * **SortOrder** (*string*) -- The sort order, whether "Ascending" or "Descending", of the alert history. The default is "Descending". * **CreationTimeBefore** (*datetime*) -- A filter that returns only alerts created on or before the specified time. * **CreationTimeAfter** (*datetime*) -- A filter that returns only alerts created on or after the specified time. * **StatusEquals** (*string*) -- A filter that retrieves only alerts with a specific status. * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'MonitoringAlertHistory': [ { 'MonitoringScheduleName': 'string', 'MonitoringAlertName': 'string', 'CreationTime': datetime(2015, 1, 1), 'AlertStatus': 'InAlert'|'OK' }, ], } **Response Structure** * *(dict) --* * **MonitoringAlertHistory** *(list) --* An alert history for a model monitoring schedule. * *(dict) --* Provides summary information of an alert's history. * **MonitoringScheduleName** *(string) --* The name of a monitoring schedule. * **MonitoringAlertName** *(string) --* The name of a monitoring alert. * **CreationTime** *(datetime) --* A timestamp that indicates when the first alert transition occurred in an alert history. An alert transition can be from status "InAlert" to "OK", or from "OK" to "InAlert". * **AlertStatus** *(string) --* The current alert status of an alert. SageMaker / Paginator / ListLabelingJobs ListLabelingJobs **************** class SageMaker.Paginator.ListLabelingJobs paginator = client.get_paginator('list_labeling_jobs') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_labeling_jobs()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( CreationTimeAfter=datetime(2015, 1, 1), CreationTimeBefore=datetime(2015, 1, 1), LastModifiedTimeAfter=datetime(2015, 1, 1), LastModifiedTimeBefore=datetime(2015, 1, 1), NameContains='string', SortBy='Name'|'CreationTime'|'Status', SortOrder='Ascending'|'Descending', StatusEquals='Initializing'|'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **CreationTimeAfter** (*datetime*) -- A filter that returns only labeling jobs created after the specified time (timestamp). * **CreationTimeBefore** (*datetime*) -- A filter that returns only labeling jobs created before the specified time (timestamp). * **LastModifiedTimeAfter** (*datetime*) -- A filter that returns only labeling jobs modified after the specified time (timestamp). * **LastModifiedTimeBefore** (*datetime*) -- A filter that returns only labeling jobs modified before the specified time (timestamp). * **NameContains** (*string*) -- A string in the labeling job name. This filter returns only labeling jobs whose name contains the specified string. * **SortBy** (*string*) -- The field to sort results by. The default is "CreationTime". * **SortOrder** (*string*) -- The sort order for results. The default is "Ascending". * **StatusEquals** (*string*) -- A filter that retrieves only labeling jobs with a specific status. * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'LabelingJobSummaryList': [ { 'LabelingJobName': 'string', 'LabelingJobArn': 'string', 'CreationTime': datetime(2015, 1, 1), 'LastModifiedTime': datetime(2015, 1, 1), 'LabelingJobStatus': 'Initializing'|'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped', 'LabelCounters': { 'TotalLabeled': 123, 'HumanLabeled': 123, 'MachineLabeled': 123, 'FailedNonRetryableError': 123, 'Unlabeled': 123 }, 'WorkteamArn': 'string', 'PreHumanTaskLambdaArn': 'string', 'AnnotationConsolidationLambdaArn': 'string', 'FailureReason': 'string', 'LabelingJobOutput': { 'OutputDatasetS3Uri': 'string', 'FinalActiveLearningModelArn': 'string' }, 'InputConfig': { 'DataSource': { 'S3DataSource': { 'ManifestS3Uri': 'string' }, 'SnsDataSource': { 'SnsTopicArn': 'string' } }, 'DataAttributes': { 'ContentClassifiers': [ 'FreeOfPersonallyIdentifiableInformation'|'FreeOfAdultContent', ] } } }, ], } **Response Structure** * *(dict) --* * **LabelingJobSummaryList** *(list) --* An array of "LabelingJobSummary" objects, each describing a labeling job. * *(dict) --* Provides summary information about a labeling job. * **LabelingJobName** *(string) --* The name of the labeling job. * **LabelingJobArn** *(string) --* The Amazon Resource Name (ARN) assigned to the labeling job when it was created. * **CreationTime** *(datetime) --* The date and time that the job was created (timestamp). * **LastModifiedTime** *(datetime) --* The date and time that the job was last modified (timestamp). * **LabelingJobStatus** *(string) --* The current status of the labeling job. * **LabelCounters** *(dict) --* Counts showing the progress of the labeling job. * **TotalLabeled** *(integer) --* The total number of objects labeled. * **HumanLabeled** *(integer) --* The total number of objects labeled by a human worker. * **MachineLabeled** *(integer) --* The total number of objects labeled by automated data labeling. * **FailedNonRetryableError** *(integer) --* The total number of objects that could not be labeled due to an error. * **Unlabeled** *(integer) --* The total number of objects not yet labeled. * **WorkteamArn** *(string) --* The Amazon Resource Name (ARN) of the work team assigned to the job. * **PreHumanTaskLambdaArn** *(string) --* The Amazon Resource Name (ARN) of a Lambda function. The function is run before each data object is sent to a worker. * **AnnotationConsolidationLambdaArn** *(string) --* The Amazon Resource Name (ARN) of the Lambda function used to consolidate the annotations from individual workers into a label for a data object. For more information, see Annotation Consolidation. * **FailureReason** *(string) --* If the "LabelingJobStatus" field is "Failed", this field contains a description of the error. * **LabelingJobOutput** *(dict) --* The location of the output produced by the labeling job. * **OutputDatasetS3Uri** *(string) --* The Amazon S3 bucket location of the manifest file for labeled data. * **FinalActiveLearningModelArn** *(string) --* The Amazon Resource Name (ARN) for the most recent SageMaker model trained as part of automated data labeling. * **InputConfig** *(dict) --* Input configuration for the labeling job. * **DataSource** *(dict) --* The location of the input data. * **S3DataSource** *(dict) --* The Amazon S3 location of the input data objects. * **ManifestS3Uri** *(string) --* The Amazon S3 location of the manifest file that describes the input data objects. The input manifest file referenced in "ManifestS3Uri" must contain one of the following keys: "source-ref" or "source". The value of the keys are interpreted as follows: * "source-ref": The source of the object is the Amazon S3 object specified in the value. Use this value when the object is a binary object, such as an image. * "source": The source of the object is the value. Use this value when the object is a text value. If you are a new user of Ground Truth, it is recommended you review Use an Input Manifest File in the Amazon SageMaker Developer Guide to learn how to create an input manifest file. * **SnsDataSource** *(dict) --* An Amazon SNS data source used for streaming labeling jobs. To learn more, see Send Data to a Streaming Labeling Job. * **SnsTopicArn** *(string) --* The Amazon SNS input topic Amazon Resource Name (ARN). Specify the ARN of the input topic you will use to send new data objects to a streaming labeling job. * **DataAttributes** *(dict) --* Attributes of the data specified by the customer. * **ContentClassifiers** *(list) --* Declares that your content is free of personally identifiable information or adult content. SageMaker may restrict the Amazon Mechanical Turk workers that can view your task based on this information. * *(string) --* SageMaker / Paginator / ListInferenceRecommendationsJobSteps ListInferenceRecommendationsJobSteps ************************************ class SageMaker.Paginator.ListInferenceRecommendationsJobSteps paginator = client.get_paginator('list_inference_recommendations_job_steps') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_inference_recommendations_job_steps()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( JobName='string', Status='PENDING'|'IN_PROGRESS'|'COMPLETED'|'FAILED'|'STOPPING'|'STOPPED'|'DELETING'|'DELETED', StepType='BENCHMARK', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **JobName** (*string*) -- **[REQUIRED]** The name for the Inference Recommender job. * **Status** (*string*) -- A filter to return benchmarks of a specified status. If this field is left empty, then all benchmarks are returned. * **StepType** (*string*) -- A filter to return details about the specified type of subtask. "BENCHMARK": Evaluate the performance of your model on different instance types. * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'Steps': [ { 'StepType': 'BENCHMARK', 'JobName': 'string', 'Status': 'PENDING'|'IN_PROGRESS'|'COMPLETED'|'FAILED'|'STOPPING'|'STOPPED'|'DELETING'|'DELETED', 'InferenceBenchmark': { 'Metrics': { 'CostPerHour': ..., 'CostPerInference': ..., 'MaxInvocations': 123, 'ModelLatency': 123, 'CpuUtilization': ..., 'MemoryUtilization': ..., 'ModelSetupTime': 123 }, 'EndpointMetrics': { 'MaxInvocations': 123, 'ModelLatency': 123 }, 'EndpointConfiguration': { 'EndpointName': 'string', 'VariantName': 'string', 'InstanceType': 'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.dl1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.r8g.medium'|'ml.r8g.large'|'ml.r8g.xlarge'|'ml.r8g.2xlarge'|'ml.r8g.4xlarge'|'ml.r8g.8xlarge'|'ml.r8g.12xlarge'|'ml.r8g.16xlarge'|'ml.r8g.24xlarge'|'ml.r8g.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.trn2.48xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.p5e.48xlarge'|'ml.p5en.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.c8g.medium'|'ml.c8g.large'|'ml.c8g.xlarge'|'ml.c8g.2xlarge'|'ml.c8g.4xlarge'|'ml.c8g.8xlarge'|'ml.c8g.12xlarge'|'ml.c8g.16xlarge'|'ml.c8g.24xlarge'|'ml.c8g.48xlarge'|'ml.r7gd.medium'|'ml.r7gd.large'|'ml.r7gd.xlarge'|'ml.r7gd.2xlarge'|'ml.r7gd.4xlarge'|'ml.r7gd.8xlarge'|'ml.r7gd.12xlarge'|'ml.r7gd.16xlarge'|'ml.m8g.medium'|'ml.m8g.large'|'ml.m8g.xlarge'|'ml.m8g.2xlarge'|'ml.m8g.4xlarge'|'ml.m8g.8xlarge'|'ml.m8g.12xlarge'|'ml.m8g.16xlarge'|'ml.m8g.24xlarge'|'ml.m8g.48xlarge'|'ml.c6in.large'|'ml.c6in.xlarge'|'ml.c6in.2xlarge'|'ml.c6in.4xlarge'|'ml.c6in.8xlarge'|'ml.c6in.12xlarge'|'ml.c6in.16xlarge'|'ml.c6in.24xlarge'|'ml.c6in.32xlarge'|'ml.p6-b200.48xlarge'|'ml.p6e-gb200.36xlarge', 'InitialInstanceCount': 123, 'ServerlessConfig': { 'MemorySizeInMB': 123, 'MaxConcurrency': 123, 'ProvisionedConcurrency': 123 } }, 'ModelConfiguration': { 'InferenceSpecificationName': 'string', 'EnvironmentParameters': [ { 'Key': 'string', 'ValueType': 'string', 'Value': 'string' }, ], 'CompilationJobName': 'string' }, 'FailureReason': 'string', 'InvocationEndTime': datetime(2015, 1, 1), 'InvocationStartTime': datetime(2015, 1, 1) } }, ], } **Response Structure** * *(dict) --* * **Steps** *(list) --* A list of all subtask details in Inference Recommender. * *(dict) --* A returned array object for the "Steps" response field in the ListInferenceRecommendationsJobSteps API command. * **StepType** *(string) --* The type of the subtask. "BENCHMARK": Evaluate the performance of your model on different instance types. * **JobName** *(string) --* The name of the Inference Recommender job. * **Status** *(string) --* The current status of the benchmark. * **InferenceBenchmark** *(dict) --* The details for a specific benchmark. * **Metrics** *(dict) --* The metrics of recommendations. * **CostPerHour** *(float) --* Defines the cost per hour for the instance. * **CostPerInference** *(float) --* Defines the cost per inference for the instance . * **MaxInvocations** *(integer) --* The expected maximum number of requests per minute for the instance. * **ModelLatency** *(integer) --* The expected model latency at maximum invocation per minute for the instance. * **CpuUtilization** *(float) --* The expected CPU utilization at maximum invocations per minute for the instance. "NaN" indicates that the value is not available. * **MemoryUtilization** *(float) --* The expected memory utilization at maximum invocations per minute for the instance. "NaN" indicates that the value is not available. * **ModelSetupTime** *(integer) --* The time it takes to launch new compute resources for a serverless endpoint. The time can vary depending on the model size, how long it takes to download the model, and the start-up time of the container. "NaN" indicates that the value is not available. * **EndpointMetrics** *(dict) --* The metrics for an existing endpoint compared in an Inference Recommender job. * **MaxInvocations** *(integer) --* The expected maximum number of requests per minute for the instance. * **ModelLatency** *(integer) --* The expected model latency at maximum invocations per minute for the instance. * **EndpointConfiguration** *(dict) --* The endpoint configuration made by Inference Recommender during a recommendation job. * **EndpointName** *(string) --* The name of the endpoint made during a recommendation job. * **VariantName** *(string) --* The name of the production variant (deployed model) made during a recommendation job. * **InstanceType** *(string) --* The instance type recommended by Amazon SageMaker Inference Recommender. * **InitialInstanceCount** *(integer) --* The number of instances recommended to launch initially. * **ServerlessConfig** *(dict) --* Specifies the serverless configuration for an endpoint variant. * **MemorySizeInMB** *(integer) --* The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB. * **MaxConcurrency** *(integer) --* The maximum number of concurrent invocations your serverless endpoint can process. * **ProvisionedConcurrency** *(integer) --* The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to "MaxConcurrency". Note: This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs. * **ModelConfiguration** *(dict) --* Defines the model configuration. Includes the specification name and environment parameters. * **InferenceSpecificationName** *(string) --* The inference specification name in the model package version. * **EnvironmentParameters** *(list) --* Defines the environment parameters that includes key, value types, and values. * *(dict) --* A list of environment parameters suggested by the Amazon SageMaker Inference Recommender. * **Key** *(string) --* The environment key suggested by the Amazon SageMaker Inference Recommender. * **ValueType** *(string) --* The value type suggested by the Amazon SageMaker Inference Recommender. * **Value** *(string) --* The value suggested by the Amazon SageMaker Inference Recommender. * **CompilationJobName** *(string) --* The name of the compilation job used to create the recommended model artifacts. * **FailureReason** *(string) --* The reason why a benchmark failed. * **InvocationEndTime** *(datetime) --* A timestamp that shows when the benchmark completed. * **InvocationStartTime** *(datetime) --* A timestamp that shows when the benchmark started. SageMaker / Paginator / ListFlowDefinitions ListFlowDefinitions ******************* class SageMaker.Paginator.ListFlowDefinitions paginator = client.get_paginator('list_flow_definitions') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_flow_definitions()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( CreationTimeAfter=datetime(2015, 1, 1), CreationTimeBefore=datetime(2015, 1, 1), SortOrder='Ascending'|'Descending', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **CreationTimeAfter** (*datetime*) -- A filter that returns only flow definitions with a creation time greater than or equal to the specified timestamp. * **CreationTimeBefore** (*datetime*) -- A filter that returns only flow definitions that were created before the specified timestamp. * **SortOrder** (*string*) -- An optional value that specifies whether you want the results sorted in "Ascending" or "Descending" order. * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'FlowDefinitionSummaries': [ { 'FlowDefinitionName': 'string', 'FlowDefinitionArn': 'string', 'FlowDefinitionStatus': 'Initializing'|'Active'|'Failed'|'Deleting', 'CreationTime': datetime(2015, 1, 1), 'FailureReason': 'string' }, ], } **Response Structure** * *(dict) --* * **FlowDefinitionSummaries** *(list) --* An array of objects describing the flow definitions. * *(dict) --* Contains summary information about the flow definition. * **FlowDefinitionName** *(string) --* The name of the flow definition. * **FlowDefinitionArn** *(string) --* The Amazon Resource Name (ARN) of the flow definition. * **FlowDefinitionStatus** *(string) --* The status of the flow definition. Valid values: * **CreationTime** *(datetime) --* The timestamp when SageMaker created the flow definition. * **FailureReason** *(string) --* The reason why the flow definition creation failed. A failure reason is returned only when the flow definition status is "Failed". SageMaker / Paginator / ListActions ListActions *********** class SageMaker.Paginator.ListActions paginator = client.get_paginator('list_actions') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_actions()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( SourceUri='string', ActionType='string', CreatedAfter=datetime(2015, 1, 1), CreatedBefore=datetime(2015, 1, 1), SortBy='Name'|'CreationTime', SortOrder='Ascending'|'Descending', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **SourceUri** (*string*) -- A filter that returns only actions with the specified source URI. * **ActionType** (*string*) -- A filter that returns only actions of the specified type. * **CreatedAfter** (*datetime*) -- A filter that returns only actions created on or after the specified time. * **CreatedBefore** (*datetime*) -- A filter that returns only actions created on or before the specified time. * **SortBy** (*string*) -- The property used to sort results. The default value is "CreationTime". * **SortOrder** (*string*) -- The sort order. The default value is "Descending". * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'ActionSummaries': [ { 'ActionArn': 'string', 'ActionName': 'string', 'Source': { 'SourceUri': 'string', 'SourceType': 'string', 'SourceId': 'string' }, 'ActionType': 'string', 'Status': 'Unknown'|'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped', 'CreationTime': datetime(2015, 1, 1), 'LastModifiedTime': datetime(2015, 1, 1) }, ], } **Response Structure** * *(dict) --* * **ActionSummaries** *(list) --* A list of actions and their properties. * *(dict) --* Lists the properties of an *action*. An action represents an action or activity. Some examples are a workflow step and a model deployment. Generally, an action involves at least one input artifact or output artifact. * **ActionArn** *(string) --* The Amazon Resource Name (ARN) of the action. * **ActionName** *(string) --* The name of the action. * **Source** *(dict) --* The source of the action. * **SourceUri** *(string) --* The URI of the source. * **SourceType** *(string) --* The type of the source. * **SourceId** *(string) --* The ID of the source. * **ActionType** *(string) --* The type of the action. * **Status** *(string) --* The status of the action. * **CreationTime** *(datetime) --* When the action was created. * **LastModifiedTime** *(datetime) --* When the action was last modified. SageMaker / Paginator / ListPipelineVersions ListPipelineVersions ******************** class SageMaker.Paginator.ListPipelineVersions paginator = client.get_paginator('list_pipeline_versions') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_pipeline_versions()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( PipelineName='string', CreatedAfter=datetime(2015, 1, 1), CreatedBefore=datetime(2015, 1, 1), SortOrder='Ascending'|'Descending', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **PipelineName** (*string*) -- **[REQUIRED]** The Amazon Resource Name (ARN) of the pipeline. * **CreatedAfter** (*datetime*) -- A filter that returns the pipeline versions that were created after a specified time. * **CreatedBefore** (*datetime*) -- A filter that returns the pipeline versions that were created before a specified time. * **SortOrder** (*string*) -- The sort order for the results. * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'PipelineVersionSummaries': [ { 'PipelineArn': 'string', 'PipelineVersionId': 123, 'CreationTime': datetime(2015, 1, 1), 'PipelineVersionDescription': 'string', 'PipelineVersionDisplayName': 'string', 'LastExecutionPipelineExecutionArn': 'string' }, ], } **Response Structure** * *(dict) --* * **PipelineVersionSummaries** *(list) --* Contains a sorted list of pipeline version summary objects matching the specified filters. Each version summary includes the pipeline version ID, the creation date, and the last pipeline execution created from that version. This list can be empty. * *(dict) --* The summary of the pipeline version. * **PipelineArn** *(string) --* The Amazon Resource Name (ARN) of the pipeline. * **PipelineVersionId** *(integer) --* The ID of the pipeline version. * **CreationTime** *(datetime) --* The creation time of the pipeline version. * **PipelineVersionDescription** *(string) --* The description of the pipeline version. * **PipelineVersionDisplayName** *(string) --* The display name of the pipeline version. * **LastExecutionPipelineExecutionArn** *(string) --* The Amazon Resource Name (ARN) of the most recent pipeline execution created from this pipeline version. SageMaker / Paginator / ListWorkteams ListWorkteams ************* class SageMaker.Paginator.ListWorkteams paginator = client.get_paginator('list_workteams') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_workteams()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( SortBy='Name'|'CreateDate', SortOrder='Ascending'|'Descending', NameContains='string', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **SortBy** (*string*) -- The field to sort results by. The default is "CreationTime". * **SortOrder** (*string*) -- The sort order for results. The default is "Ascending". * **NameContains** (*string*) -- A string in the work team's name. This filter returns only work teams whose name contains the specified string. * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'Workteams': [ { 'WorkteamName': 'string', 'MemberDefinitions': [ { 'CognitoMemberDefinition': { 'UserPool': 'string', 'UserGroup': 'string', 'ClientId': 'string' }, 'OidcMemberDefinition': { 'Groups': [ 'string', ] } }, ], 'WorkteamArn': 'string', 'WorkforceArn': 'string', 'ProductListingIds': [ 'string', ], 'Description': 'string', 'SubDomain': 'string', 'CreateDate': datetime(2015, 1, 1), 'LastUpdatedDate': datetime(2015, 1, 1), 'NotificationConfiguration': { 'NotificationTopicArn': 'string' }, 'WorkerAccessConfiguration': { 'S3Presign': { 'IamPolicyConstraints': { 'SourceIp': 'Enabled'|'Disabled', 'VpcSourceIp': 'Enabled'|'Disabled' } } } }, ], } **Response Structure** * *(dict) --* * **Workteams** *(list) --* An array of "Workteam" objects, each describing a work team. * *(dict) --* Provides details about a labeling work team. * **WorkteamName** *(string) --* The name of the work team. * **MemberDefinitions** *(list) --* A list of "MemberDefinition" objects that contains objects that identify the workers that make up the work team. Workforces can be created using Amazon Cognito or your own OIDC Identity Provider (IdP). For private workforces created using Amazon Cognito use "CognitoMemberDefinition". For workforces created using your own OIDC identity provider (IdP) use "OidcMemberDefinition". * *(dict) --* Defines an Amazon Cognito or your own OIDC IdP user group that is part of a work team. * **CognitoMemberDefinition** *(dict) --* The Amazon Cognito user group that is part of the work team. * **UserPool** *(string) --* An identifier for a user pool. The user pool must be in the same region as the service that you are calling. * **UserGroup** *(string) --* An identifier for a user group. * **ClientId** *(string) --* An identifier for an application client. You must create the app client ID using Amazon Cognito. * **OidcMemberDefinition** *(dict) --* A list user groups that exist in your OIDC Identity Provider (IdP). One to ten groups can be used to create a single private work team. When you add a user group to the list of "Groups", you can add that user group to one or more private work teams. If you add a user group to a private work team, all workers in that user group are added to the work team. * **Groups** *(list) --* A list of comma seperated strings that identifies user groups in your OIDC IdP. Each user group is made up of a group of private workers. * *(string) --* * **WorkteamArn** *(string) --* The Amazon Resource Name (ARN) that identifies the work team. * **WorkforceArn** *(string) --* The Amazon Resource Name (ARN) of the workforce. * **ProductListingIds** *(list) --* The Amazon Marketplace identifier for a vendor's work team. * *(string) --* * **Description** *(string) --* A description of the work team. * **SubDomain** *(string) --* The URI of the labeling job's user interface. Workers open this URI to start labeling your data objects. * **CreateDate** *(datetime) --* The date and time that the work team was created (timestamp). * **LastUpdatedDate** *(datetime) --* The date and time that the work team was last updated (timestamp). * **NotificationConfiguration** *(dict) --* Configures SNS notifications of available or expiring work items for work teams. * **NotificationTopicArn** *(string) --* The ARN for the Amazon SNS topic to which notifications should be published. * **WorkerAccessConfiguration** *(dict) --* Describes any access constraints that have been defined for Amazon S3 resources. * **S3Presign** *(dict) --* Defines any Amazon S3 resource constraints. * **IamPolicyConstraints** *(dict) --* Use this parameter to specify the allowed request source. Possible sources are either "SourceIp" or "VpcSourceIp". * **SourceIp** *(string) --* When "SourceIp" is "Enabled" the worker's IP address when a task is rendered in the worker portal is added to the IAM policy as a "Condition" used to generate the Amazon S3 presigned URL. This IP address is checked by Amazon S3 and must match in order for the Amazon S3 resource to be rendered in the worker portal. * **VpcSourceIp** *(string) --* When "VpcSourceIp" is "Enabled" the worker's IP address when a task is rendered in private worker portal inside the VPC is added to the IAM policy as a "Condition" used to generate the Amazon S3 presigned URL. To render the task successfully Amazon S3 checks that the presigned URL is being accessed over an Amazon S3 VPC Endpoint, and that the worker's IP address matches the IP address in the IAM policy. To learn more about configuring private worker portal, see Use Amazon VPC mode from a private worker portal. SageMaker / Paginator / ListComputeQuotas ListComputeQuotas ***************** class SageMaker.Paginator.ListComputeQuotas paginator = client.get_paginator('list_compute_quotas') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_compute_quotas()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( CreatedAfter=datetime(2015, 1, 1), CreatedBefore=datetime(2015, 1, 1), NameContains='string', Status='Creating'|'CreateFailed'|'CreateRollbackFailed'|'Created'|'Updating'|'UpdateFailed'|'UpdateRollbackFailed'|'Updated'|'Deleting'|'DeleteFailed'|'DeleteRollbackFailed'|'Deleted', ClusterArn='string', SortBy='Name'|'CreationTime'|'Status'|'ClusterArn', SortOrder='Ascending'|'Descending', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **CreatedAfter** (*datetime*) -- Filter for after this creation time. The input for this parameter is a Unix timestamp. To convert a date and time into a Unix timestamp, see EpochConverter. * **CreatedBefore** (*datetime*) -- Filter for before this creation time. The input for this parameter is a Unix timestamp. To convert a date and time into a Unix timestamp, see EpochConverter. * **NameContains** (*string*) -- Filter for name containing this string. * **Status** (*string*) -- Filter for status. * **ClusterArn** (*string*) -- Filter for ARN of the cluster. * **SortBy** (*string*) -- Filter for sorting the list by a given value. For example, sort by name, creation time, or status. * **SortOrder** (*string*) -- The order of the list. By default, listed in "Descending" order according to by "SortBy". To change the list order, you can specify "SortOrder" to be "Ascending". * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'ComputeQuotaSummaries': [ { 'ComputeQuotaArn': 'string', 'ComputeQuotaId': 'string', 'Name': 'string', 'ComputeQuotaVersion': 123, 'Status': 'Creating'|'CreateFailed'|'CreateRollbackFailed'|'Created'|'Updating'|'UpdateFailed'|'UpdateRollbackFailed'|'Updated'|'Deleting'|'DeleteFailed'|'DeleteRollbackFailed'|'Deleted', 'ClusterArn': 'string', 'ComputeQuotaConfig': { 'ComputeQuotaResources': [ { 'InstanceType': 'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.c5n.large'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.16xlarge'|'ml.g6.12xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.gr6.4xlarge'|'ml.gr6.8xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.16xlarge'|'ml.g6e.12xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.p5e.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.trn2.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.i3en.large'|'ml.i3en.xlarge'|'ml.i3en.2xlarge'|'ml.i3en.3xlarge'|'ml.i3en.6xlarge'|'ml.i3en.12xlarge'|'ml.i3en.24xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge', 'Count': 123 }, ], 'ResourceSharingConfig': { 'Strategy': 'Lend'|'DontLend'|'LendAndBorrow', 'BorrowLimit': 123 }, 'PreemptTeamTasks': 'Never'|'LowerPriority' }, 'ComputeQuotaTarget': { 'TeamName': 'string', 'FairShareWeight': 123 }, 'ActivationState': 'Enabled'|'Disabled', 'CreationTime': datetime(2015, 1, 1), 'LastModifiedTime': datetime(2015, 1, 1) }, ], } **Response Structure** * *(dict) --* * **ComputeQuotaSummaries** *(list) --* Summaries of the compute allocation definitions. * *(dict) --* Summary of the compute allocation definition. * **ComputeQuotaArn** *(string) --* ARN of the compute allocation definition. * **ComputeQuotaId** *(string) --* ID of the compute allocation definition. * **Name** *(string) --* Name of the compute allocation definition. * **ComputeQuotaVersion** *(integer) --* Version of the compute allocation definition. * **Status** *(string) --* Status of the compute allocation definition. * **ClusterArn** *(string) --* ARN of the cluster. * **ComputeQuotaConfig** *(dict) --* Configuration of the compute allocation definition. This includes the resource sharing option, and the setting to preempt low priority tasks. * **ComputeQuotaResources** *(list) --* Allocate compute resources by instance types. * *(dict) --* Configuration of the resources used for the compute allocation definition. * **InstanceType** *(string) --* The instance type of the instance group for the cluster. * **Count** *(integer) --* The number of instances to add to the instance group of a SageMaker HyperPod cluster. * **ResourceSharingConfig** *(dict) --* Resource sharing configuration. This defines how an entity can lend and borrow idle compute with other entities within the cluster. * **Strategy** *(string) --* The strategy of how idle compute is shared within the cluster. The following are the options of strategies. * "DontLend": entities do not lend idle compute. * "Lend": entities can lend idle compute to entities that can borrow. * "LendandBorrow": entities can lend idle compute and borrow idle compute from other entities. Default is "LendandBorrow". * **BorrowLimit** *(integer) --* The limit on how much idle compute can be borrowed.The values can be 1 - 500 percent of idle compute that the team is allowed to borrow. Default is "50". * **PreemptTeamTasks** *(string) --* Allows workloads from within an entity to preempt same-team workloads. When set to "LowerPriority", the entity's lower priority tasks are preempted by their own higher priority tasks. Default is "LowerPriority". * **ComputeQuotaTarget** *(dict) --* The target entity to allocate compute resources to. * **TeamName** *(string) --* Name of the team to allocate compute resources to. * **FairShareWeight** *(integer) --* Assigned entity fair-share weight. Idle compute will be shared across entities based on these assigned weights. This weight is only used when "FairShare" is enabled. A weight of 0 is the lowest priority and 100 is the highest. Weight 0 is the default. * **ActivationState** *(string) --* The state of the compute allocation being described. Use to enable or disable compute allocation. Default is "Enabled". * **CreationTime** *(datetime) --* Creation time of the compute allocation definition. * **LastModifiedTime** *(datetime) --* Last modified time of the compute allocation definition. SageMaker / Paginator / ListInferenceRecommendationsJobs ListInferenceRecommendationsJobs ******************************** class SageMaker.Paginator.ListInferenceRecommendationsJobs paginator = client.get_paginator('list_inference_recommendations_jobs') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_inference_recommendations_jobs()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( CreationTimeAfter=datetime(2015, 1, 1), CreationTimeBefore=datetime(2015, 1, 1), LastModifiedTimeAfter=datetime(2015, 1, 1), LastModifiedTimeBefore=datetime(2015, 1, 1), NameContains='string', StatusEquals='PENDING'|'IN_PROGRESS'|'COMPLETED'|'FAILED'|'STOPPING'|'STOPPED'|'DELETING'|'DELETED', SortBy='Name'|'CreationTime'|'Status', SortOrder='Ascending'|'Descending', ModelNameEquals='string', ModelPackageVersionArnEquals='string', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **CreationTimeAfter** (*datetime*) -- A filter that returns only jobs created after the specified time (timestamp). * **CreationTimeBefore** (*datetime*) -- A filter that returns only jobs created before the specified time (timestamp). * **LastModifiedTimeAfter** (*datetime*) -- A filter that returns only jobs that were last modified after the specified time (timestamp). * **LastModifiedTimeBefore** (*datetime*) -- A filter that returns only jobs that were last modified before the specified time (timestamp). * **NameContains** (*string*) -- A string in the job name. This filter returns only recommendations whose name contains the specified string. * **StatusEquals** (*string*) -- A filter that retrieves only inference recommendations jobs with a specific status. * **SortBy** (*string*) -- The parameter by which to sort the results. * **SortOrder** (*string*) -- The sort order for the results. * **ModelNameEquals** (*string*) -- A filter that returns only jobs that were created for this model. * **ModelPackageVersionArnEquals** (*string*) -- A filter that returns only jobs that were created for this versioned model package. * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'InferenceRecommendationsJobs': [ { 'JobName': 'string', 'JobDescription': 'string', 'JobType': 'Default'|'Advanced', 'JobArn': 'string', 'Status': 'PENDING'|'IN_PROGRESS'|'COMPLETED'|'FAILED'|'STOPPING'|'STOPPED'|'DELETING'|'DELETED', 'CreationTime': datetime(2015, 1, 1), 'CompletionTime': datetime(2015, 1, 1), 'RoleArn': 'string', 'LastModifiedTime': datetime(2015, 1, 1), 'FailureReason': 'string', 'ModelName': 'string', 'SamplePayloadUrl': 'string', 'ModelPackageVersionArn': 'string' }, ], } **Response Structure** * *(dict) --* * **InferenceRecommendationsJobs** *(list) --* The recommendations created from the Amazon SageMaker Inference Recommender job. * *(dict) --* A structure that contains a list of recommendation jobs. * **JobName** *(string) --* The name of the job. * **JobDescription** *(string) --* The job description. * **JobType** *(string) --* The recommendation job type. * **JobArn** *(string) --* The Amazon Resource Name (ARN) of the recommendation job. * **Status** *(string) --* The status of the job. * **CreationTime** *(datetime) --* A timestamp that shows when the job was created. * **CompletionTime** *(datetime) --* A timestamp that shows when the job completed. * **RoleArn** *(string) --* The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf. * **LastModifiedTime** *(datetime) --* A timestamp that shows when the job was last modified. * **FailureReason** *(string) --* If the job fails, provides information why the job failed. * **ModelName** *(string) --* The name of the created model. * **SamplePayloadUrl** *(string) --* The Amazon Simple Storage Service (Amazon S3) path where the sample payload is stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). * **ModelPackageVersionArn** *(string) --* The Amazon Resource Name (ARN) of a versioned model package. SageMaker / Paginator / ListImageVersions ListImageVersions ***************** class SageMaker.Paginator.ListImageVersions paginator = client.get_paginator('list_image_versions') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_image_versions()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( CreationTimeAfter=datetime(2015, 1, 1), CreationTimeBefore=datetime(2015, 1, 1), ImageName='string', LastModifiedTimeAfter=datetime(2015, 1, 1), LastModifiedTimeBefore=datetime(2015, 1, 1), SortBy='CREATION_TIME'|'LAST_MODIFIED_TIME'|'VERSION', SortOrder='ASCENDING'|'DESCENDING', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **CreationTimeAfter** (*datetime*) -- A filter that returns only versions created on or after the specified time. * **CreationTimeBefore** (*datetime*) -- A filter that returns only versions created on or before the specified time. * **ImageName** (*string*) -- **[REQUIRED]** The name of the image to list the versions of. * **LastModifiedTimeAfter** (*datetime*) -- A filter that returns only versions modified on or after the specified time. * **LastModifiedTimeBefore** (*datetime*) -- A filter that returns only versions modified on or before the specified time. * **SortBy** (*string*) -- The property used to sort results. The default value is "CREATION_TIME". * **SortOrder** (*string*) -- The sort order. The default value is "DESCENDING". * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'ImageVersions': [ { 'CreationTime': datetime(2015, 1, 1), 'FailureReason': 'string', 'ImageArn': 'string', 'ImageVersionArn': 'string', 'ImageVersionStatus': 'CREATING'|'CREATED'|'CREATE_FAILED'|'DELETING'|'DELETE_FAILED', 'LastModifiedTime': datetime(2015, 1, 1), 'Version': 123 }, ], } **Response Structure** * *(dict) --* * **ImageVersions** *(list) --* A list of versions and their properties. * *(dict) --* A version of a SageMaker AI "Image". A version represents an existing container image. * **CreationTime** *(datetime) --* When the version was created. * **FailureReason** *(string) --* When a create or delete operation fails, the reason for the failure. * **ImageArn** *(string) --* The ARN of the image the version is based on. * **ImageVersionArn** *(string) --* The ARN of the version. * **ImageVersionStatus** *(string) --* The status of the version. * **LastModifiedTime** *(datetime) --* When the version was last modified. * **Version** *(integer) --* The version number. SageMaker / Paginator / ListTrialComponents ListTrialComponents ******************* class SageMaker.Paginator.ListTrialComponents paginator = client.get_paginator('list_trial_components') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_trial_components()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( ExperimentName='string', TrialName='string', SourceArn='string', CreatedAfter=datetime(2015, 1, 1), CreatedBefore=datetime(2015, 1, 1), SortBy='Name'|'CreationTime', SortOrder='Ascending'|'Descending', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **ExperimentName** (*string*) -- A filter that returns only components that are part of the specified experiment. If you specify "ExperimentName", you can't filter by "SourceArn" or "TrialName". * **TrialName** (*string*) -- A filter that returns only components that are part of the specified trial. If you specify "TrialName", you can't filter by "ExperimentName" or "SourceArn". * **SourceArn** (*string*) -- A filter that returns only components that have the specified source Amazon Resource Name (ARN). If you specify "SourceArn", you can't filter by "ExperimentName" or "TrialName". * **CreatedAfter** (*datetime*) -- A filter that returns only components created after the specified time. * **CreatedBefore** (*datetime*) -- A filter that returns only components created before the specified time. * **SortBy** (*string*) -- The property used to sort results. The default value is "CreationTime". * **SortOrder** (*string*) -- The sort order. The default value is "Descending". * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'TrialComponentSummaries': [ { 'TrialComponentName': 'string', 'TrialComponentArn': 'string', 'DisplayName': 'string', 'TrialComponentSource': { 'SourceArn': 'string', 'SourceType': 'string' }, 'Status': { 'PrimaryStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped', 'Message': 'string' }, 'StartTime': datetime(2015, 1, 1), 'EndTime': datetime(2015, 1, 1), 'CreationTime': datetime(2015, 1, 1), 'CreatedBy': { 'UserProfileArn': 'string', 'UserProfileName': 'string', 'DomainId': 'string', 'IamIdentity': { 'Arn': 'string', 'PrincipalId': 'string', 'SourceIdentity': 'string' } }, 'LastModifiedTime': datetime(2015, 1, 1), 'LastModifiedBy': { 'UserProfileArn': 'string', 'UserProfileName': 'string', 'DomainId': 'string', 'IamIdentity': { 'Arn': 'string', 'PrincipalId': 'string', 'SourceIdentity': 'string' } } }, ], } **Response Structure** * *(dict) --* * **TrialComponentSummaries** *(list) --* A list of the summaries of your trial components. * *(dict) --* A summary of the properties of a trial component. To get all the properties, call the DescribeTrialComponent API and provide the "TrialComponentName". * **TrialComponentName** *(string) --* The name of the trial component. * **TrialComponentArn** *(string) --* The Amazon Resource Name (ARN) of the trial component. * **DisplayName** *(string) --* The name of the component as displayed. If "DisplayName" isn't specified, "TrialComponentName" is displayed. * **TrialComponentSource** *(dict) --* The Amazon Resource Name (ARN) and job type of the source of a trial component. * **SourceArn** *(string) --* The source Amazon Resource Name (ARN). * **SourceType** *(string) --* The source job type. * **Status** *(dict) --* The status of the component. States include: * InProgress * Completed * Failed * **PrimaryStatus** *(string) --* The status of the trial component. * **Message** *(string) --* If the component failed, a message describing why. * **StartTime** *(datetime) --* When the component started. * **EndTime** *(datetime) --* When the component ended. * **CreationTime** *(datetime) --* When the component was created. * **CreatedBy** *(dict) --* Who created the trial component. * **UserProfileArn** *(string) --* The Amazon Resource Name (ARN) of the user's profile. * **UserProfileName** *(string) --* The name of the user's profile. * **DomainId** *(string) --* The domain associated with the user. * **IamIdentity** *(dict) --* The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only. * **Arn** *(string) --* The Amazon Resource Name (ARN) of the IAM identity. * **PrincipalId** *(string) --* The ID of the principal that assumes the IAM identity. * **SourceIdentity** *(string) --* The person or application which assumes the IAM identity. * **LastModifiedTime** *(datetime) --* When the component was last modified. * **LastModifiedBy** *(dict) --* Who last modified the component. * **UserProfileArn** *(string) --* The Amazon Resource Name (ARN) of the user's profile. * **UserProfileName** *(string) --* The name of the user's profile. * **DomainId** *(string) --* The domain associated with the user. * **IamIdentity** *(dict) --* The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only. * **Arn** *(string) --* The Amazon Resource Name (ARN) of the IAM identity. * **PrincipalId** *(string) --* The ID of the principal that assumes the IAM identity. * **SourceIdentity** *(string) --* The person or application which assumes the IAM identity. SageMaker / Paginator / ListEndpoints ListEndpoints ************* class SageMaker.Paginator.ListEndpoints paginator = client.get_paginator('list_endpoints') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_endpoints()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( SortBy='Name'|'CreationTime'|'Status', SortOrder='Ascending'|'Descending', NameContains='string', CreationTimeBefore=datetime(2015, 1, 1), CreationTimeAfter=datetime(2015, 1, 1), LastModifiedTimeBefore=datetime(2015, 1, 1), LastModifiedTimeAfter=datetime(2015, 1, 1), StatusEquals='OutOfService'|'Creating'|'Updating'|'SystemUpdating'|'RollingBack'|'InService'|'Deleting'|'Failed'|'UpdateRollbackFailed', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **SortBy** (*string*) -- Sorts the list of results. The default is "CreationTime". * **SortOrder** (*string*) -- The sort order for results. The default is "Descending". * **NameContains** (*string*) -- A string in endpoint names. This filter returns only endpoints whose name contains the specified string. * **CreationTimeBefore** (*datetime*) -- A filter that returns only endpoints that were created before the specified time (timestamp). * **CreationTimeAfter** (*datetime*) -- A filter that returns only endpoints with a creation time greater than or equal to the specified time (timestamp). * **LastModifiedTimeBefore** (*datetime*) -- A filter that returns only endpoints that were modified before the specified timestamp. * **LastModifiedTimeAfter** (*datetime*) -- A filter that returns only endpoints that were modified after the specified timestamp. * **StatusEquals** (*string*) -- A filter that returns only endpoints with the specified status. * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'Endpoints': [ { 'EndpointName': 'string', 'EndpointArn': 'string', 'CreationTime': datetime(2015, 1, 1), 'LastModifiedTime': datetime(2015, 1, 1), 'EndpointStatus': 'OutOfService'|'Creating'|'Updating'|'SystemUpdating'|'RollingBack'|'InService'|'Deleting'|'Failed'|'UpdateRollbackFailed' }, ], } **Response Structure** * *(dict) --* * **Endpoints** *(list) --* An array or endpoint objects. * *(dict) --* Provides summary information for an endpoint. * **EndpointName** *(string) --* The name of the endpoint. * **EndpointArn** *(string) --* The Amazon Resource Name (ARN) of the endpoint. * **CreationTime** *(datetime) --* A timestamp that shows when the endpoint was created. * **LastModifiedTime** *(datetime) --* A timestamp that shows when the endpoint was last modified. * **EndpointStatus** *(string) --* The status of the endpoint. * "OutOfService": Endpoint is not available to take incoming requests. * "Creating": CreateEndpoint is executing. * "Updating": UpdateEndpoint or UpdateEndpointWeightsAndCapacities is executing. * "SystemUpdating": Endpoint is undergoing maintenance and cannot be updated or deleted or re- scaled until it has completed. This maintenance operation does not change any customer-specified values such as VPC config, KMS encryption, model, instance type, or instance count. * "RollingBack": Endpoint fails to scale up or down or change its variant weight and is in the process of rolling back to its previous configuration. Once the rollback completes, endpoint returns to an "InService" status. This transitional status only applies to an endpoint that has autoscaling enabled and is undergoing variant weight or capacity changes as part of an UpdateEndpointWeightsAndCapacities call or when the UpdateEndpointWeightsAndCapacities operation is called explicitly. * "InService": Endpoint is available to process incoming requests. * "Deleting": DeleteEndpoint is executing. * "Failed": Endpoint could not be created, updated, or re-scaled. Use "DescribeEndpointOutput$FailureReason" for information about the failure. DeleteEndpoint is the only operation that can be performed on a failed endpoint. To get a list of endpoints with a specified status, use the "StatusEquals" filter with a call to ListEndpoints. SageMaker / Paginator / ListDomains ListDomains *********** class SageMaker.Paginator.ListDomains paginator = client.get_paginator('list_domains') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_domains()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max- items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'Domains': [ { 'DomainArn': 'string', 'DomainId': 'string', 'DomainName': 'string', 'Status': 'Deleting'|'Failed'|'InService'|'Pending'|'Updating'|'Update_Failed'|'Delete_Failed', 'CreationTime': datetime(2015, 1, 1), 'LastModifiedTime': datetime(2015, 1, 1), 'Url': 'string' }, ], } **Response Structure** * *(dict) --* * **Domains** *(list) --* The list of domains. * *(dict) --* The domain's details. * **DomainArn** *(string) --* The domain's Amazon Resource Name (ARN). * **DomainId** *(string) --* The domain ID. * **DomainName** *(string) --* The domain name. * **Status** *(string) --* The status. * **CreationTime** *(datetime) --* The creation time. * **LastModifiedTime** *(datetime) --* The last modified time. * **Url** *(string) --* The domain's URL. SageMaker / Paginator / ListTrainingPlans ListTrainingPlans ***************** class SageMaker.Paginator.ListTrainingPlans paginator = client.get_paginator('list_training_plans') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_training_plans()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( StartTimeAfter=datetime(2015, 1, 1), StartTimeBefore=datetime(2015, 1, 1), SortBy='TrainingPlanName'|'StartTime'|'Status', SortOrder='Ascending'|'Descending', Filters=[ { 'Name': 'Status', 'Value': 'string' }, ], PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **StartTimeAfter** (*datetime*) -- Filter to list only training plans with an actual start time after this date. * **StartTimeBefore** (*datetime*) -- Filter to list only training plans with an actual start time before this date. * **SortBy** (*string*) -- The training plan field to sort the results by (e.g., StartTime, Status). * **SortOrder** (*string*) -- The order to sort the results (Ascending or Descending). * **Filters** (*list*) -- Additional filters to apply to the list of training plans. * *(dict) --* A filter to apply when listing or searching for training plans. For more information about how to reserve GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see >>``<>``<<. * **Name** *(string) --* **[REQUIRED]** The name of the filter field (e.g., Status, InstanceType). * **Value** *(string) --* **[REQUIRED]** The value to filter by for the specified field. * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'TrainingPlanSummaries': [ { 'TrainingPlanArn': 'string', 'TrainingPlanName': 'string', 'Status': 'Pending'|'Active'|'Scheduled'|'Expired'|'Failed', 'StatusMessage': 'string', 'DurationHours': 123, 'DurationMinutes': 123, 'StartTime': datetime(2015, 1, 1), 'EndTime': datetime(2015, 1, 1), 'UpfrontFee': 'string', 'CurrencyCode': 'string', 'TotalInstanceCount': 123, 'AvailableInstanceCount': 123, 'InUseInstanceCount': 123, 'TotalUltraServerCount': 123, 'TargetResources': [ 'training-job'|'hyperpod-cluster', ], 'ReservedCapacitySummaries': [ { 'ReservedCapacityArn': 'string', 'ReservedCapacityType': 'UltraServer'|'Instance', 'UltraServerType': 'string', 'UltraServerCount': 123, 'InstanceType': 'ml.p4d.24xlarge'|'ml.p5.48xlarge'|'ml.p5e.48xlarge'|'ml.p5en.48xlarge'|'ml.trn1.32xlarge'|'ml.trn2.48xlarge'|'ml.p6-b200.48xlarge'|'ml.p4de.24xlarge'|'ml.p6e-gb200.36xlarge', 'TotalInstanceCount': 123, 'Status': 'Pending'|'Active'|'Scheduled'|'Expired'|'Failed', 'AvailabilityZone': 'string', 'DurationHours': 123, 'DurationMinutes': 123, 'StartTime': datetime(2015, 1, 1), 'EndTime': datetime(2015, 1, 1) }, ] }, ] } **Response Structure** * *(dict) --* * **TrainingPlanSummaries** *(list) --* A list of summary information for the training plans. * *(dict) --* Details of the training plan. For more information about how to reserve GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see >>``<>``<<. * **TrainingPlanArn** *(string) --* The Amazon Resource Name (ARN); of the training plan. * **TrainingPlanName** *(string) --* The name of the training plan. * **Status** *(string) --* The current status of the training plan (e.g., Pending, Active, Expired). To see the complete list of status values available for a training plan, refer to the "Status" attribute within the "TrainingPlanSummary" object. * **StatusMessage** *(string) --* A message providing additional information about the current status of the training plan. * **DurationHours** *(integer) --* The number of whole hours in the total duration for this training plan. * **DurationMinutes** *(integer) --* The additional minutes beyond whole hours in the total duration for this training plan. * **StartTime** *(datetime) --* The start time of the training plan. * **EndTime** *(datetime) --* The end time of the training plan. * **UpfrontFee** *(string) --* The upfront fee for the training plan. * **CurrencyCode** *(string) --* The currency code for the upfront fee (e.g., USD). * **TotalInstanceCount** *(integer) --* The total number of instances reserved in this training plan. * **AvailableInstanceCount** *(integer) --* The number of instances currently available for use in this training plan. * **InUseInstanceCount** *(integer) --* The number of instances currently in use from this training plan. * **TotalUltraServerCount** *(integer) --* The total number of UltraServers allocated to this training plan. * **TargetResources** *(list) --* The target resources (e.g., training jobs, HyperPod clusters) that can use this training plan. Training plans are specific to their target resource. * A training plan designed for SageMaker training jobs can only be used to schedule and run training jobs. * A training plan for HyperPod clusters can be used exclusively to provide compute resources to a cluster's instance group. * *(string) --* * **ReservedCapacitySummaries** *(list) --* A list of reserved capacities associated with this training plan, including details such as instance types, counts, and availability zones. * *(dict) --* Details of a reserved capacity for the training plan. For more information about how to reserve GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see >>``<>``<<. * **ReservedCapacityArn** *(string) --* The Amazon Resource Name (ARN); of the reserved capacity. * **ReservedCapacityType** *(string) --* The type of reserved capacity. * **UltraServerType** *(string) --* The type of UltraServer included in this reserved capacity, such as ml.u-p6e-gb200x72. * **UltraServerCount** *(integer) --* The number of UltraServers included in this reserved capacity. * **InstanceType** *(string) --* The instance type for the reserved capacity. * **TotalInstanceCount** *(integer) --* The total number of instances in the reserved capacity. * **Status** *(string) --* The current status of the reserved capacity. * **AvailabilityZone** *(string) --* The availability zone for the reserved capacity. * **DurationHours** *(integer) --* The number of whole hours in the total duration for this reserved capacity. * **DurationMinutes** *(integer) --* The additional minutes beyond whole hours in the total duration for this reserved capacity. * **StartTime** *(datetime) --* The start time of the reserved capacity. * **EndTime** *(datetime) --* The end time of the reserved capacity. SageMaker / Paginator / ListEdgeDeploymentPlans ListEdgeDeploymentPlans *********************** class SageMaker.Paginator.ListEdgeDeploymentPlans paginator = client.get_paginator('list_edge_deployment_plans') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_edge_deployment_plans()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( CreationTimeAfter=datetime(2015, 1, 1), CreationTimeBefore=datetime(2015, 1, 1), LastModifiedTimeAfter=datetime(2015, 1, 1), LastModifiedTimeBefore=datetime(2015, 1, 1), NameContains='string', DeviceFleetNameContains='string', SortBy='NAME'|'DEVICE_FLEET_NAME'|'CREATION_TIME'|'LAST_MODIFIED_TIME', SortOrder='Ascending'|'Descending', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **CreationTimeAfter** (*datetime*) -- Selects edge deployment plans created after this time. * **CreationTimeBefore** (*datetime*) -- Selects edge deployment plans created before this time. * **LastModifiedTimeAfter** (*datetime*) -- Selects edge deployment plans that were last updated after this time. * **LastModifiedTimeBefore** (*datetime*) -- Selects edge deployment plans that were last updated before this time. * **NameContains** (*string*) -- Selects edge deployment plans with names containing this name. * **DeviceFleetNameContains** (*string*) -- Selects edge deployment plans with a device fleet name containing this name. * **SortBy** (*string*) -- The column by which to sort the edge deployment plans. Can be one of "NAME", "DEVICEFLEETNAME", "CREATIONTIME", "LASTMODIFIEDTIME". * **SortOrder** (*string*) -- The direction of the sorting (ascending or descending). * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'EdgeDeploymentPlanSummaries': [ { 'EdgeDeploymentPlanArn': 'string', 'EdgeDeploymentPlanName': 'string', 'DeviceFleetName': 'string', 'EdgeDeploymentSuccess': 123, 'EdgeDeploymentPending': 123, 'EdgeDeploymentFailed': 123, 'CreationTime': datetime(2015, 1, 1), 'LastModifiedTime': datetime(2015, 1, 1) }, ], } **Response Structure** * *(dict) --* * **EdgeDeploymentPlanSummaries** *(list) --* List of summaries of edge deployment plans. * *(dict) --* Contains information summarizing an edge deployment plan. * **EdgeDeploymentPlanArn** *(string) --* The ARN of the edge deployment plan. * **EdgeDeploymentPlanName** *(string) --* The name of the edge deployment plan. * **DeviceFleetName** *(string) --* The name of the device fleet used for the deployment. * **EdgeDeploymentSuccess** *(integer) --* The number of edge devices with the successful deployment. * **EdgeDeploymentPending** *(integer) --* The number of edge devices yet to pick up the deployment, or in progress. * **EdgeDeploymentFailed** *(integer) --* The number of edge devices that failed the deployment. * **CreationTime** *(datetime) --* The time when the edge deployment plan was created. * **LastModifiedTime** *(datetime) --* The time when the edge deployment plan was last updated. SageMaker / Paginator / ListAlgorithms ListAlgorithms ************** class SageMaker.Paginator.ListAlgorithms paginator = client.get_paginator('list_algorithms') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_algorithms()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( CreationTimeAfter=datetime(2015, 1, 1), CreationTimeBefore=datetime(2015, 1, 1), NameContains='string', SortBy='Name'|'CreationTime', SortOrder='Ascending'|'Descending', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **CreationTimeAfter** (*datetime*) -- A filter that returns only algorithms created after the specified time (timestamp). * **CreationTimeBefore** (*datetime*) -- A filter that returns only algorithms created before the specified time (timestamp). * **NameContains** (*string*) -- A string in the algorithm name. This filter returns only algorithms whose name contains the specified string. * **SortBy** (*string*) -- The parameter by which to sort the results. The default is "CreationTime". * **SortOrder** (*string*) -- The sort order for the results. The default is "Ascending". * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'AlgorithmSummaryList': [ { 'AlgorithmName': 'string', 'AlgorithmArn': 'string', 'AlgorithmDescription': 'string', 'CreationTime': datetime(2015, 1, 1), 'AlgorithmStatus': 'Pending'|'InProgress'|'Completed'|'Failed'|'Deleting' }, ], } **Response Structure** * *(dict) --* * **AlgorithmSummaryList** *(list) --* >An array of "AlgorithmSummary" objects, each of which lists an algorithm. * *(dict) --* Provides summary information about an algorithm. * **AlgorithmName** *(string) --* The name of the algorithm that is described by the summary. * **AlgorithmArn** *(string) --* The Amazon Resource Name (ARN) of the algorithm. * **AlgorithmDescription** *(string) --* A brief description of the algorithm. * **CreationTime** *(datetime) --* A timestamp that shows when the algorithm was created. * **AlgorithmStatus** *(string) --* The overall status of the algorithm. SageMaker / Paginator / ListUserProfiles ListUserProfiles **************** class SageMaker.Paginator.ListUserProfiles paginator = client.get_paginator('list_user_profiles') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_user_profiles()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( SortOrder='Ascending'|'Descending', SortBy='CreationTime'|'LastModifiedTime', DomainIdEquals='string', UserProfileNameContains='string', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **SortOrder** (*string*) -- The sort order for the results. The default is Ascending. * **SortBy** (*string*) -- The parameter by which to sort the results. The default is CreationTime. * **DomainIdEquals** (*string*) -- A parameter by which to filter the results. * **UserProfileNameContains** (*string*) -- A parameter by which to filter the results. * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'UserProfiles': [ { 'DomainId': 'string', 'UserProfileName': 'string', 'Status': 'Deleting'|'Failed'|'InService'|'Pending'|'Updating'|'Update_Failed'|'Delete_Failed', 'CreationTime': datetime(2015, 1, 1), 'LastModifiedTime': datetime(2015, 1, 1) }, ], } **Response Structure** * *(dict) --* * **UserProfiles** *(list) --* The list of user profiles. * *(dict) --* The user profile details. * **DomainId** *(string) --* The domain ID. * **UserProfileName** *(string) --* The user profile name. * **Status** *(string) --* The status. * **CreationTime** *(datetime) --* The creation time. * **LastModifiedTime** *(datetime) --* The last modified time. SageMaker / Paginator / ListDeviceFleets ListDeviceFleets **************** class SageMaker.Paginator.ListDeviceFleets paginator = client.get_paginator('list_device_fleets') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_device_fleets()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( CreationTimeAfter=datetime(2015, 1, 1), CreationTimeBefore=datetime(2015, 1, 1), LastModifiedTimeAfter=datetime(2015, 1, 1), LastModifiedTimeBefore=datetime(2015, 1, 1), NameContains='string', SortBy='NAME'|'CREATION_TIME'|'LAST_MODIFIED_TIME', SortOrder='Ascending'|'Descending', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **CreationTimeAfter** (*datetime*) -- Filter fleets where packaging job was created after specified time. * **CreationTimeBefore** (*datetime*) -- Filter fleets where the edge packaging job was created before specified time. * **LastModifiedTimeAfter** (*datetime*) -- Select fleets where the job was updated after X * **LastModifiedTimeBefore** (*datetime*) -- Select fleets where the job was updated before X * **NameContains** (*string*) -- Filter for fleets containing this name in their fleet device name. * **SortBy** (*string*) -- The column to sort by. * **SortOrder** (*string*) -- What direction to sort in. * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'DeviceFleetSummaries': [ { 'DeviceFleetArn': 'string', 'DeviceFleetName': 'string', 'CreationTime': datetime(2015, 1, 1), 'LastModifiedTime': datetime(2015, 1, 1) }, ], } **Response Structure** * *(dict) --* * **DeviceFleetSummaries** *(list) --* Summary of the device fleet. * *(dict) --* Summary of the device fleet. * **DeviceFleetArn** *(string) --* Amazon Resource Name (ARN) of the device fleet. * **DeviceFleetName** *(string) --* Name of the device fleet. * **CreationTime** *(datetime) --* Timestamp of when the device fleet was created. * **LastModifiedTime** *(datetime) --* Timestamp of when the device fleet was last updated. SageMaker / Paginator / ListTrainingJobs ListTrainingJobs **************** class SageMaker.Paginator.ListTrainingJobs paginator = client.get_paginator('list_training_jobs') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_training_jobs()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( CreationTimeAfter=datetime(2015, 1, 1), CreationTimeBefore=datetime(2015, 1, 1), LastModifiedTimeAfter=datetime(2015, 1, 1), LastModifiedTimeBefore=datetime(2015, 1, 1), NameContains='string', StatusEquals='InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped', SortBy='Name'|'CreationTime'|'Status', SortOrder='Ascending'|'Descending', WarmPoolStatusEquals='Available'|'Terminated'|'Reused'|'InUse', TrainingPlanArnEquals='string', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **CreationTimeAfter** (*datetime*) -- A filter that returns only training jobs created after the specified time (timestamp). * **CreationTimeBefore** (*datetime*) -- A filter that returns only training jobs created before the specified time (timestamp). * **LastModifiedTimeAfter** (*datetime*) -- A filter that returns only training jobs modified after the specified time (timestamp). * **LastModifiedTimeBefore** (*datetime*) -- A filter that returns only training jobs modified before the specified time (timestamp). * **NameContains** (*string*) -- A string in the training job name. This filter returns only training jobs whose name contains the specified string. * **StatusEquals** (*string*) -- A filter that retrieves only training jobs with a specific status. * **SortBy** (*string*) -- The field to sort results by. The default is "CreationTime". * **SortOrder** (*string*) -- The sort order for results. The default is "Ascending". * **WarmPoolStatusEquals** (*string*) -- A filter that retrieves only training jobs with a specific warm pool status. * **TrainingPlanArnEquals** (*string*) -- The Amazon Resource Name (ARN); of the training plan to filter training jobs by. For more information about reserving GPU capacity for your SageMaker training jobs using Amazon SageMaker Training Plan, see >>``<>``<<. * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'TrainingJobSummaries': [ { 'TrainingJobName': 'string', 'TrainingJobArn': 'string', 'CreationTime': datetime(2015, 1, 1), 'TrainingEndTime': datetime(2015, 1, 1), 'LastModifiedTime': datetime(2015, 1, 1), 'TrainingJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped', 'SecondaryStatus': 'Starting'|'LaunchingMLInstances'|'PreparingTrainingStack'|'Downloading'|'DownloadingTrainingImage'|'Training'|'Uploading'|'Stopping'|'Stopped'|'MaxRuntimeExceeded'|'Completed'|'Failed'|'Interrupted'|'MaxWaitTimeExceeded'|'Updating'|'Restarting'|'Pending', 'WarmPoolStatus': { 'Status': 'Available'|'Terminated'|'Reused'|'InUse', 'ResourceRetainedBillableTimeInSeconds': 123, 'ReusedByJob': 'string' }, 'TrainingPlanArn': 'string' }, ], } **Response Structure** * *(dict) --* * **TrainingJobSummaries** *(list) --* An array of "TrainingJobSummary" objects, each listing a training job. * *(dict) --* Provides summary information about a training job. * **TrainingJobName** *(string) --* The name of the training job that you want a summary for. * **TrainingJobArn** *(string) --* The Amazon Resource Name (ARN) of the training job. * **CreationTime** *(datetime) --* A timestamp that shows when the training job was created. * **TrainingEndTime** *(datetime) --* A timestamp that shows when the training job ended. This field is set only if the training job has one of the terminal statuses ( "Completed", "Failed", or "Stopped"). * **LastModifiedTime** *(datetime) --* Timestamp when the training job was last modified. * **TrainingJobStatus** *(string) --* The status of the training job. * **SecondaryStatus** *(string) --* The secondary status of the training job. * **WarmPoolStatus** *(dict) --* The status of the warm pool associated with the training job. * **Status** *(string) --* The status of the warm pool. * "InUse": The warm pool is in use for the training job. * "Available": The warm pool is available to reuse for a matching training job. * "Reused": The warm pool moved to a matching training job for reuse. * "Terminated": The warm pool is no longer available. Warm pools are unavailable if they are terminated by a user, terminated for a patch update, or terminated for exceeding the specified "KeepAlivePeriodInSeconds". * **ResourceRetainedBillableTimeInSeconds** *(integer) --* The billable time in seconds used by the warm pool. Billable time refers to the absolute wall-clock time. Multiply "ResourceRetainedBillableTimeInSeconds" by the number of instances ( "InstanceCount") in your training cluster to get the total compute time SageMaker bills you if you run warm pool training. The formula is as follows: "ResourceRetainedBillableTimeInSeconds * InstanceCount". * **ReusedByJob** *(string) --* The name of the matching training job that reused the warm pool. * **TrainingPlanArn** *(string) --* The Amazon Resource Name (ARN); of the training plan associated with this training job. For more information about how to reserve GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see >>``<>``<<. SageMaker / Paginator / ListPartnerApps ListPartnerApps *************** class SageMaker.Paginator.ListPartnerApps paginator = client.get_paginator('list_partner_apps') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_partner_apps()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max- items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'Summaries': [ { 'Arn': 'string', 'Name': 'string', 'Type': 'lakera-guard'|'comet'|'deepchecks-llm-evaluation'|'fiddler', 'Status': 'Creating'|'Updating'|'Deleting'|'Available'|'Failed'|'UpdateFailed'|'Deleted', 'CreationTime': datetime(2015, 1, 1) }, ], } **Response Structure** * *(dict) --* * **Summaries** *(list) --* The information related to each of the SageMaker Partner AI Apps in an account. * *(dict) --* A subset of information related to a SageMaker Partner AI App. This information is used as part of the "ListPartnerApps" API response. * **Arn** *(string) --* The ARN of the SageMaker Partner AI App. * **Name** *(string) --* The name of the SageMaker Partner AI App. * **Type** *(string) --* The type of SageMaker Partner AI App to create. Must be one of the following: "lakera-guard", "comet", "deepchecks-llm-evaluation", or "fiddler". * **Status** *(string) --* The status of the SageMaker Partner AI App. * **CreationTime** *(datetime) --* The creation time of the SageMaker Partner AI App. SageMaker / Paginator / ListMlflowTrackingServers ListMlflowTrackingServers ************************* class SageMaker.Paginator.ListMlflowTrackingServers paginator = client.get_paginator('list_mlflow_tracking_servers') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_mlflow_tracking_servers()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( CreatedAfter=datetime(2015, 1, 1), CreatedBefore=datetime(2015, 1, 1), TrackingServerStatus='Creating'|'Created'|'CreateFailed'|'Updating'|'Updated'|'UpdateFailed'|'Deleting'|'DeleteFailed'|'Stopping'|'Stopped'|'StopFailed'|'Starting'|'Started'|'StartFailed'|'MaintenanceInProgress'|'MaintenanceComplete'|'MaintenanceFailed', MlflowVersion='string', SortBy='Name'|'CreationTime'|'Status', SortOrder='Ascending'|'Descending', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **CreatedAfter** (*datetime*) -- Use the "CreatedAfter" filter to only list tracking servers created after a specific date and time. Listed tracking servers are shown with a date and time such as ""2024-03-16T01:46:56+00:00"". The "CreatedAfter" parameter takes in a Unix timestamp. To convert a date and time into a Unix timestamp, see EpochConverter. * **CreatedBefore** (*datetime*) -- Use the "CreatedBefore" filter to only list tracking servers created before a specific date and time. Listed tracking servers are shown with a date and time such as ""2024-03-16T01:46:56+00:00"". The "CreatedBefore" parameter takes in a Unix timestamp. To convert a date and time into a Unix timestamp, see EpochConverter. * **TrackingServerStatus** (*string*) -- Filter for tracking servers with a specified creation status. * **MlflowVersion** (*string*) -- Filter for tracking servers using the specified MLflow version. * **SortBy** (*string*) -- Filter for trackings servers sorting by name, creation time, or creation status. * **SortOrder** (*string*) -- Change the order of the listed tracking servers. By default, tracking servers are listed in "Descending" order by creation time. To change the list order, you can specify "SortOrder" to be "Ascending". * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'TrackingServerSummaries': [ { 'TrackingServerArn': 'string', 'TrackingServerName': 'string', 'CreationTime': datetime(2015, 1, 1), 'LastModifiedTime': datetime(2015, 1, 1), 'TrackingServerStatus': 'Creating'|'Created'|'CreateFailed'|'Updating'|'Updated'|'UpdateFailed'|'Deleting'|'DeleteFailed'|'Stopping'|'Stopped'|'StopFailed'|'Starting'|'Started'|'StartFailed'|'MaintenanceInProgress'|'MaintenanceComplete'|'MaintenanceFailed', 'IsActive': 'Active'|'Inactive', 'MlflowVersion': 'string' }, ], } **Response Structure** * *(dict) --* * **TrackingServerSummaries** *(list) --* A list of tracking servers according to chosen filters. * *(dict) --* The summary of the tracking server to list. * **TrackingServerArn** *(string) --* The ARN of a listed tracking server. * **TrackingServerName** *(string) --* The name of a listed tracking server. * **CreationTime** *(datetime) --* The creation time of a listed tracking server. * **LastModifiedTime** *(datetime) --* The last modified time of a listed tracking server. * **TrackingServerStatus** *(string) --* The creation status of a listed tracking server. * **IsActive** *(string) --* The activity status of a listed tracking server. * **MlflowVersion** *(string) --* The MLflow version used for a listed tracking server. SageMaker / Paginator / ListContexts ListContexts ************ class SageMaker.Paginator.ListContexts paginator = client.get_paginator('list_contexts') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_contexts()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( SourceUri='string', ContextType='string', CreatedAfter=datetime(2015, 1, 1), CreatedBefore=datetime(2015, 1, 1), SortBy='Name'|'CreationTime', SortOrder='Ascending'|'Descending', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **SourceUri** (*string*) -- A filter that returns only contexts with the specified source URI. * **ContextType** (*string*) -- A filter that returns only contexts of the specified type. * **CreatedAfter** (*datetime*) -- A filter that returns only contexts created on or after the specified time. * **CreatedBefore** (*datetime*) -- A filter that returns only contexts created on or before the specified time. * **SortBy** (*string*) -- The property used to sort results. The default value is "CreationTime". * **SortOrder** (*string*) -- The sort order. The default value is "Descending". * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'ContextSummaries': [ { 'ContextArn': 'string', 'ContextName': 'string', 'Source': { 'SourceUri': 'string', 'SourceType': 'string', 'SourceId': 'string' }, 'ContextType': 'string', 'CreationTime': datetime(2015, 1, 1), 'LastModifiedTime': datetime(2015, 1, 1) }, ], } **Response Structure** * *(dict) --* * **ContextSummaries** *(list) --* A list of contexts and their properties. * *(dict) --* Lists a summary of the properties of a context. A context provides a logical grouping of other entities. * **ContextArn** *(string) --* The Amazon Resource Name (ARN) of the context. * **ContextName** *(string) --* The name of the context. * **Source** *(dict) --* The source of the context. * **SourceUri** *(string) --* The URI of the source. * **SourceType** *(string) --* The type of the source. * **SourceId** *(string) --* The ID of the source. * **ContextType** *(string) --* The type of the context. * **CreationTime** *(datetime) --* When the context was created. * **LastModifiedTime** *(datetime) --* When the context was last modified. SageMaker / Paginator / CreateHubContentPresignedUrls CreateHubContentPresignedUrls ***************************** class SageMaker.Paginator.CreateHubContentPresignedUrls paginator = client.get_paginator('create_hub_content_presigned_urls') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.create_hub_content_presigned_urls()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( HubName='string', HubContentType='Model'|'Notebook'|'ModelReference', HubContentName='string', HubContentVersion='string', AccessConfig={ 'AcceptEula': True|False, 'ExpectedS3Url': 'string' }, PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **HubName** (*string*) -- **[REQUIRED]** The name or Amazon Resource Name (ARN) of the hub that contains the content. For public content, use "SageMakerPublicHub". * **HubContentType** (*string*) -- **[REQUIRED]** The type of hub content to access. Valid values include "Model", "Notebook", and "ModelReference". * **HubContentName** (*string*) -- **[REQUIRED]** The name of the hub content for which to generate presigned URLs. This identifies the specific model or content within the hub. * **HubContentVersion** (*string*) -- The version of the hub content. If not specified, the latest version is used. * **AccessConfig** (*dict*) -- Configuration settings for accessing the hub content, including end-user license agreement acceptance for gated models and expected S3 URL validation. * **AcceptEula** *(boolean) --* Indicates acceptance of the End User License Agreement (EULA) for gated models. Set to true to acknowledge acceptance of the license terms required for accessing gated content. * **ExpectedS3Url** *(string) --* The expected S3 URL prefix for validation purposes. This parameter helps ensure consistency between the resolved S3 URIs and the deployment configuration, reducing potential compatibility issues. * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'AuthorizedUrlConfigs': [ { 'Url': 'string', 'LocalPath': 'string' }, ], } **Response Structure** * *(dict) --* * **AuthorizedUrlConfigs** *(list) --* An array of authorized URL configurations, each containing a presigned URL and its corresponding local file path for proper file organization during download. * *(dict) --* Contains a presigned URL and its associated local file path for downloading hub content artifacts. * **Url** *(string) --* The presigned S3 URL that provides temporary, secure access to download the file. URLs expire within 15 minutes for security purposes. * **LocalPath** *(string) --* The recommended local file path where the downloaded file should be stored to maintain proper directory structure and file organization. SageMaker / Paginator / ListClusterSchedulerConfigs ListClusterSchedulerConfigs *************************** class SageMaker.Paginator.ListClusterSchedulerConfigs paginator = client.get_paginator('list_cluster_scheduler_configs') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_cluster_scheduler_configs()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( CreatedAfter=datetime(2015, 1, 1), CreatedBefore=datetime(2015, 1, 1), NameContains='string', ClusterArn='string', Status='Creating'|'CreateFailed'|'CreateRollbackFailed'|'Created'|'Updating'|'UpdateFailed'|'UpdateRollbackFailed'|'Updated'|'Deleting'|'DeleteFailed'|'DeleteRollbackFailed'|'Deleted', SortBy='Name'|'CreationTime'|'Status', SortOrder='Ascending'|'Descending', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **CreatedAfter** (*datetime*) -- Filter for after this creation time. The input for this parameter is a Unix timestamp. To convert a date and time into a Unix timestamp, see EpochConverter. * **CreatedBefore** (*datetime*) -- Filter for before this creation time. The input for this parameter is a Unix timestamp. To convert a date and time into a Unix timestamp, see EpochConverter. * **NameContains** (*string*) -- Filter for name containing this string. * **ClusterArn** (*string*) -- Filter for ARN of the cluster. * **Status** (*string*) -- Filter for status. * **SortBy** (*string*) -- Filter for sorting the list by a given value. For example, sort by name, creation time, or status. * **SortOrder** (*string*) -- The order of the list. By default, listed in "Descending" order according to by "SortBy". To change the list order, you can specify "SortOrder" to be "Ascending". * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'ClusterSchedulerConfigSummaries': [ { 'ClusterSchedulerConfigArn': 'string', 'ClusterSchedulerConfigId': 'string', 'ClusterSchedulerConfigVersion': 123, 'Name': 'string', 'CreationTime': datetime(2015, 1, 1), 'LastModifiedTime': datetime(2015, 1, 1), 'Status': 'Creating'|'CreateFailed'|'CreateRollbackFailed'|'Created'|'Updating'|'UpdateFailed'|'UpdateRollbackFailed'|'Updated'|'Deleting'|'DeleteFailed'|'DeleteRollbackFailed'|'Deleted', 'ClusterArn': 'string' }, ], } **Response Structure** * *(dict) --* * **ClusterSchedulerConfigSummaries** *(list) --* Summaries of the cluster policies. * *(dict) --* Summary of the cluster policy. * **ClusterSchedulerConfigArn** *(string) --* ARN of the cluster policy. * **ClusterSchedulerConfigId** *(string) --* ID of the cluster policy. * **ClusterSchedulerConfigVersion** *(integer) --* Version of the cluster policy. * **Name** *(string) --* Name of the cluster policy. * **CreationTime** *(datetime) --* Creation time of the cluster policy. * **LastModifiedTime** *(datetime) --* Last modified time of the cluster policy. * **Status** *(string) --* Status of the cluster policy. * **ClusterArn** *(string) --* ARN of the cluster. SageMaker / Paginator / ListHyperParameterTuningJobs ListHyperParameterTuningJobs **************************** class SageMaker.Paginator.ListHyperParameterTuningJobs paginator = client.get_paginator('list_hyper_parameter_tuning_jobs') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_hyper_parameter_tuning_jobs()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( SortBy='Name'|'Status'|'CreationTime', SortOrder='Ascending'|'Descending', NameContains='string', CreationTimeAfter=datetime(2015, 1, 1), CreationTimeBefore=datetime(2015, 1, 1), LastModifiedTimeAfter=datetime(2015, 1, 1), LastModifiedTimeBefore=datetime(2015, 1, 1), StatusEquals='Completed'|'InProgress'|'Failed'|'Stopped'|'Stopping'|'Deleting'|'DeleteFailed', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **SortBy** (*string*) -- The field to sort results by. The default is "Name". * **SortOrder** (*string*) -- The sort order for results. The default is "Ascending". * **NameContains** (*string*) -- A string in the tuning job name. This filter returns only tuning jobs whose name contains the specified string. * **CreationTimeAfter** (*datetime*) -- A filter that returns only tuning jobs that were created after the specified time. * **CreationTimeBefore** (*datetime*) -- A filter that returns only tuning jobs that were created before the specified time. * **LastModifiedTimeAfter** (*datetime*) -- A filter that returns only tuning jobs that were modified after the specified time. * **LastModifiedTimeBefore** (*datetime*) -- A filter that returns only tuning jobs that were modified before the specified time. * **StatusEquals** (*string*) -- A filter that returns only tuning jobs with the specified status. * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'HyperParameterTuningJobSummaries': [ { 'HyperParameterTuningJobName': 'string', 'HyperParameterTuningJobArn': 'string', 'HyperParameterTuningJobStatus': 'Completed'|'InProgress'|'Failed'|'Stopped'|'Stopping'|'Deleting'|'DeleteFailed', 'Strategy': 'Bayesian'|'Random'|'Hyperband'|'Grid', 'CreationTime': datetime(2015, 1, 1), 'HyperParameterTuningEndTime': datetime(2015, 1, 1), 'LastModifiedTime': datetime(2015, 1, 1), 'TrainingJobStatusCounters': { 'Completed': 123, 'InProgress': 123, 'RetryableError': 123, 'NonRetryableError': 123, 'Stopped': 123 }, 'ObjectiveStatusCounters': { 'Succeeded': 123, 'Pending': 123, 'Failed': 123 }, 'ResourceLimits': { 'MaxNumberOfTrainingJobs': 123, 'MaxParallelTrainingJobs': 123, 'MaxRuntimeInSeconds': 123 } }, ], } **Response Structure** * *(dict) --* * **HyperParameterTuningJobSummaries** *(list) --* A list of HyperParameterTuningJobSummary objects that describe the tuning jobs that the "ListHyperParameterTuningJobs" request returned. * *(dict) --* Provides summary information about a hyperparameter tuning job. * **HyperParameterTuningJobName** *(string) --* The name of the tuning job. * **HyperParameterTuningJobArn** *(string) --* The Amazon Resource Name (ARN) of the tuning job. * **HyperParameterTuningJobStatus** *(string) --* The status of the tuning job. * **Strategy** *(string) --* Specifies the search strategy hyperparameter tuning uses to choose which hyperparameters to evaluate at each iteration. * **CreationTime** *(datetime) --* The date and time that the tuning job was created. * **HyperParameterTuningEndTime** *(datetime) --* The date and time that the tuning job ended. * **LastModifiedTime** *(datetime) --* The date and time that the tuning job was modified. * **TrainingJobStatusCounters** *(dict) --* The TrainingJobStatusCounters object that specifies the numbers of training jobs, categorized by status, that this tuning job launched. * **Completed** *(integer) --* The number of completed training jobs launched by the hyperparameter tuning job. * **InProgress** *(integer) --* The number of in-progress training jobs launched by a hyperparameter tuning job. * **RetryableError** *(integer) --* The number of training jobs that failed, but can be retried. A failed training job can be retried only if it failed because an internal service error occurred. * **NonRetryableError** *(integer) --* The number of training jobs that failed and can't be retried. A failed training job can't be retried if it failed because a client error occurred. * **Stopped** *(integer) --* The number of training jobs launched by a hyperparameter tuning job that were manually stopped. * **ObjectiveStatusCounters** *(dict) --* The ObjectiveStatusCounters object that specifies the numbers of training jobs, categorized by objective metric status, that this tuning job launched. * **Succeeded** *(integer) --* The number of training jobs whose final objective metric was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process. * **Pending** *(integer) --* The number of training jobs that are in progress and pending evaluation of their final objective metric. * **Failed** *(integer) --* The number of training jobs whose final objective metric was not evaluated and used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric. * **ResourceLimits** *(dict) --* The ResourceLimits object that specifies the maximum number of training jobs and parallel training jobs allowed for this tuning job. * **MaxNumberOfTrainingJobs** *(integer) --* The maximum number of training jobs that a hyperparameter tuning job can launch. * **MaxParallelTrainingJobs** *(integer) --* The maximum number of concurrent training jobs that a hyperparameter tuning job can launch. * **MaxRuntimeInSeconds** *(integer) --* The maximum time in seconds that a hyperparameter tuning job can run. SageMaker / Paginator / ListClusterNodes ListClusterNodes **************** class SageMaker.Paginator.ListClusterNodes paginator = client.get_paginator('list_cluster_nodes') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_cluster_nodes()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( ClusterName='string', CreationTimeAfter=datetime(2015, 1, 1), CreationTimeBefore=datetime(2015, 1, 1), InstanceGroupNameContains='string', SortBy='CREATION_TIME'|'NAME', SortOrder='Ascending'|'Descending', IncludeNodeLogicalIds=True|False, PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **ClusterName** (*string*) -- **[REQUIRED]** The string name or the Amazon Resource Name (ARN) of the SageMaker HyperPod cluster in which you want to retrieve the list of nodes. * **CreationTimeAfter** (*datetime*) -- A filter that returns nodes in a SageMaker HyperPod cluster created after the specified time. Timestamps are formatted according to the ISO 8601 standard. Acceptable formats include: * "YYYY-MM-DDThh:mm:ss.sssTZD" (UTC), for example, "2014-10-01T20:30:00.000Z" * "YYYY-MM-DDThh:mm:ss.sssTZD" (with offset), for example, "2014-10-01T12:30:00.000-08:00" * "YYYY-MM-DD", for example, "2014-10-01" * Unix time in seconds, for example, "1412195400". This is also referred to as Unix Epoch time and represents the number of seconds since midnight, January 1, 1970 UTC. For more information about the timestamp format, see Timestamp in the *Amazon Web Services Command Line Interface User Guide*. * **CreationTimeBefore** (*datetime*) -- A filter that returns nodes in a SageMaker HyperPod cluster created before the specified time. The acceptable formats are the same as the timestamp formats for "CreationTimeAfter". For more information about the timestamp format, see Timestamp in the *Amazon Web Services Command Line Interface User Guide*. * **InstanceGroupNameContains** (*string*) -- A filter that returns the instance groups whose name contain a specified string. * **SortBy** (*string*) -- The field by which to sort results. The default value is "CREATION_TIME". * **SortOrder** (*string*) -- The sort order for results. The default value is "Ascending". * **IncludeNodeLogicalIds** (*boolean*) -- Specifies whether to include nodes that are still being provisioned in the response. When set to true, the response includes all nodes regardless of their provisioning status. When set to "False" (default), only nodes with assigned "InstanceIds" are returned. * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'ClusterNodeSummaries': [ { 'InstanceGroupName': 'string', 'InstanceId': 'string', 'NodeLogicalId': 'string', 'InstanceType': 'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.c5n.large'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.16xlarge'|'ml.g6.12xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.gr6.4xlarge'|'ml.gr6.8xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.16xlarge'|'ml.g6e.12xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.p5e.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.trn2.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.i3en.large'|'ml.i3en.xlarge'|'ml.i3en.2xlarge'|'ml.i3en.3xlarge'|'ml.i3en.6xlarge'|'ml.i3en.12xlarge'|'ml.i3en.24xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge', 'LaunchTime': datetime(2015, 1, 1), 'LastSoftwareUpdateTime': datetime(2015, 1, 1), 'InstanceStatus': { 'Status': 'Running'|'Failure'|'Pending'|'ShuttingDown'|'SystemUpdating'|'DeepHealthCheckInProgress'|'NotFound', 'Message': 'string' }, 'UltraServerInfo': { 'Id': 'string' } }, ] } **Response Structure** * *(dict) --* * **ClusterNodeSummaries** *(list) --* The summaries of listed instances in a SageMaker HyperPod cluster * *(dict) --* Lists a summary of the properties of an instance (also called a *node* interchangeably) of a SageMaker HyperPod cluster. * **InstanceGroupName** *(string) --* The name of the instance group in which the instance is. * **InstanceId** *(string) --* The ID of the instance. * **NodeLogicalId** *(string) --* A unique identifier for the node that persists throughout its lifecycle, from provisioning request to termination. This identifier can be used to track the node even before it has an assigned "InstanceId". This field is only included when "IncludeNodeLogicalIds" is set to "True" in the "ListClusterNodes" request. * **InstanceType** *(string) --* The type of the instance. * **LaunchTime** *(datetime) --* The time when the instance is launched. * **LastSoftwareUpdateTime** *(datetime) --* The time when SageMaker last updated the software of the instances in the cluster. * **InstanceStatus** *(dict) --* The status of the instance. * **Status** *(string) --* The status of an instance in a SageMaker HyperPod cluster. * **Message** *(string) --* The message from an instance in a SageMaker HyperPod cluster. * **UltraServerInfo** *(dict) --* Contains information about the UltraServer. * **Id** *(string) --* The unique identifier of the UltraServer. SageMaker / Paginator / ListPipelineExecutions ListPipelineExecutions ********************** class SageMaker.Paginator.ListPipelineExecutions paginator = client.get_paginator('list_pipeline_executions') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_pipeline_executions()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( PipelineName='string', CreatedAfter=datetime(2015, 1, 1), CreatedBefore=datetime(2015, 1, 1), SortBy='CreationTime'|'PipelineExecutionArn', SortOrder='Ascending'|'Descending', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **PipelineName** (*string*) -- **[REQUIRED]** The name or Amazon Resource Name (ARN) of the pipeline. * **CreatedAfter** (*datetime*) -- A filter that returns the pipeline executions that were created after a specified time. * **CreatedBefore** (*datetime*) -- A filter that returns the pipeline executions that were created before a specified time. * **SortBy** (*string*) -- The field by which to sort results. The default is "CreatedTime". * **SortOrder** (*string*) -- The sort order for results. * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'PipelineExecutionSummaries': [ { 'PipelineExecutionArn': 'string', 'StartTime': datetime(2015, 1, 1), 'PipelineExecutionStatus': 'Executing'|'Stopping'|'Stopped'|'Failed'|'Succeeded', 'PipelineExecutionDescription': 'string', 'PipelineExecutionDisplayName': 'string', 'PipelineExecutionFailureReason': 'string' }, ], } **Response Structure** * *(dict) --* * **PipelineExecutionSummaries** *(list) --* Contains a sorted list of pipeline execution summary objects matching the specified filters. Each run summary includes the Amazon Resource Name (ARN) of the pipeline execution, the run date, and the status. This list can be empty. * *(dict) --* A pipeline execution summary. * **PipelineExecutionArn** *(string) --* The Amazon Resource Name (ARN) of the pipeline execution. * **StartTime** *(datetime) --* The start time of the pipeline execution. * **PipelineExecutionStatus** *(string) --* The status of the pipeline execution. * **PipelineExecutionDescription** *(string) --* The description of the pipeline execution. * **PipelineExecutionDisplayName** *(string) --* The display name of the pipeline execution. * **PipelineExecutionFailureReason** *(string) --* A message generated by SageMaker Pipelines describing why the pipeline execution failed. SageMaker / Paginator / ListFeatureGroups ListFeatureGroups ***************** class SageMaker.Paginator.ListFeatureGroups paginator = client.get_paginator('list_feature_groups') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_feature_groups()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( NameContains='string', FeatureGroupStatusEquals='Creating'|'Created'|'CreateFailed'|'Deleting'|'DeleteFailed', OfflineStoreStatusEquals='Active'|'Blocked'|'Disabled', CreationTimeAfter=datetime(2015, 1, 1), CreationTimeBefore=datetime(2015, 1, 1), SortOrder='Ascending'|'Descending', SortBy='Name'|'FeatureGroupStatus'|'OfflineStoreStatus'|'CreationTime', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **NameContains** (*string*) -- A string that partially matches one or more >>``<>``<>``<>``<>``<>``<<. * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'ClusterSummaries': [ { 'ClusterArn': 'string', 'ClusterName': 'string', 'CreationTime': datetime(2015, 1, 1), 'ClusterStatus': 'Creating'|'Deleting'|'Failed'|'InService'|'RollingBack'|'SystemUpdating'|'Updating', 'TrainingPlanArns': [ 'string', ] }, ] } **Response Structure** * *(dict) --* * **ClusterSummaries** *(list) --* The summaries of listed SageMaker HyperPod clusters. * *(dict) --* Lists a summary of the properties of a SageMaker HyperPod cluster. * **ClusterArn** *(string) --* The Amazon Resource Name (ARN) of the SageMaker HyperPod cluster. * **ClusterName** *(string) --* The name of the SageMaker HyperPod cluster. * **CreationTime** *(datetime) --* The time when the SageMaker HyperPod cluster is created. * **ClusterStatus** *(string) --* The status of the SageMaker HyperPod cluster. * **TrainingPlanArns** *(list) --* A list of Amazon Resource Names (ARNs) of the training plans associated with this cluster. For more information about how to reserve GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see >>``<>``<<. * *(string) --* SageMaker / Paginator / ListMonitoringExecutions ListMonitoringExecutions ************************ class SageMaker.Paginator.ListMonitoringExecutions paginator = client.get_paginator('list_monitoring_executions') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_monitoring_executions()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( MonitoringScheduleName='string', EndpointName='string', SortBy='CreationTime'|'ScheduledTime'|'Status', SortOrder='Ascending'|'Descending', ScheduledTimeBefore=datetime(2015, 1, 1), ScheduledTimeAfter=datetime(2015, 1, 1), CreationTimeBefore=datetime(2015, 1, 1), CreationTimeAfter=datetime(2015, 1, 1), LastModifiedTimeBefore=datetime(2015, 1, 1), LastModifiedTimeAfter=datetime(2015, 1, 1), StatusEquals='Pending'|'Completed'|'CompletedWithViolations'|'InProgress'|'Failed'|'Stopping'|'Stopped', MonitoringJobDefinitionName='string', MonitoringTypeEquals='DataQuality'|'ModelQuality'|'ModelBias'|'ModelExplainability', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **MonitoringScheduleName** (*string*) -- Name of a specific schedule to fetch jobs for. * **EndpointName** (*string*) -- Name of a specific endpoint to fetch jobs for. * **SortBy** (*string*) -- Whether to sort the results by the "Status", "CreationTime", or "ScheduledTime" field. The default is "CreationTime". * **SortOrder** (*string*) -- Whether to sort the results in "Ascending" or "Descending" order. The default is "Descending". * **ScheduledTimeBefore** (*datetime*) -- Filter for jobs scheduled before a specified time. * **ScheduledTimeAfter** (*datetime*) -- Filter for jobs scheduled after a specified time. * **CreationTimeBefore** (*datetime*) -- A filter that returns only jobs created before a specified time. * **CreationTimeAfter** (*datetime*) -- A filter that returns only jobs created after a specified time. * **LastModifiedTimeBefore** (*datetime*) -- A filter that returns only jobs modified after a specified time. * **LastModifiedTimeAfter** (*datetime*) -- A filter that returns only jobs modified before a specified time. * **StatusEquals** (*string*) -- A filter that retrieves only jobs with a specific status. * **MonitoringJobDefinitionName** (*string*) -- Gets a list of the monitoring job runs of the specified monitoring job definitions. * **MonitoringTypeEquals** (*string*) -- A filter that returns only the monitoring job runs of the specified monitoring type. * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'MonitoringExecutionSummaries': [ { 'MonitoringScheduleName': 'string', 'ScheduledTime': datetime(2015, 1, 1), 'CreationTime': datetime(2015, 1, 1), 'LastModifiedTime': datetime(2015, 1, 1), 'MonitoringExecutionStatus': 'Pending'|'Completed'|'CompletedWithViolations'|'InProgress'|'Failed'|'Stopping'|'Stopped', 'ProcessingJobArn': 'string', 'EndpointName': 'string', 'FailureReason': 'string', 'MonitoringJobDefinitionName': 'string', 'MonitoringType': 'DataQuality'|'ModelQuality'|'ModelBias'|'ModelExplainability' }, ], } **Response Structure** * *(dict) --* * **MonitoringExecutionSummaries** *(list) --* A JSON array in which each element is a summary for a monitoring execution. * *(dict) --* Summary of information about the last monitoring job to run. * **MonitoringScheduleName** *(string) --* The name of the monitoring schedule. * **ScheduledTime** *(datetime) --* The time the monitoring job was scheduled. * **CreationTime** *(datetime) --* The time at which the monitoring job was created. * **LastModifiedTime** *(datetime) --* A timestamp that indicates the last time the monitoring job was modified. * **MonitoringExecutionStatus** *(string) --* The status of the monitoring job. * **ProcessingJobArn** *(string) --* The Amazon Resource Name (ARN) of the monitoring job. * **EndpointName** *(string) --* The name of the endpoint used to run the monitoring job. * **FailureReason** *(string) --* Contains the reason a monitoring job failed, if it failed. * **MonitoringJobDefinitionName** *(string) --* The name of the monitoring job. * **MonitoringType** *(string) --* The type of the monitoring job. SageMaker / Paginator / ListUltraServersByReservedCapacity ListUltraServersByReservedCapacity ********************************** class SageMaker.Paginator.ListUltraServersByReservedCapacity paginator = client.get_paginator('list_ultra_servers_by_reserved_capacity') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_ultra_servers_by_reserved_capacity()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( ReservedCapacityArn='string', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **ReservedCapacityArn** (*string*) -- **[REQUIRED]** The ARN of the reserved capacity to list UltraServers for. * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'UltraServers': [ { 'UltraServerId': 'string', 'UltraServerType': 'string', 'AvailabilityZone': 'string', 'InstanceType': 'ml.p4d.24xlarge'|'ml.p5.48xlarge'|'ml.p5e.48xlarge'|'ml.p5en.48xlarge'|'ml.trn1.32xlarge'|'ml.trn2.48xlarge'|'ml.p6-b200.48xlarge'|'ml.p4de.24xlarge'|'ml.p6e-gb200.36xlarge', 'TotalInstanceCount': 123, 'ConfiguredSpareInstanceCount': 123, 'AvailableInstanceCount': 123, 'InUseInstanceCount': 123, 'AvailableSpareInstanceCount': 123, 'UnhealthyInstanceCount': 123, 'HealthStatus': 'OK'|'Impaired'|'Insufficient-Data' }, ] } **Response Structure** * *(dict) --* * **UltraServers** *(list) --* A list of UltraServers that are part of the specified reserved capacity. * *(dict) --* Represents a high-performance compute server used for distributed training in SageMaker AI. An UltraServer consists of multiple instances within a shared NVLink interconnect domain. * **UltraServerId** *(string) --* The unique identifier for the UltraServer. * **UltraServerType** *(string) --* The type of UltraServer, such as ml.u-p6e-gb200x72. * **AvailabilityZone** *(string) --* The name of the Availability Zone where the UltraServer is provisioned. * **InstanceType** *(string) --* The Amazon EC2 instance type used in the UltraServer. * **TotalInstanceCount** *(integer) --* The total number of instances in this UltraServer. * **ConfiguredSpareInstanceCount** *(integer) --* The number of spare instances configured for this UltraServer to provide enhanced resiliency. * **AvailableInstanceCount** *(integer) --* The number of instances currently available for use in this UltraServer. * **InUseInstanceCount** *(integer) --* The number of instances currently in use in this UltraServer. * **AvailableSpareInstanceCount** *(integer) --* The number of available spare instances in the UltraServer. * **UnhealthyInstanceCount** *(integer) --* The number of instances in this UltraServer that are currently in an unhealthy state. * **HealthStatus** *(string) --* The overall health status of the UltraServer. SageMaker / Paginator / ListMonitoringSchedules ListMonitoringSchedules *********************** class SageMaker.Paginator.ListMonitoringSchedules paginator = client.get_paginator('list_monitoring_schedules') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_monitoring_schedules()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( EndpointName='string', SortBy='Name'|'CreationTime'|'Status', SortOrder='Ascending'|'Descending', NameContains='string', CreationTimeBefore=datetime(2015, 1, 1), CreationTimeAfter=datetime(2015, 1, 1), LastModifiedTimeBefore=datetime(2015, 1, 1), LastModifiedTimeAfter=datetime(2015, 1, 1), StatusEquals='Pending'|'Failed'|'Scheduled'|'Stopped', MonitoringJobDefinitionName='string', MonitoringTypeEquals='DataQuality'|'ModelQuality'|'ModelBias'|'ModelExplainability', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **EndpointName** (*string*) -- Name of a specific endpoint to fetch schedules for. * **SortBy** (*string*) -- Whether to sort the results by the "Status", "CreationTime", or "ScheduledTime" field. The default is "CreationTime". * **SortOrder** (*string*) -- Whether to sort the results in "Ascending" or "Descending" order. The default is "Descending". * **NameContains** (*string*) -- Filter for monitoring schedules whose name contains a specified string. * **CreationTimeBefore** (*datetime*) -- A filter that returns only monitoring schedules created before a specified time. * **CreationTimeAfter** (*datetime*) -- A filter that returns only monitoring schedules created after a specified time. * **LastModifiedTimeBefore** (*datetime*) -- A filter that returns only monitoring schedules modified before a specified time. * **LastModifiedTimeAfter** (*datetime*) -- A filter that returns only monitoring schedules modified after a specified time. * **StatusEquals** (*string*) -- A filter that returns only monitoring schedules modified before a specified time. * **MonitoringJobDefinitionName** (*string*) -- Gets a list of the monitoring schedules for the specified monitoring job definition. * **MonitoringTypeEquals** (*string*) -- A filter that returns only the monitoring schedules for the specified monitoring type. * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'MonitoringScheduleSummaries': [ { 'MonitoringScheduleName': 'string', 'MonitoringScheduleArn': 'string', 'CreationTime': datetime(2015, 1, 1), 'LastModifiedTime': datetime(2015, 1, 1), 'MonitoringScheduleStatus': 'Pending'|'Failed'|'Scheduled'|'Stopped', 'EndpointName': 'string', 'MonitoringJobDefinitionName': 'string', 'MonitoringType': 'DataQuality'|'ModelQuality'|'ModelBias'|'ModelExplainability' }, ], } **Response Structure** * *(dict) --* * **MonitoringScheduleSummaries** *(list) --* A JSON array in which each element is a summary for a monitoring schedule. * *(dict) --* Summarizes the monitoring schedule. * **MonitoringScheduleName** *(string) --* The name of the monitoring schedule. * **MonitoringScheduleArn** *(string) --* The Amazon Resource Name (ARN) of the monitoring schedule. * **CreationTime** *(datetime) --* The creation time of the monitoring schedule. * **LastModifiedTime** *(datetime) --* The last time the monitoring schedule was modified. * **MonitoringScheduleStatus** *(string) --* The status of the monitoring schedule. * **EndpointName** *(string) --* The name of the endpoint using the monitoring schedule. * **MonitoringJobDefinitionName** *(string) --* The name of the monitoring job definition that the schedule is for. * **MonitoringType** *(string) --* The type of the monitoring job definition that the schedule is for. SageMaker / Paginator / ListTrials ListTrials ********** class SageMaker.Paginator.ListTrials paginator = client.get_paginator('list_trials') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_trials()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( ExperimentName='string', TrialComponentName='string', CreatedAfter=datetime(2015, 1, 1), CreatedBefore=datetime(2015, 1, 1), SortBy='Name'|'CreationTime', SortOrder='Ascending'|'Descending', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **ExperimentName** (*string*) -- A filter that returns only trials that are part of the specified experiment. * **TrialComponentName** (*string*) -- A filter that returns only trials that are associated with the specified trial component. * **CreatedAfter** (*datetime*) -- A filter that returns only trials created after the specified time. * **CreatedBefore** (*datetime*) -- A filter that returns only trials created before the specified time. * **SortBy** (*string*) -- The property used to sort results. The default value is "CreationTime". * **SortOrder** (*string*) -- The sort order. The default value is "Descending". * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'TrialSummaries': [ { 'TrialArn': 'string', 'TrialName': 'string', 'DisplayName': 'string', 'TrialSource': { 'SourceArn': 'string', 'SourceType': 'string' }, 'CreationTime': datetime(2015, 1, 1), 'LastModifiedTime': datetime(2015, 1, 1) }, ], } **Response Structure** * *(dict) --* * **TrialSummaries** *(list) --* A list of the summaries of your trials. * *(dict) --* A summary of the properties of a trial. To get the complete set of properties, call the DescribeTrial API and provide the "TrialName". * **TrialArn** *(string) --* The Amazon Resource Name (ARN) of the trial. * **TrialName** *(string) --* The name of the trial. * **DisplayName** *(string) --* The name of the trial as displayed. If "DisplayName" isn't specified, "TrialName" is displayed. * **TrialSource** *(dict) --* The source of the trial. * **SourceArn** *(string) --* The Amazon Resource Name (ARN) of the source. * **SourceType** *(string) --* The source job type. * **CreationTime** *(datetime) --* When the trial was created. * **LastModifiedTime** *(datetime) --* When the trial was last modified. SageMaker / Paginator / ListModelCards ListModelCards ************** class SageMaker.Paginator.ListModelCards paginator = client.get_paginator('list_model_cards') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_model_cards()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( CreationTimeAfter=datetime(2015, 1, 1), CreationTimeBefore=datetime(2015, 1, 1), NameContains='string', ModelCardStatus='Draft'|'PendingReview'|'Approved'|'Archived', SortBy='Name'|'CreationTime', SortOrder='Ascending'|'Descending', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **CreationTimeAfter** (*datetime*) -- Only list model cards that were created after the time specified. * **CreationTimeBefore** (*datetime*) -- Only list model cards that were created before the time specified. * **NameContains** (*string*) -- Only list model cards with names that contain the specified string. * **ModelCardStatus** (*string*) -- Only list model cards with the specified approval status. * **SortBy** (*string*) -- Sort model cards by either name or creation time. Sorts by creation time by default. * **SortOrder** (*string*) -- Sort model cards by ascending or descending order. * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'ModelCardSummaries': [ { 'ModelCardName': 'string', 'ModelCardArn': 'string', 'ModelCardStatus': 'Draft'|'PendingReview'|'Approved'|'Archived', 'CreationTime': datetime(2015, 1, 1), 'LastModifiedTime': datetime(2015, 1, 1) }, ], } **Response Structure** * *(dict) --* * **ModelCardSummaries** *(list) --* The summaries of the listed model cards. * *(dict) --* A summary of the model card. * **ModelCardName** *(string) --* The name of the model card. * **ModelCardArn** *(string) --* The Amazon Resource Name (ARN) of the model card. * **ModelCardStatus** *(string) --* The approval status of the model card within your organization. Different organizations might have different criteria for model card review and approval. * "Draft": The model card is a work in progress. * "PendingReview": The model card is pending review. * "Approved": The model card is approved. * "Archived": The model card is archived. No more updates should be made to the model card, but it can still be exported. * **CreationTime** *(datetime) --* The date and time that the model card was created. * **LastModifiedTime** *(datetime) --* The date and time that the model card was last modified. SageMaker / Paginator / ListArtifacts ListArtifacts ************* class SageMaker.Paginator.ListArtifacts paginator = client.get_paginator('list_artifacts') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_artifacts()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( SourceUri='string', ArtifactType='string', CreatedAfter=datetime(2015, 1, 1), CreatedBefore=datetime(2015, 1, 1), SortBy='CreationTime', SortOrder='Ascending'|'Descending', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **SourceUri** (*string*) -- A filter that returns only artifacts with the specified source URI. * **ArtifactType** (*string*) -- A filter that returns only artifacts of the specified type. * **CreatedAfter** (*datetime*) -- A filter that returns only artifacts created on or after the specified time. * **CreatedBefore** (*datetime*) -- A filter that returns only artifacts created on or before the specified time. * **SortBy** (*string*) -- The property used to sort results. The default value is "CreationTime". * **SortOrder** (*string*) -- The sort order. The default value is "Descending". * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'ArtifactSummaries': [ { 'ArtifactArn': 'string', 'ArtifactName': 'string', 'Source': { 'SourceUri': 'string', 'SourceTypes': [ { 'SourceIdType': 'MD5Hash'|'S3ETag'|'S3Version'|'Custom', 'Value': 'string' }, ] }, 'ArtifactType': 'string', 'CreationTime': datetime(2015, 1, 1), 'LastModifiedTime': datetime(2015, 1, 1) }, ], } **Response Structure** * *(dict) --* * **ArtifactSummaries** *(list) --* A list of artifacts and their properties. * *(dict) --* Lists a summary of the properties of an artifact. An artifact represents a URI addressable object or data. Some examples are a dataset and a model. * **ArtifactArn** *(string) --* The Amazon Resource Name (ARN) of the artifact. * **ArtifactName** *(string) --* The name of the artifact. * **Source** *(dict) --* The source of the artifact. * **SourceUri** *(string) --* The URI of the source. * **SourceTypes** *(list) --* A list of source types. * *(dict) --* The ID and ID type of an artifact source. * **SourceIdType** *(string) --* The type of ID. * **Value** *(string) --* The ID. * **ArtifactType** *(string) --* The type of the artifact. * **CreationTime** *(datetime) --* When the artifact was created. * **LastModifiedTime** *(datetime) --* When the artifact was last modified. SageMaker / Paginator / ListAliases ListAliases *********** class SageMaker.Paginator.ListAliases paginator = client.get_paginator('list_aliases') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_aliases()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( ImageName='string', Alias='string', Version=123, PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **ImageName** (*string*) -- **[REQUIRED]** The name of the image. * **Alias** (*string*) -- The alias of the image version. * **Version** (*integer*) -- The version of the image. If image version is not specified, the aliases of all versions of the image are listed. * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'SageMakerImageVersionAliases': [ 'string', ], } **Response Structure** * *(dict) --* * **SageMakerImageVersionAliases** *(list) --* A list of SageMaker AI image version aliases. * *(string) --* SageMaker / Paginator / ListAppImageConfigs ListAppImageConfigs ******************* class SageMaker.Paginator.ListAppImageConfigs paginator = client.get_paginator('list_app_image_configs') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_app_image_configs()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( NameContains='string', CreationTimeBefore=datetime(2015, 1, 1), CreationTimeAfter=datetime(2015, 1, 1), ModifiedTimeBefore=datetime(2015, 1, 1), ModifiedTimeAfter=datetime(2015, 1, 1), SortBy='CreationTime'|'LastModifiedTime'|'Name', SortOrder='Ascending'|'Descending', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **NameContains** (*string*) -- A filter that returns only AppImageConfigs whose name contains the specified string. * **CreationTimeBefore** (*datetime*) -- A filter that returns only AppImageConfigs created on or before the specified time. * **CreationTimeAfter** (*datetime*) -- A filter that returns only AppImageConfigs created on or after the specified time. * **ModifiedTimeBefore** (*datetime*) -- A filter that returns only AppImageConfigs modified on or before the specified time. * **ModifiedTimeAfter** (*datetime*) -- A filter that returns only AppImageConfigs modified on or after the specified time. * **SortBy** (*string*) -- The property used to sort results. The default value is "CreationTime". * **SortOrder** (*string*) -- The sort order. The default value is "Descending". * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'AppImageConfigs': [ { 'AppImageConfigArn': 'string', 'AppImageConfigName': 'string', 'CreationTime': datetime(2015, 1, 1), 'LastModifiedTime': datetime(2015, 1, 1), 'KernelGatewayImageConfig': { 'KernelSpecs': [ { 'Name': 'string', 'DisplayName': 'string' }, ], 'FileSystemConfig': { 'MountPath': 'string', 'DefaultUid': 123, 'DefaultGid': 123 } }, 'JupyterLabAppImageConfig': { 'FileSystemConfig': { 'MountPath': 'string', 'DefaultUid': 123, 'DefaultGid': 123 }, 'ContainerConfig': { 'ContainerArguments': [ 'string', ], 'ContainerEntrypoint': [ 'string', ], 'ContainerEnvironmentVariables': { 'string': 'string' } } }, 'CodeEditorAppImageConfig': { 'FileSystemConfig': { 'MountPath': 'string', 'DefaultUid': 123, 'DefaultGid': 123 }, 'ContainerConfig': { 'ContainerArguments': [ 'string', ], 'ContainerEntrypoint': [ 'string', ], 'ContainerEnvironmentVariables': { 'string': 'string' } } } }, ] } **Response Structure** * *(dict) --* * **AppImageConfigs** *(list) --* A list of AppImageConfigs and their properties. * *(dict) --* The configuration for running a SageMaker AI image as a KernelGateway app. * **AppImageConfigArn** *(string) --* The ARN of the AppImageConfig. * **AppImageConfigName** *(string) --* The name of the AppImageConfig. Must be unique to your account. * **CreationTime** *(datetime) --* When the AppImageConfig was created. * **LastModifiedTime** *(datetime) --* When the AppImageConfig was last modified. * **KernelGatewayImageConfig** *(dict) --* The configuration for the file system and kernels in the SageMaker AI image. * **KernelSpecs** *(list) --* The specification of the Jupyter kernels in the image. * *(dict) --* The specification of a Jupyter kernel. * **Name** *(string) --* The name of the Jupyter kernel in the image. This value is case sensitive. * **DisplayName** *(string) --* The display name of the kernel. * **FileSystemConfig** *(dict) --* The Amazon Elastic File System storage configuration for a SageMaker AI image. * **MountPath** *(string) --* The path within the image to mount the user's EFS home directory. The directory should be empty. If not specified, defaults to */home/sagemaker- user*. * **DefaultUid** *(integer) --* The default POSIX user ID (UID). If not specified, defaults to "1000". * **DefaultGid** *(integer) --* The default POSIX group ID (GID). If not specified, defaults to "100". * **JupyterLabAppImageConfig** *(dict) --* The configuration for the file system and the runtime, such as the environment variables and entry point. * **FileSystemConfig** *(dict) --* The Amazon Elastic File System storage configuration for a SageMaker AI image. * **MountPath** *(string) --* The path within the image to mount the user's EFS home directory. The directory should be empty. If not specified, defaults to */home/sagemaker- user*. * **DefaultUid** *(integer) --* The default POSIX user ID (UID). If not specified, defaults to "1000". * **DefaultGid** *(integer) --* The default POSIX group ID (GID). If not specified, defaults to "100". * **ContainerConfig** *(dict) --* The configuration used to run the application image container. * **ContainerArguments** *(list) --* The arguments for the container when you're running the application. * *(string) --* * **ContainerEntrypoint** *(list) --* The entrypoint used to run the application in the container. * *(string) --* * **ContainerEnvironmentVariables** *(dict) --* The environment variables to set in the container * *(string) --* * *(string) --* * **CodeEditorAppImageConfig** *(dict) --* The configuration for the file system and the runtime, such as the environment variables and entry point. * **FileSystemConfig** *(dict) --* The Amazon Elastic File System storage configuration for a SageMaker AI image. * **MountPath** *(string) --* The path within the image to mount the user's EFS home directory. The directory should be empty. If not specified, defaults to */home/sagemaker- user*. * **DefaultUid** *(integer) --* The default POSIX user ID (UID). If not specified, defaults to "1000". * **DefaultGid** *(integer) --* The default POSIX group ID (GID). If not specified, defaults to "100". * **ContainerConfig** *(dict) --* The configuration used to run the application image container. * **ContainerArguments** *(list) --* The arguments for the container when you're running the application. * *(string) --* * **ContainerEntrypoint** *(list) --* The entrypoint used to run the application in the container. * *(string) --* * **ContainerEnvironmentVariables** *(dict) --* The environment variables to set in the container * *(string) --* * *(string) --* SageMaker / Paginator / ListModelBiasJobDefinitions ListModelBiasJobDefinitions *************************** class SageMaker.Paginator.ListModelBiasJobDefinitions paginator = client.get_paginator('list_model_bias_job_definitions') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_model_bias_job_definitions()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( EndpointName='string', SortBy='Name'|'CreationTime', SortOrder='Ascending'|'Descending', NameContains='string', CreationTimeBefore=datetime(2015, 1, 1), CreationTimeAfter=datetime(2015, 1, 1), PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **EndpointName** (*string*) -- Name of the endpoint to monitor for model bias. * **SortBy** (*string*) -- Whether to sort results by the "Name" or "CreationTime" field. The default is "CreationTime". * **SortOrder** (*string*) -- Whether to sort the results in "Ascending" or "Descending" order. The default is "Descending". * **NameContains** (*string*) -- Filter for model bias jobs whose name contains a specified string. * **CreationTimeBefore** (*datetime*) -- A filter that returns only model bias jobs created before a specified time. * **CreationTimeAfter** (*datetime*) -- A filter that returns only model bias jobs created after a specified time. * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'JobDefinitionSummaries': [ { 'MonitoringJobDefinitionName': 'string', 'MonitoringJobDefinitionArn': 'string', 'CreationTime': datetime(2015, 1, 1), 'EndpointName': 'string' }, ], } **Response Structure** * *(dict) --* * **JobDefinitionSummaries** *(list) --* A JSON array in which each element is a summary for a model bias jobs. * *(dict) --* Summary information about a monitoring job. * **MonitoringJobDefinitionName** *(string) --* The name of the monitoring job. * **MonitoringJobDefinitionArn** *(string) --* The Amazon Resource Name (ARN) of the monitoring job. * **CreationTime** *(datetime) --* The time that the monitoring job was created. * **EndpointName** *(string) --* The name of the endpoint that the job monitors. SageMaker / Paginator / ListPipelines ListPipelines ************* class SageMaker.Paginator.ListPipelines paginator = client.get_paginator('list_pipelines') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_pipelines()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( PipelineNamePrefix='string', CreatedAfter=datetime(2015, 1, 1), CreatedBefore=datetime(2015, 1, 1), SortBy='Name'|'CreationTime', SortOrder='Ascending'|'Descending', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **PipelineNamePrefix** (*string*) -- The prefix of the pipeline name. * **CreatedAfter** (*datetime*) -- A filter that returns the pipelines that were created after a specified time. * **CreatedBefore** (*datetime*) -- A filter that returns the pipelines that were created before a specified time. * **SortBy** (*string*) -- The field by which to sort results. The default is "CreatedTime". * **SortOrder** (*string*) -- The sort order for results. * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'PipelineSummaries': [ { 'PipelineArn': 'string', 'PipelineName': 'string', 'PipelineDisplayName': 'string', 'PipelineDescription': 'string', 'RoleArn': 'string', 'CreationTime': datetime(2015, 1, 1), 'LastModifiedTime': datetime(2015, 1, 1), 'LastExecutionTime': datetime(2015, 1, 1) }, ], } **Response Structure** * *(dict) --* * **PipelineSummaries** *(list) --* Contains a sorted list of "PipelineSummary" objects matching the specified filters. Each "PipelineSummary" consists of PipelineArn, PipelineName, ExperimentName, PipelineDescription, CreationTime, LastModifiedTime, LastRunTime, and RoleArn. This list can be empty. * *(dict) --* A summary of a pipeline. * **PipelineArn** *(string) --* The Amazon Resource Name (ARN) of the pipeline. * **PipelineName** *(string) --* The name of the pipeline. * **PipelineDisplayName** *(string) --* The display name of the pipeline. * **PipelineDescription** *(string) --* The description of the pipeline. * **RoleArn** *(string) --* The Amazon Resource Name (ARN) that the pipeline used to execute. * **CreationTime** *(datetime) --* The creation time of the pipeline. * **LastModifiedTime** *(datetime) --* The time that the pipeline was last modified. * **LastExecutionTime** *(datetime) --* The last time that a pipeline execution began. SageMaker / Paginator / ListModelQualityJobDefinitions ListModelQualityJobDefinitions ****************************** class SageMaker.Paginator.ListModelQualityJobDefinitions paginator = client.get_paginator('list_model_quality_job_definitions') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_model_quality_job_definitions()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( EndpointName='string', SortBy='Name'|'CreationTime', SortOrder='Ascending'|'Descending', NameContains='string', CreationTimeBefore=datetime(2015, 1, 1), CreationTimeAfter=datetime(2015, 1, 1), PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **EndpointName** (*string*) -- A filter that returns only model quality monitoring job definitions that are associated with the specified endpoint. * **SortBy** (*string*) -- The field to sort results by. The default is "CreationTime". * **SortOrder** (*string*) -- Whether to sort the results in "Ascending" or "Descending" order. The default is "Descending". * **NameContains** (*string*) -- A string in the transform job name. This filter returns only model quality monitoring job definitions whose name contains the specified string. * **CreationTimeBefore** (*datetime*) -- A filter that returns only model quality monitoring job definitions created before the specified time. * **CreationTimeAfter** (*datetime*) -- A filter that returns only model quality monitoring job definitions created after the specified time. * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'JobDefinitionSummaries': [ { 'MonitoringJobDefinitionName': 'string', 'MonitoringJobDefinitionArn': 'string', 'CreationTime': datetime(2015, 1, 1), 'EndpointName': 'string' }, ], } **Response Structure** * *(dict) --* * **JobDefinitionSummaries** *(list) --* A list of summaries of model quality monitoring job definitions. * *(dict) --* Summary information about a monitoring job. * **MonitoringJobDefinitionName** *(string) --* The name of the monitoring job. * **MonitoringJobDefinitionArn** *(string) --* The Amazon Resource Name (ARN) of the monitoring job. * **CreationTime** *(datetime) --* The time that the monitoring job was created. * **EndpointName** *(string) --* The name of the endpoint that the job monitors. SageMaker / Paginator / ListSubscribedWorkteams ListSubscribedWorkteams *********************** class SageMaker.Paginator.ListSubscribedWorkteams paginator = client.get_paginator('list_subscribed_workteams') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_subscribed_workteams()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( NameContains='string', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **NameContains** (*string*) -- A string in the work team name. This filter returns only work teams whose name contains the specified string. * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'SubscribedWorkteams': [ { 'WorkteamArn': 'string', 'MarketplaceTitle': 'string', 'SellerName': 'string', 'MarketplaceDescription': 'string', 'ListingId': 'string' }, ], } **Response Structure** * *(dict) --* * **SubscribedWorkteams** *(list) --* An array of "Workteam" objects, each describing a work team. * *(dict) --* Describes a work team of a vendor that does the labelling job. * **WorkteamArn** *(string) --* The Amazon Resource Name (ARN) of the vendor that you have subscribed. * **MarketplaceTitle** *(string) --* The title of the service provided by the vendor in the Amazon Marketplace. * **SellerName** *(string) --* The name of the vendor in the Amazon Marketplace. * **MarketplaceDescription** *(string) --* The description of the vendor from the Amazon Marketplace. * **ListingId** *(string) --* Marketplace product listing ID. SageMaker / Paginator / ListProcessingJobs ListProcessingJobs ****************** class SageMaker.Paginator.ListProcessingJobs paginator = client.get_paginator('list_processing_jobs') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_processing_jobs()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( CreationTimeAfter=datetime(2015, 1, 1), CreationTimeBefore=datetime(2015, 1, 1), LastModifiedTimeAfter=datetime(2015, 1, 1), LastModifiedTimeBefore=datetime(2015, 1, 1), NameContains='string', StatusEquals='InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped', SortBy='Name'|'CreationTime'|'Status', SortOrder='Ascending'|'Descending', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **CreationTimeAfter** (*datetime*) -- A filter that returns only processing jobs created after the specified time. * **CreationTimeBefore** (*datetime*) -- A filter that returns only processing jobs created after the specified time. * **LastModifiedTimeAfter** (*datetime*) -- A filter that returns only processing jobs modified after the specified time. * **LastModifiedTimeBefore** (*datetime*) -- A filter that returns only processing jobs modified before the specified time. * **NameContains** (*string*) -- A string in the processing job name. This filter returns only processing jobs whose name contains the specified string. * **StatusEquals** (*string*) -- A filter that retrieves only processing jobs with a specific status. * **SortBy** (*string*) -- The field to sort results by. The default is "CreationTime". * **SortOrder** (*string*) -- The sort order for results. The default is "Ascending". * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'ProcessingJobSummaries': [ { 'ProcessingJobName': 'string', 'ProcessingJobArn': 'string', 'CreationTime': datetime(2015, 1, 1), 'ProcessingEndTime': datetime(2015, 1, 1), 'LastModifiedTime': datetime(2015, 1, 1), 'ProcessingJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped', 'FailureReason': 'string', 'ExitMessage': 'string' }, ], } **Response Structure** * *(dict) --* * **ProcessingJobSummaries** *(list) --* An array of "ProcessingJobSummary" objects, each listing a processing job. * *(dict) --* Summary of information about a processing job. * **ProcessingJobName** *(string) --* The name of the processing job. * **ProcessingJobArn** *(string) --* The Amazon Resource Name (ARN) of the processing job.. * **CreationTime** *(datetime) --* The time at which the processing job was created. * **ProcessingEndTime** *(datetime) --* The time at which the processing job completed. * **LastModifiedTime** *(datetime) --* A timestamp that indicates the last time the processing job was modified. * **ProcessingJobStatus** *(string) --* The status of the processing job. * **FailureReason** *(string) --* A string, up to one KB in size, that contains the reason a processing job failed, if it failed. * **ExitMessage** *(string) --* An optional string, up to one KB in size, that contains metadata from the processing container when the processing job exits. SageMaker / Paginator / ListModelCardExportJobs ListModelCardExportJobs *********************** class SageMaker.Paginator.ListModelCardExportJobs paginator = client.get_paginator('list_model_card_export_jobs') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_model_card_export_jobs()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( ModelCardName='string', ModelCardVersion=123, CreationTimeAfter=datetime(2015, 1, 1), CreationTimeBefore=datetime(2015, 1, 1), ModelCardExportJobNameContains='string', StatusEquals='InProgress'|'Completed'|'Failed', SortBy='Name'|'CreationTime'|'Status', SortOrder='Ascending'|'Descending', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **ModelCardName** (*string*) -- **[REQUIRED]** List export jobs for the model card with the specified name. * **ModelCardVersion** (*integer*) -- List export jobs for the model card with the specified version. * **CreationTimeAfter** (*datetime*) -- Only list model card export jobs that were created after the time specified. * **CreationTimeBefore** (*datetime*) -- Only list model card export jobs that were created before the time specified. * **ModelCardExportJobNameContains** (*string*) -- Only list model card export jobs with names that contain the specified string. * **StatusEquals** (*string*) -- Only list model card export jobs with the specified status. * **SortBy** (*string*) -- Sort model card export jobs by either name or creation time. Sorts by creation time by default. * **SortOrder** (*string*) -- Sort model card export jobs by ascending or descending order. * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'ModelCardExportJobSummaries': [ { 'ModelCardExportJobName': 'string', 'ModelCardExportJobArn': 'string', 'Status': 'InProgress'|'Completed'|'Failed', 'ModelCardName': 'string', 'ModelCardVersion': 123, 'CreatedAt': datetime(2015, 1, 1), 'LastModifiedAt': datetime(2015, 1, 1) }, ], } **Response Structure** * *(dict) --* * **ModelCardExportJobSummaries** *(list) --* The summaries of the listed model card export jobs. * *(dict) --* The summary of the Amazon SageMaker Model Card export job. * **ModelCardExportJobName** *(string) --* The name of the model card export job. * **ModelCardExportJobArn** *(string) --* The Amazon Resource Name (ARN) of the model card export job. * **Status** *(string) --* The completion status of the model card export job. * **ModelCardName** *(string) --* The name of the model card that the export job exports. * **ModelCardVersion** *(integer) --* The version of the model card that the export job exports. * **CreatedAt** *(datetime) --* The date and time that the model card export job was created. * **LastModifiedAt** *(datetime) --* The date and time that the model card export job was last modified.. SageMaker / Paginator / ListModelMetadata ListModelMetadata ***************** class SageMaker.Paginator.ListModelMetadata paginator = client.get_paginator('list_model_metadata') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_model_metadata()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( SearchExpression={ 'Filters': [ { 'Name': 'Domain'|'Framework'|'Task'|'FrameworkVersion', 'Value': 'string' }, ] }, PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **SearchExpression** (*dict*) -- One or more filters that searches for the specified resource or resources in a search. All resource objects that satisfy the expression's condition are included in the search results. Specify the Framework, FrameworkVersion, Domain or Task to filter supported. Filter names and values are case-sensitive. * **Filters** *(list) --* A list of filter objects. * *(dict) --* Part of the search expression. You can specify the name and value (domain, task, framework, framework version, task, and model). * **Name** *(string) --* **[REQUIRED]** The name of the of the model to filter by. * **Value** *(string) --* **[REQUIRED]** The value to filter the model metadata. * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'ModelMetadataSummaries': [ { 'Domain': 'string', 'Framework': 'string', 'Task': 'string', 'Model': 'string', 'FrameworkVersion': 'string' }, ], } **Response Structure** * *(dict) --* * **ModelMetadataSummaries** *(list) --* A structure that holds model metadata. * *(dict) --* A summary of the model metadata. * **Domain** *(string) --* The machine learning domain of the model. * **Framework** *(string) --* The machine learning framework of the model. * **Task** *(string) --* The machine learning task of the model. * **Model** *(string) --* The name of the model. * **FrameworkVersion** *(string) --* The framework version of the model. SageMaker / Paginator / ListImages ListImages ********** class SageMaker.Paginator.ListImages paginator = client.get_paginator('list_images') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_images()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( CreationTimeAfter=datetime(2015, 1, 1), CreationTimeBefore=datetime(2015, 1, 1), LastModifiedTimeAfter=datetime(2015, 1, 1), LastModifiedTimeBefore=datetime(2015, 1, 1), NameContains='string', SortBy='CREATION_TIME'|'LAST_MODIFIED_TIME'|'IMAGE_NAME', SortOrder='ASCENDING'|'DESCENDING', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **CreationTimeAfter** (*datetime*) -- A filter that returns only images created on or after the specified time. * **CreationTimeBefore** (*datetime*) -- A filter that returns only images created on or before the specified time. * **LastModifiedTimeAfter** (*datetime*) -- A filter that returns only images modified on or after the specified time. * **LastModifiedTimeBefore** (*datetime*) -- A filter that returns only images modified on or before the specified time. * **NameContains** (*string*) -- A filter that returns only images whose name contains the specified string. * **SortBy** (*string*) -- The property used to sort results. The default value is "CREATION_TIME". * **SortOrder** (*string*) -- The sort order. The default value is "DESCENDING". * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'Images': [ { 'CreationTime': datetime(2015, 1, 1), 'Description': 'string', 'DisplayName': 'string', 'FailureReason': 'string', 'ImageArn': 'string', 'ImageName': 'string', 'ImageStatus': 'CREATING'|'CREATED'|'CREATE_FAILED'|'UPDATING'|'UPDATE_FAILED'|'DELETING'|'DELETE_FAILED', 'LastModifiedTime': datetime(2015, 1, 1) }, ], } **Response Structure** * *(dict) --* * **Images** *(list) --* A list of images and their properties. * *(dict) --* A SageMaker AI image. A SageMaker AI image represents a set of container images that are derived from a common base container image. Each of these container images is represented by a SageMaker AI "ImageVersion". * **CreationTime** *(datetime) --* When the image was created. * **Description** *(string) --* The description of the image. * **DisplayName** *(string) --* The name of the image as displayed. * **FailureReason** *(string) --* When a create, update, or delete operation fails, the reason for the failure. * **ImageArn** *(string) --* The ARN of the image. * **ImageName** *(string) --* The name of the image. * **ImageStatus** *(string) --* The status of the image. * **LastModifiedTime** *(datetime) --* When the image was last modified. SageMaker / Paginator / ListLabelingJobsForWorkteam ListLabelingJobsForWorkteam *************************** class SageMaker.Paginator.ListLabelingJobsForWorkteam paginator = client.get_paginator('list_labeling_jobs_for_workteam') paginate(**kwargs) Creates an iterator that will paginate through responses from "SageMaker.Client.list_labeling_jobs_for_workteam()". See also: AWS API Documentation **Request Syntax** response_iterator = paginator.paginate( WorkteamArn='string', CreationTimeAfter=datetime(2015, 1, 1), CreationTimeBefore=datetime(2015, 1, 1), JobReferenceCodeContains='string', SortBy='CreationTime', SortOrder='Ascending'|'Descending', PaginationConfig={ 'MaxItems': 123, 'PageSize': 123, 'StartingToken': 'string' } ) Parameters: * **WorkteamArn** (*string*) -- **[REQUIRED]** The Amazon Resource Name (ARN) of the work team for which you want to see labeling jobs for. * **CreationTimeAfter** (*datetime*) -- A filter that returns only labeling jobs created after the specified time (timestamp). * **CreationTimeBefore** (*datetime*) -- A filter that returns only labeling jobs created before the specified time (timestamp). * **JobReferenceCodeContains** (*string*) -- A filter the limits jobs to only the ones whose job reference code contains the specified string. * **SortBy** (*string*) -- The field to sort results by. The default is "CreationTime". * **SortOrder** (*string*) -- The sort order for results. The default is "Ascending". * **PaginationConfig** (*dict*) -- A dictionary that provides parameters to control pagination. * **MaxItems** *(integer) --* The total number of items to return. If the total number of items available is more than the value specified in max-items then a "NextToken" will be provided in the output that you can use to resume pagination. * **PageSize** *(integer) --* The size of each page. * **StartingToken** *(string) --* A token to specify where to start paginating. This is the "NextToken" from a previous response. Return type: dict Returns: **Response Syntax** { 'LabelingJobSummaryList': [ { 'LabelingJobName': 'string', 'JobReferenceCode': 'string', 'WorkRequesterAccountId': 'string', 'CreationTime': datetime(2015, 1, 1), 'LabelCounters': { 'HumanLabeled': 123, 'PendingHuman': 123, 'Total': 123 }, 'NumberOfHumanWorkersPerDataObject': 123 }, ], } **Response Structure** * *(dict) --* * **LabelingJobSummaryList** *(list) --* An array of "LabelingJobSummary" objects, each describing a labeling job. * *(dict) --* Provides summary information for a work team. * **LabelingJobName** *(string) --* The name of the labeling job that the work team is assigned to. * **JobReferenceCode** *(string) --* A unique identifier for a labeling job. You can use this to refer to a specific labeling job. * **WorkRequesterAccountId** *(string) --* The Amazon Web Services account ID of the account used to start the labeling job. * **CreationTime** *(datetime) --* The date and time that the labeling job was created. * **LabelCounters** *(dict) --* Provides information about the progress of a labeling job. * **HumanLabeled** *(integer) --* The total number of data objects labeled by a human worker. * **PendingHuman** *(integer) --* The total number of data objects that need to be labeled by a human worker. * **Total** *(integer) --* The total number of tasks in the labeling job. * **NumberOfHumanWorkersPerDataObject** *(integer) --* The configured number of workers per data object. SageMaker / Client / describe_cluster_node describe_cluster_node ********************* SageMaker.Client.describe_cluster_node(**kwargs) Retrieves information of a node (also called a *instance* interchangeably) of a SageMaker HyperPod cluster. See also: AWS API Documentation **Request Syntax** response = client.describe_cluster_node( ClusterName='string', NodeId='string', NodeLogicalId='string' ) Parameters: * **ClusterName** (*string*) -- **[REQUIRED]** The string name or the Amazon Resource Name (ARN) of the SageMaker HyperPod cluster in which the node is. * **NodeId** (*string*) -- The ID of the SageMaker HyperPod cluster node. * **NodeLogicalId** (*string*) -- The logical identifier of the node to describe. You can specify either "NodeLogicalId" or "InstanceId", but not both. "NodeLogicalId" can be used to describe nodes that are still being provisioned and don't yet have an "InstanceId" assigned. Return type: dict Returns: **Response Syntax** { 'NodeDetails': { 'InstanceGroupName': 'string', 'InstanceId': 'string', 'NodeLogicalId': 'string', 'InstanceStatus': { 'Status': 'Running'|'Failure'|'Pending'|'ShuttingDown'|'SystemUpdating'|'DeepHealthCheckInProgress'|'NotFound', 'Message': 'string' }, 'InstanceType': 'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.c5n.large'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.16xlarge'|'ml.g6.12xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.gr6.4xlarge'|'ml.gr6.8xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.16xlarge'|'ml.g6e.12xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.p5e.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.trn2.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.i3en.large'|'ml.i3en.xlarge'|'ml.i3en.2xlarge'|'ml.i3en.3xlarge'|'ml.i3en.6xlarge'|'ml.i3en.12xlarge'|'ml.i3en.24xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge', 'LaunchTime': datetime(2015, 1, 1), 'LastSoftwareUpdateTime': datetime(2015, 1, 1), 'LifeCycleConfig': { 'SourceS3Uri': 'string', 'OnCreate': 'string' }, 'OverrideVpcConfig': { 'SecurityGroupIds': [ 'string', ], 'Subnets': [ 'string', ] }, 'ThreadsPerCore': 123, 'InstanceStorageConfigs': [ { 'EbsVolumeConfig': { 'VolumeSizeInGB': 123 } }, ], 'PrivatePrimaryIp': 'string', 'PrivatePrimaryIpv6': 'string', 'PrivateDnsHostname': 'string', 'Placement': { 'AvailabilityZone': 'string', 'AvailabilityZoneId': 'string' }, 'CurrentImageId': 'string', 'DesiredImageId': 'string', 'UltraServerInfo': { 'Id': 'string' } } } **Response Structure** * *(dict) --* * **NodeDetails** *(dict) --* The details of the SageMaker HyperPod cluster node. * **InstanceGroupName** *(string) --* The instance group name in which the instance is. * **InstanceId** *(string) --* The ID of the instance. * **NodeLogicalId** *(string) --* A unique identifier for the node that persists throughout its lifecycle, from provisioning request to termination. This identifier can be used to track the node even before it has an assigned "InstanceId". * **InstanceStatus** *(dict) --* The status of the instance. * **Status** *(string) --* The status of an instance in a SageMaker HyperPod cluster. * **Message** *(string) --* The message from an instance in a SageMaker HyperPod cluster. * **InstanceType** *(string) --* The type of the instance. * **LaunchTime** *(datetime) --* The time when the instance is launched. * **LastSoftwareUpdateTime** *(datetime) --* The time when the cluster was last updated. * **LifeCycleConfig** *(dict) --* The LifeCycle configuration applied to the instance. * **SourceS3Uri** *(string) --* An Amazon S3 bucket path where your lifecycle scripts are stored. Warning: Make sure that the S3 bucket path starts with "s3://sagemaker-". The IAM role for SageMaker HyperPod has the managed AmazonSageMakerClusterInstanceRolePolicy attached, which allows access to S3 buckets with the specific prefix "sagemaker-". * **OnCreate** *(string) --* The file name of the entrypoint script of lifecycle scripts under "SourceS3Uri". This entrypoint script runs during cluster creation. * **OverrideVpcConfig** *(dict) --* The customized Amazon VPC configuration at the instance group level that overrides the default Amazon VPC configuration of the SageMaker HyperPod cluster. * **SecurityGroupIds** *(list) --* The VPC security group IDs, in the form "sg-xxxxxxxx". Specify the security groups for the VPC that is specified in the "Subnets" field. * *(string) --* * **Subnets** *(list) --* The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones. * *(string) --* * **ThreadsPerCore** *(integer) --* The number of threads per CPU core you specified under "CreateCluster". * **InstanceStorageConfigs** *(list) --* The configurations of additional storage specified to the instance group where the instance (node) is launched. * *(dict) --* Defines the configuration for attaching additional storage to the instances in the SageMaker HyperPod cluster instance group. To learn more, see SageMaker HyperPod release notes: June 20, 2024. Note: This is a Tagged Union structure. Only one of the following top level keys will be set: "EbsVolumeConfig". If a client receives an unknown member it will set "SDK_UNKNOWN_MEMBER" as the top level key, which maps to the name or tag of the unknown member. The structure of "SDK_UNKNOWN_MEMBER" is as follows: 'SDK_UNKNOWN_MEMBER': {'name': 'UnknownMemberName'} * **EbsVolumeConfig** *(dict) --* Defines the configuration for attaching additional Amazon Elastic Block Store (EBS) volumes to the instances in the SageMaker HyperPod cluster instance group. The additional EBS volume is attached to each instance within the SageMaker HyperPod cluster instance group and mounted to "/opt/sagemaker". * **VolumeSizeInGB** *(integer) --* The size in gigabytes (GB) of the additional EBS volume to be attached to the instances in the SageMaker HyperPod cluster instance group. The additional EBS volume is attached to each instance within the SageMaker HyperPod cluster instance group and mounted to "/opt/sagemaker". * **PrivatePrimaryIp** *(string) --* The private primary IP address of the SageMaker HyperPod cluster node. * **PrivatePrimaryIpv6** *(string) --* The private primary IPv6 address of the SageMaker HyperPod cluster node when configured with an Amazon VPC that supports IPv6 and includes subnets with IPv6 addressing enabled in either the cluster Amazon VPC configuration or the instance group Amazon VPC configuration. * **PrivateDnsHostname** *(string) --* The private DNS hostname of the SageMaker HyperPod cluster node. * **Placement** *(dict) --* The placement details of the SageMaker HyperPod cluster node. * **AvailabilityZone** *(string) --* The Availability Zone where the node in the SageMaker HyperPod cluster is launched. * **AvailabilityZoneId** *(string) --* The unique identifier (ID) of the Availability Zone where the node in the SageMaker HyperPod cluster is launched. * **CurrentImageId** *(string) --* The ID of the Amazon Machine Image (AMI) currently in use by the node. * **DesiredImageId** *(string) --* The ID of the Amazon Machine Image (AMI) desired for the node. * **UltraServerInfo** *(dict) --* Contains information about the UltraServer. * **Id** *(string) --* The unique identifier of the UltraServer. **Exceptions** * "SageMaker.Client.exceptions.ResourceNotFound" SageMaker / Client / batch_delete_cluster_nodes batch_delete_cluster_nodes ************************** SageMaker.Client.batch_delete_cluster_nodes(**kwargs) Deletes specific nodes within a SageMaker HyperPod cluster. "BatchDeleteClusterNodes" accepts a cluster name and a list of node IDs. Warning: * To safeguard your work, back up your data to Amazon S3 or an FSx for Lustre file system before invoking the API on a worker node group. This will help prevent any potential data loss from the instance root volume. For more information about backup, see Use the backup script provided by SageMaker HyperPod. * If you want to invoke this API on an existing cluster, you'll first need to patch the cluster by running the UpdateClusterSoftware API. For more information about patching a cluster, see Update the SageMaker HyperPod platform software of a cluster. See also: AWS API Documentation **Request Syntax** response = client.batch_delete_cluster_nodes( ClusterName='string', NodeIds=[ 'string', ], NodeLogicalIds=[ 'string', ] ) Parameters: * **ClusterName** (*string*) -- **[REQUIRED]** The name of the SageMaker HyperPod cluster from which to delete the specified nodes. * **NodeIds** (*list*) -- A list of node IDs to be deleted from the specified cluster. Note: * For SageMaker HyperPod clusters using the Slurm workload manager, you cannot remove instances that are configured as Slurm controller nodes. * If you need to delete more than 99 instances, contact Support for assistance. * *(string) --* * **NodeLogicalIds** (*list*) -- A list of "NodeLogicalIds" identifying the nodes to be deleted. You can specify up to 50 "NodeLogicalIds". You must specify either "NodeLogicalIds", "InstanceIds", or both, with a combined maximum of 50 identifiers. * *(string) --* Return type: dict Returns: **Response Syntax** { 'Failed': [ { 'Code': 'NodeIdNotFound'|'InvalidNodeStatus'|'NodeIdInUse', 'Message': 'string', 'NodeId': 'string' }, ], 'Successful': [ 'string', ], 'FailedNodeLogicalIds': [ { 'Code': 'NodeIdNotFound'|'InvalidNodeStatus'|'NodeIdInUse', 'Message': 'string', 'NodeLogicalId': 'string' }, ], 'SuccessfulNodeLogicalIds': [ 'string', ] } **Response Structure** * *(dict) --* * **Failed** *(list) --* A list of errors encountered when deleting the specified nodes. * *(dict) --* Represents an error encountered when deleting a node from a SageMaker HyperPod cluster. * **Code** *(string) --* The error code associated with the error encountered when deleting a node. The code provides information about the specific issue encountered, such as the node not being found, the node's status being invalid for deletion, or the node ID being in use by another process. * **Message** *(string) --* A message describing the error encountered when deleting a node. * **NodeId** *(string) --* The ID of the node that encountered an error during the deletion process. * **Successful** *(list) --* A list of node IDs that were successfully deleted from the specified cluster. * *(string) --* * **FailedNodeLogicalIds** *(list) --* A list of "NodeLogicalIds" that could not be deleted, along with error information explaining why the deletion failed. * *(dict) --* Information about an error that occurred when attempting to delete a node identified by its "NodeLogicalId". * **Code** *(string) --* The error code associated with the failure. Possible values include "NodeLogicalIdNotFound", "InvalidNodeStatus", and "InternalError". * **Message** *(string) --* A descriptive message providing additional details about the error. * **NodeLogicalId** *(string) --* The "NodeLogicalId" of the node that could not be deleted. * **SuccessfulNodeLogicalIds** *(list) --* A list of "NodeLogicalIds" that were successfully deleted from the cluster. * *(string) --* **Exceptions** * "SageMaker.Client.exceptions.ResourceNotFound" SageMaker / Client / delete_monitoring_schedule delete_monitoring_schedule ************************** SageMaker.Client.delete_monitoring_schedule(**kwargs) Deletes a monitoring schedule. Also stops the schedule had not already been stopped. This does not delete the job execution history of the monitoring schedule. See also: AWS API Documentation **Request Syntax** response = client.delete_monitoring_schedule( MonitoringScheduleName='string' ) Parameters: **MonitoringScheduleName** (*string*) -- **[REQUIRED]** The name of the monitoring schedule to delete. Returns: None **Exceptions** * "SageMaker.Client.exceptions.ResourceNotFound" SageMaker / Client / delete_code_repository delete_code_repository ********************** SageMaker.Client.delete_code_repository(**kwargs) Deletes the specified Git repository from your account. See also: AWS API Documentation **Request Syntax** response = client.delete_code_repository( CodeRepositoryName='string' ) Parameters: **CodeRepositoryName** (*string*) -- **[REQUIRED]** The name of the Git repository to delete. Returns: None SageMaker / Client / describe_reserved_capacity describe_reserved_capacity ************************** SageMaker.Client.describe_reserved_capacity(**kwargs) Retrieves details about a reserved capacity. See also: AWS API Documentation **Request Syntax** response = client.describe_reserved_capacity( ReservedCapacityArn='string' ) Parameters: **ReservedCapacityArn** (*string*) -- **[REQUIRED]** ARN of the reserved capacity to describe. Return type: dict Returns: **Response Syntax** { 'ReservedCapacityArn': 'string', 'ReservedCapacityType': 'UltraServer'|'Instance', 'Status': 'Pending'|'Active'|'Scheduled'|'Expired'|'Failed', 'AvailabilityZone': 'string', 'DurationHours': 123, 'DurationMinutes': 123, 'StartTime': datetime(2015, 1, 1), 'EndTime': datetime(2015, 1, 1), 'InstanceType': 'ml.p4d.24xlarge'|'ml.p5.48xlarge'|'ml.p5e.48xlarge'|'ml.p5en.48xlarge'|'ml.trn1.32xlarge'|'ml.trn2.48xlarge'|'ml.p6-b200.48xlarge'|'ml.p4de.24xlarge'|'ml.p6e-gb200.36xlarge', 'TotalInstanceCount': 123, 'AvailableInstanceCount': 123, 'InUseInstanceCount': 123, 'UltraServerSummary': { 'UltraServerType': 'string', 'InstanceType': 'ml.p4d.24xlarge'|'ml.p5.48xlarge'|'ml.p5e.48xlarge'|'ml.p5en.48xlarge'|'ml.trn1.32xlarge'|'ml.trn2.48xlarge'|'ml.p6-b200.48xlarge'|'ml.p4de.24xlarge'|'ml.p6e-gb200.36xlarge', 'UltraServerCount': 123, 'AvailableSpareInstanceCount': 123, 'UnhealthyInstanceCount': 123 } } **Response Structure** * *(dict) --* * **ReservedCapacityArn** *(string) --* ARN of the reserved capacity. * **ReservedCapacityType** *(string) --* The type of reserved capacity. * **Status** *(string) --* The current status of the reserved capacity. * **AvailabilityZone** *(string) --* The Availability Zone where the reserved capacity is provisioned. * **DurationHours** *(integer) --* The total duration of the reserved capacity in hours. * **DurationMinutes** *(integer) --* The number of minutes for the duration of the reserved capacity. For example, if a reserved capacity starts at 08:55 and ends at 11:30, the minutes field would be 35. * **StartTime** *(datetime) --* The timestamp when the reserved capacity becomes active. * **EndTime** *(datetime) --* The timestamp when the reserved capacity expires. * **InstanceType** *(string) --* The Amazon EC2 instance type used in the reserved capacity. * **TotalInstanceCount** *(integer) --* The total number of instances allocated to this reserved capacity. * **AvailableInstanceCount** *(integer) --* The number of instances currently available for use in this reserved capacity. * **InUseInstanceCount** *(integer) --* The number of instances currently in use from this reserved capacity. * **UltraServerSummary** *(dict) --* A summary of the UltraServer associated with this reserved capacity. * **UltraServerType** *(string) --* The type of UltraServer, such as ml.u-p6e-gb200x72. * **InstanceType** *(string) --* The Amazon EC2 instance type used in the UltraServer. * **UltraServerCount** *(integer) --* The number of UltraServers of this type. * **AvailableSpareInstanceCount** *(integer) --* The number of available spare instances in the UltraServers. * **UnhealthyInstanceCount** *(integer) --* The total number of instances across all UltraServers of this type that are currently in an unhealthy state. **Exceptions** * "SageMaker.Client.exceptions.ResourceNotFound" SageMaker / Client / stop_mlflow_tracking_server stop_mlflow_tracking_server *************************** SageMaker.Client.stop_mlflow_tracking_server(**kwargs) Programmatically stop an MLflow Tracking Server. See also: AWS API Documentation **Request Syntax** response = client.stop_mlflow_tracking_server( TrackingServerName='string' ) Parameters: **TrackingServerName** (*string*) -- **[REQUIRED]** The name of the tracking server to stop. Return type: dict Returns: **Response Syntax** { 'TrackingServerArn': 'string' } **Response Structure** * *(dict) --* * **TrackingServerArn** *(string) --* The ARN of the stopped tracking server. **Exceptions** * "SageMaker.Client.exceptions.ConflictException" * "SageMaker.Client.exceptions.ResourceNotFound" SageMaker / Client / list_compilation_jobs list_compilation_jobs ********************* SageMaker.Client.list_compilation_jobs(**kwargs) Lists model compilation jobs that satisfy various filters. To create a model compilation job, use CreateCompilationJob. To get information about a particular model compilation job you have created, use DescribeCompilationJob. See also: AWS API Documentation **Request Syntax** response = client.list_compilation_jobs( NextToken='string', MaxResults=123, CreationTimeAfter=datetime(2015, 1, 1), CreationTimeBefore=datetime(2015, 1, 1), LastModifiedTimeAfter=datetime(2015, 1, 1), LastModifiedTimeBefore=datetime(2015, 1, 1), NameContains='string', StatusEquals='INPROGRESS'|'COMPLETED'|'FAILED'|'STARTING'|'STOPPING'|'STOPPED', SortBy='Name'|'CreationTime'|'Status', SortOrder='Ascending'|'Descending' ) Parameters: * **NextToken** (*string*) -- If the result of the previous "ListCompilationJobs" request was truncated, the response includes a "NextToken". To retrieve the next set of model compilation jobs, use the token in the next request. * **MaxResults** (*integer*) -- The maximum number of model compilation jobs to return in the response. * **CreationTimeAfter** (*datetime*) -- A filter that returns the model compilation jobs that were created after a specified time. * **CreationTimeBefore** (*datetime*) -- A filter that returns the model compilation jobs that were created before a specified time. * **LastModifiedTimeAfter** (*datetime*) -- A filter that returns the model compilation jobs that were modified after a specified time. * **LastModifiedTimeBefore** (*datetime*) -- A filter that returns the model compilation jobs that were modified before a specified time. * **NameContains** (*string*) -- A filter that returns the model compilation jobs whose name contains a specified string. * **StatusEquals** (*string*) -- A filter that retrieves model compilation jobs with a specific "CompilationJobStatus" status. * **SortBy** (*string*) -- The field by which to sort results. The default is "CreationTime". * **SortOrder** (*string*) -- The sort order for results. The default is "Ascending". Return type: dict Returns: **Response Syntax** { 'CompilationJobSummaries': [ { 'CompilationJobName': 'string', 'CompilationJobArn': 'string', 'CreationTime': datetime(2015, 1, 1), 'CompilationStartTime': datetime(2015, 1, 1), 'CompilationEndTime': datetime(2015, 1, 1), 'CompilationTargetDevice': 'lambda'|'ml_m4'|'ml_m5'|'ml_m6g'|'ml_c4'|'ml_c5'|'ml_c6g'|'ml_p2'|'ml_p3'|'ml_g4dn'|'ml_inf1'|'ml_inf2'|'ml_trn1'|'ml_eia2'|'jetson_tx1'|'jetson_tx2'|'jetson_nano'|'jetson_xavier'|'rasp3b'|'rasp4b'|'imx8qm'|'deeplens'|'rk3399'|'rk3288'|'aisage'|'sbe_c'|'qcs605'|'qcs603'|'sitara_am57x'|'amba_cv2'|'amba_cv22'|'amba_cv25'|'x86_win32'|'x86_win64'|'coreml'|'jacinto_tda4vm'|'imx8mplus', 'CompilationTargetPlatformOs': 'ANDROID'|'LINUX', 'CompilationTargetPlatformArch': 'X86_64'|'X86'|'ARM64'|'ARM_EABI'|'ARM_EABIHF', 'CompilationTargetPlatformAccelerator': 'INTEL_GRAPHICS'|'MALI'|'NVIDIA'|'NNA', 'LastModifiedTime': datetime(2015, 1, 1), 'CompilationJobStatus': 'INPROGRESS'|'COMPLETED'|'FAILED'|'STARTING'|'STOPPING'|'STOPPED' }, ], 'NextToken': 'string' } **Response Structure** * *(dict) --* * **CompilationJobSummaries** *(list) --* An array of CompilationJobSummary objects, each describing a model compilation job. * *(dict) --* A summary of a model compilation job. * **CompilationJobName** *(string) --* The name of the model compilation job that you want a summary for. * **CompilationJobArn** *(string) --* The Amazon Resource Name (ARN) of the model compilation job. * **CreationTime** *(datetime) --* The time when the model compilation job was created. * **CompilationStartTime** *(datetime) --* The time when the model compilation job started. * **CompilationEndTime** *(datetime) --* The time when the model compilation job completed. * **CompilationTargetDevice** *(string) --* The type of device that the model will run on after the compilation job has completed. * **CompilationTargetPlatformOs** *(string) --* The type of OS that the model will run on after the compilation job has completed. * **CompilationTargetPlatformArch** *(string) --* The type of architecture that the model will run on after the compilation job has completed. * **CompilationTargetPlatformAccelerator** *(string) --* The type of accelerator that the model will run on after the compilation job has completed. * **LastModifiedTime** *(datetime) --* The time when the model compilation job was last modified. * **CompilationJobStatus** *(string) --* The status of the model compilation job. * **NextToken** *(string) --* If the response is truncated, Amazon SageMaker AI returns this "NextToken". To retrieve the next set of model compilation jobs, use this token in the next request. SageMaker / Client / create_inference_component create_inference_component ************************** SageMaker.Client.create_inference_component(**kwargs) Creates an inference component, which is a SageMaker AI hosting object that you can use to deploy a model to an endpoint. In the inference component settings, you specify the model, the endpoint, and how the model utilizes the resources that the endpoint hosts. You can optimize resource utilization by tailoring how the required CPU cores, accelerators, and memory are allocated. You can deploy multiple inference components to an endpoint, where each inference component contains one model and the resource utilization needs for that individual model. After you deploy an inference component, you can directly invoke the associated model when you use the InvokeEndpoint API action. See also: AWS API Documentation **Request Syntax** response = client.create_inference_component( InferenceComponentName='string', EndpointName='string', VariantName='string', Specification={ 'ModelName': 'string', 'Container': { 'Image': 'string', 'ArtifactUrl': 'string', 'Environment': { 'string': 'string' } }, 'StartupParameters': { 'ModelDataDownloadTimeoutInSeconds': 123, 'ContainerStartupHealthCheckTimeoutInSeconds': 123 }, 'ComputeResourceRequirements': { 'NumberOfCpuCoresRequired': ..., 'NumberOfAcceleratorDevicesRequired': ..., 'MinMemoryRequiredInMb': 123, 'MaxMemoryRequiredInMb': 123 }, 'BaseInferenceComponentName': 'string' }, RuntimeConfig={ 'CopyCount': 123 }, Tags=[ { 'Key': 'string', 'Value': 'string' }, ] ) Parameters: * **InferenceComponentName** (*string*) -- **[REQUIRED]** A unique name to assign to the inference component. * **EndpointName** (*string*) -- **[REQUIRED]** The name of an existing endpoint where you host the inference component. * **VariantName** (*string*) -- The name of an existing production variant where you host the inference component. * **Specification** (*dict*) -- **[REQUIRED]** Details about the resources to deploy with this inference component, including the model, container, and compute resources. * **ModelName** *(string) --* The name of an existing SageMaker AI model object in your account that you want to deploy with the inference component. * **Container** *(dict) --* Defines a container that provides the runtime environment for a model that you deploy with an inference component. * **Image** *(string) --* The Amazon Elastic Container Registry (Amazon ECR) path where the Docker image for the model is stored. * **ArtifactUrl** *(string) --* The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). * **Environment** *(dict) --* The environment variables to set in the Docker container. Each key and value in the Environment string-to-string map can have length of up to 1024. We support up to 16 entries in the map. * *(string) --* * *(string) --* * **StartupParameters** *(dict) --* Settings that take effect while the model container starts up. * **ModelDataDownloadTimeoutInSeconds** *(integer) --* The timeout value, in seconds, to download and extract the model that you want to host from Amazon S3 to the individual inference instance associated with this inference component. * **ContainerStartupHealthCheckTimeoutInSeconds** *(integer) --* The timeout value, in seconds, for your inference container to pass health check by Amazon S3 Hosting. For more information about health check, see How Your Container Should Respond to Health Check (Ping) Requests. * **ComputeResourceRequirements** *(dict) --* The compute resources allocated to run the model, plus any adapter models, that you assign to the inference component. Omit this parameter if your request is meant to create an adapter inference component. An adapter inference component is loaded by a base inference component, and it uses the compute resources of the base inference component. * **NumberOfCpuCoresRequired** *(float) --* The number of CPU cores to allocate to run a model that you assign to an inference component. * **NumberOfAcceleratorDevicesRequired** *(float) --* The number of accelerators to allocate to run a model that you assign to an inference component. Accelerators include GPUs and Amazon Web Services Inferentia. * **MinMemoryRequiredInMb** *(integer) --* **[REQUIRED]** The minimum MB of memory to allocate to run a model that you assign to an inference component. * **MaxMemoryRequiredInMb** *(integer) --* The maximum MB of memory to allocate to run a model that you assign to an inference component. * **BaseInferenceComponentName** *(string) --* The name of an existing inference component that is to contain the inference component that you're creating with your request. Specify this parameter only if your request is meant to create an adapter inference component. An adapter inference component contains the path to an adapter model. The purpose of the adapter model is to tailor the inference output of a base foundation model, which is hosted by the base inference component. The adapter inference component uses the compute resources that you assigned to the base inference component. When you create an adapter inference component, use the "Container" parameter to specify the location of the adapter artifacts. In the parameter value, use the "ArtifactUrl" parameter of the "InferenceComponentContainerSpecification" data type. Before you can create an adapter inference component, you must have an existing inference component that contains the foundation model that you want to adapt. * **RuntimeConfig** (*dict*) -- Runtime settings for a model that is deployed with an inference component. * **CopyCount** *(integer) --* **[REQUIRED]** The number of runtime copies of the model container to deploy with the inference component. Each copy can serve inference requests. * **Tags** (*list*) -- A list of key-value pairs associated with the model. For more information, see Tagging Amazon Web Services resources in the *Amazon Web Services General Reference*. * *(dict) --* A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources. You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags. For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy. * **Key** *(string) --* **[REQUIRED]** The tag key. Tag keys must be unique per resource. * **Value** *(string) --* **[REQUIRED]** The tag value. Return type: dict Returns: **Response Syntax** { 'InferenceComponentArn': 'string' } **Response Structure** * *(dict) --* * **InferenceComponentArn** *(string) --* The Amazon Resource Name (ARN) of the inference component. **Exceptions** * "SageMaker.Client.exceptions.ResourceLimitExceeded" SageMaker / Client / create_image_version create_image_version ******************** SageMaker.Client.create_image_version(**kwargs) Creates a version of the SageMaker AI image specified by "ImageName". The version represents the Amazon ECR container image specified by "BaseImage". See also: AWS API Documentation **Request Syntax** response = client.create_image_version( BaseImage='string', ClientToken='string', ImageName='string', Aliases=[ 'string', ], VendorGuidance='NOT_PROVIDED'|'STABLE'|'TO_BE_ARCHIVED'|'ARCHIVED', JobType='TRAINING'|'INFERENCE'|'NOTEBOOK_KERNEL', MLFramework='string', ProgrammingLang='string', Processor='CPU'|'GPU', Horovod=True|False, ReleaseNotes='string' ) Parameters: * **BaseImage** (*string*) -- **[REQUIRED]** The registry path of the container image to use as the starting point for this version. The path is an Amazon ECR URI in the following format: ".dkr.ecr..amazonaws.com/" * **ClientToken** (*string*) -- **[REQUIRED]** A unique ID. If not specified, the Amazon Web Services CLI and Amazon Web Services SDKs, such as the SDK for Python (Boto3), add a unique value to the call. This field is autopopulated if not provided. * **ImageName** (*string*) -- **[REQUIRED]** The "ImageName" of the "Image" to create a version of. * **Aliases** (*list*) -- A list of aliases created with the image version. * *(string) --* * **VendorGuidance** (*string*) -- The stability of the image version, specified by the maintainer. * "NOT_PROVIDED": The maintainers did not provide a status for image version stability. * "STABLE": The image version is stable. * "TO_BE_ARCHIVED": The image version is set to be archived. Custom image versions that are set to be archived are automatically archived after three months. * "ARCHIVED": The image version is archived. Archived image versions are not searchable and are no longer actively supported. * **JobType** (*string*) -- Indicates SageMaker AI job type compatibility. * "TRAINING": The image version is compatible with SageMaker AI training jobs. * "INFERENCE": The image version is compatible with SageMaker AI inference jobs. * "NOTEBOOK_KERNEL": The image version is compatible with SageMaker AI notebook kernels. * **MLFramework** (*string*) -- The machine learning framework vended in the image version. * **ProgrammingLang** (*string*) -- The supported programming language and its version. * **Processor** (*string*) -- Indicates CPU or GPU compatibility. * "CPU": The image version is compatible with CPU. * "GPU": The image version is compatible with GPU. * **Horovod** (*boolean*) -- Indicates Horovod compatibility. * **ReleaseNotes** (*string*) -- The maintainer description of the image version. Return type: dict Returns: **Response Syntax** { 'ImageVersionArn': 'string' } **Response Structure** * *(dict) --* * **ImageVersionArn** *(string) --* The ARN of the image version. **Exceptions** * "SageMaker.Client.exceptions.ResourceNotFound" * "SageMaker.Client.exceptions.ResourceInUse" * "SageMaker.Client.exceptions.ResourceLimitExceeded" SageMaker / Client / delete_experiment delete_experiment ***************** SageMaker.Client.delete_experiment(**kwargs) Deletes an SageMaker experiment. All trials associated with the experiment must be deleted first. Use the ListTrials API to get a list of the trials associated with the experiment. See also: AWS API Documentation **Request Syntax** response = client.delete_experiment( ExperimentName='string' ) Parameters: **ExperimentName** (*string*) -- **[REQUIRED]** The name of the experiment to delete. Return type: dict Returns: **Response Syntax** { 'ExperimentArn': 'string' } **Response Structure** * *(dict) --* * **ExperimentArn** *(string) --* The Amazon Resource Name (ARN) of the experiment that is being deleted. **Exceptions** * "SageMaker.Client.exceptions.ResourceNotFound" SageMaker / Client / create_model_quality_job_definition create_model_quality_job_definition *********************************** SageMaker.Client.create_model_quality_job_definition(**kwargs) Creates a definition for a job that monitors model quality and drift. For information about model monitor, see Amazon SageMaker AI Model Monitor. See also: AWS API Documentation **Request Syntax** response = client.create_model_quality_job_definition( JobDefinitionName='string', ModelQualityBaselineConfig={ 'BaseliningJobName': 'string', 'ConstraintsResource': { 'S3Uri': 'string' } }, ModelQualityAppSpecification={ 'ImageUri': 'string', 'ContainerEntrypoint': [ 'string', ], 'ContainerArguments': [ 'string', ], 'RecordPreprocessorSourceUri': 'string', 'PostAnalyticsProcessorSourceUri': 'string', 'ProblemType': 'BinaryClassification'|'MulticlassClassification'|'Regression', 'Environment': { 'string': 'string' } }, ModelQualityJobInput={ 'EndpointInput': { 'EndpointName': 'string', 'LocalPath': 'string', 'S3InputMode': 'Pipe'|'File', 'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key', 'FeaturesAttribute': 'string', 'InferenceAttribute': 'string', 'ProbabilityAttribute': 'string', 'ProbabilityThresholdAttribute': 123.0, 'StartTimeOffset': 'string', 'EndTimeOffset': 'string', 'ExcludeFeaturesAttribute': 'string' }, 'BatchTransformInput': { 'DataCapturedDestinationS3Uri': 'string', 'DatasetFormat': { 'Csv': { 'Header': True|False }, 'Json': { 'Line': True|False }, 'Parquet': {} }, 'LocalPath': 'string', 'S3InputMode': 'Pipe'|'File', 'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key', 'FeaturesAttribute': 'string', 'InferenceAttribute': 'string', 'ProbabilityAttribute': 'string', 'ProbabilityThresholdAttribute': 123.0, 'StartTimeOffset': 'string', 'EndTimeOffset': 'string', 'ExcludeFeaturesAttribute': 'string' }, 'GroundTruthS3Input': { 'S3Uri': 'string' } }, ModelQualityJobOutputConfig={ 'MonitoringOutputs': [ { 'S3Output': { 'S3Uri': 'string', 'LocalPath': 'string', 'S3UploadMode': 'Continuous'|'EndOfJob' } }, ], 'KmsKeyId': 'string' }, JobResources={ 'ClusterConfig': { 'InstanceCount': 123, 'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.8xlarge'|'ml.r5d.12xlarge'|'ml.r5d.16xlarge'|'ml.r5d.24xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge', 'VolumeSizeInGB': 123, 'VolumeKmsKeyId': 'string' } }, NetworkConfig={ 'EnableInterContainerTrafficEncryption': True|False, 'EnableNetworkIsolation': True|False, 'VpcConfig': { 'SecurityGroupIds': [ 'string', ], 'Subnets': [ 'string', ] } }, RoleArn='string', StoppingCondition={ 'MaxRuntimeInSeconds': 123 }, Tags=[ { 'Key': 'string', 'Value': 'string' }, ] ) Parameters: * **JobDefinitionName** (*string*) -- **[REQUIRED]** The name of the monitoring job definition. * **ModelQualityBaselineConfig** (*dict*) -- Specifies the constraints and baselines for the monitoring job. * **BaseliningJobName** *(string) --* The name of the job that performs baselining for the monitoring job. * **ConstraintsResource** *(dict) --* The constraints resource for a monitoring job. * **S3Uri** *(string) --* The Amazon S3 URI for the constraints resource. * **ModelQualityAppSpecification** (*dict*) -- **[REQUIRED]** The container that runs the monitoring job. * **ImageUri** *(string) --* **[REQUIRED]** The address of the container image that the monitoring job runs. * **ContainerEntrypoint** *(list) --* Specifies the entrypoint for a container that the monitoring job runs. * *(string) --* * **ContainerArguments** *(list) --* An array of arguments for the container used to run the monitoring job. * *(string) --* * **RecordPreprocessorSourceUri** *(string) --* An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flattened JSON so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers. * **PostAnalyticsProcessorSourceUri** *(string) --* An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers. * **ProblemType** *(string) --* The machine learning problem type of the model that the monitoring job monitors. * **Environment** *(dict) --* Sets the environment variables in the container that the monitoring job runs. * *(string) --* * *(string) --* * **ModelQualityJobInput** (*dict*) -- **[REQUIRED]** A list of the inputs that are monitored. Currently endpoints are supported. * **EndpointInput** *(dict) --* Input object for the endpoint * **EndpointName** *(string) --* **[REQUIRED]** An endpoint in customer's account which has enabled "DataCaptureConfig" enabled. * **LocalPath** *(string) --* **[REQUIRED]** Path to the filesystem where the endpoint data is available to the container. * **S3InputMode** *(string) --* Whether the "Pipe" or "File" is used as the input mode for transferring data for the monitoring job. "Pipe" mode is recommended for large datasets. "File" mode is useful for small files that fit in memory. Defaults to "File". * **S3DataDistributionType** *(string) --* Whether input data distributed in Amazon S3 is fully replicated or sharded by an Amazon S3 key. Defaults to "FullyReplicated" * **FeaturesAttribute** *(string) --* The attributes of the input data that are the input features. * **InferenceAttribute** *(string) --* The attribute of the input data that represents the ground truth label. * **ProbabilityAttribute** *(string) --* In a classification problem, the attribute that represents the class probability. * **ProbabilityThresholdAttribute** *(float) --* The threshold for the class probability to be evaluated as a positive result. * **StartTimeOffset** *(string) --* If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs. * **EndTimeOffset** *(string) --* If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs. * **ExcludeFeaturesAttribute** *(string) --* The attributes of the input data to exclude from the analysis. * **BatchTransformInput** *(dict) --* Input object for the batch transform job. * **DataCapturedDestinationS3Uri** *(string) --* **[REQUIRED]** The Amazon S3 location being used to capture the data. * **DatasetFormat** *(dict) --* **[REQUIRED]** The dataset format for your batch transform job. * **Csv** *(dict) --* The CSV dataset used in the monitoring job. * **Header** *(boolean) --* Indicates if the CSV data has a header. * **Json** *(dict) --* The JSON dataset used in the monitoring job * **Line** *(boolean) --* Indicates if the file should be read as a JSON object per line. * **Parquet** *(dict) --* The Parquet dataset used in the monitoring job * **LocalPath** *(string) --* **[REQUIRED]** Path to the filesystem where the batch transform data is available to the container. * **S3InputMode** *(string) --* Whether the "Pipe" or "File" is used as the input mode for transferring data for the monitoring job. "Pipe" mode is recommended for large datasets. "File" mode is useful for small files that fit in memory. Defaults to "File". * **S3DataDistributionType** *(string) --* Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to "FullyReplicated" * **FeaturesAttribute** *(string) --* The attributes of the input data that are the input features. * **InferenceAttribute** *(string) --* The attribute of the input data that represents the ground truth label. * **ProbabilityAttribute** *(string) --* In a classification problem, the attribute that represents the class probability. * **ProbabilityThresholdAttribute** *(float) --* The threshold for the class probability to be evaluated as a positive result. * **StartTimeOffset** *(string) --* If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs. * **EndTimeOffset** *(string) --* If specified, monitoring jobs subtract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs. * **ExcludeFeaturesAttribute** *(string) --* The attributes of the input data to exclude from the analysis. * **GroundTruthS3Input** *(dict) --* **[REQUIRED]** The ground truth label provided for the model. * **S3Uri** *(string) --* The address of the Amazon S3 location of the ground truth labels. * **ModelQualityJobOutputConfig** (*dict*) -- **[REQUIRED]** The output configuration for monitoring jobs. * **MonitoringOutputs** *(list) --* **[REQUIRED]** Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded. * *(dict) --* The output object for a monitoring job. * **S3Output** *(dict) --* **[REQUIRED]** The Amazon S3 storage location where the results of a monitoring job are saved. * **S3Uri** *(string) --* **[REQUIRED]** A URI that identifies the Amazon S3 storage location where Amazon SageMaker AI saves the results of a monitoring job. * **LocalPath** *(string) --* **[REQUIRED]** The local path to the Amazon S3 storage location where Amazon SageMaker AI saves the results of a monitoring job. LocalPath is an absolute path for the output data. * **S3UploadMode** *(string) --* Whether to upload the results of the monitoring job continuously or after the job completes. * **KmsKeyId** *(string) --* The Key Management Service (KMS) key that Amazon SageMaker AI uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. * **JobResources** (*dict*) -- **[REQUIRED]** Identifies the resources to deploy for a monitoring job. * **ClusterConfig** *(dict) --* **[REQUIRED]** The configuration for the cluster resources used to run the processing job. * **InstanceCount** *(integer) --* **[REQUIRED]** The number of ML compute instances to use in the model monitoring job. For distributed processing jobs, specify a value greater than 1. The default value is 1. * **InstanceType** *(string) --* **[REQUIRED]** The ML compute instance type for the processing job. * **VolumeSizeInGB** *(integer) --* **[REQUIRED]** The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario. * **VolumeKmsKeyId** *(string) --* The Key Management Service (KMS) key that Amazon SageMaker AI uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the model monitoring job. * **NetworkConfig** (*dict*) -- Specifies the network configuration for the monitoring job. * **EnableInterContainerTrafficEncryption** *(boolean) --* Whether to encrypt all communications between the instances used for the monitoring jobs. Choose "True" to encrypt communications. Encryption provides greater security for distributed jobs, but the processing might take longer. * **EnableNetworkIsolation** *(boolean) --* Whether to allow inbound and outbound network calls to and from the containers used for the monitoring job. * **VpcConfig** *(dict) --* Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC. * **SecurityGroupIds** *(list) --* **[REQUIRED]** The VPC security group IDs, in the form "sg-xxxxxxxx". Specify the security groups for the VPC that is specified in the "Subnets" field. * *(string) --* * **Subnets** *(list) --* **[REQUIRED]** The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones. * *(string) --* * **RoleArn** (*string*) -- **[REQUIRED]** The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker AI can assume to perform tasks on your behalf. * **StoppingCondition** (*dict*) -- A time limit for how long the monitoring job is allowed to run before stopping. * **MaxRuntimeInSeconds** *(integer) --* **[REQUIRED]** The maximum runtime allowed in seconds. Note: The "MaxRuntimeInSeconds" cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds. * **Tags** (*list*) -- (Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the *Amazon Web Services Billing and Cost Management User Guide*. * *(dict) --* A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources. You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags. For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy. * **Key** *(string) --* **[REQUIRED]** The tag key. Tag keys must be unique per resource. * **Value** *(string) --* **[REQUIRED]** The tag value. Return type: dict Returns: **Response Syntax** { 'JobDefinitionArn': 'string' } **Response Structure** * *(dict) --* * **JobDefinitionArn** *(string) --* The Amazon Resource Name (ARN) of the model quality monitoring job. **Exceptions** * "SageMaker.Client.exceptions.ResourceInUse" * "SageMaker.Client.exceptions.ResourceLimitExceeded" SageMaker / Client / disassociate_trial_component disassociate_trial_component **************************** SageMaker.Client.disassociate_trial_component(**kwargs) Disassociates a trial component from a trial. This doesn't effect other trials the component is associated with. Before you can delete a component, you must disassociate the component from all trials it is associated with. To associate a trial component with a trial, call the AssociateTrialComponent API. To get a list of the trials a component is associated with, use the Search API. Specify "ExperimentTrialComponent" for the "Resource" parameter. The list appears in the response under "Results.TrialComponent.Parents". See also: AWS API Documentation **Request Syntax** response = client.disassociate_trial_component( TrialComponentName='string', TrialName='string' ) Parameters: * **TrialComponentName** (*string*) -- **[REQUIRED]** The name of the component to disassociate from the trial. * **TrialName** (*string*) -- **[REQUIRED]** The name of the trial to disassociate from. Return type: dict Returns: **Response Syntax** { 'TrialComponentArn': 'string', 'TrialArn': 'string' } **Response Structure** * *(dict) --* * **TrialComponentArn** *(string) --* The Amazon Resource Name (ARN) of the trial component. * **TrialArn** *(string) --* The Amazon Resource Name (ARN) of the trial. **Exceptions** * "SageMaker.Client.exceptions.ResourceNotFound" SageMaker / Client / get_paginator get_paginator ************* SageMaker.Client.get_paginator(operation_name) Create a paginator for an operation. Parameters: **operation_name** (*string*) -- The operation name. This is the same name as the method name on the client. For example, if the method name is "create_foo", and you'd normally invoke the operation as "client.create_foo(**kwargs)", if the "create_foo" operation can be paginated, you can use the call "client.get_paginator("create_foo")". Raises: **OperationNotPageableError** -- Raised if the operation is not pageable. You can use the "client.can_paginate" method to check if an operation is pageable. Return type: "botocore.paginate.Paginator" Returns: A paginator object. SageMaker / Client / add_association add_association *************** SageMaker.Client.add_association(**kwargs) Creates an *association* between the source and the destination. A source can be associated with multiple destinations, and a destination can be associated with multiple sources. An association is a lineage tracking entity. For more information, see Amazon SageMaker ML Lineage Tracking. See also: AWS API Documentation **Request Syntax** response = client.add_association( SourceArn='string', DestinationArn='string', AssociationType='ContributedTo'|'AssociatedWith'|'DerivedFrom'|'Produced'|'SameAs' ) Parameters: * **SourceArn** (*string*) -- **[REQUIRED]** The ARN of the source. * **DestinationArn** (*string*) -- **[REQUIRED]** The Amazon Resource Name (ARN) of the destination. * **AssociationType** (*string*) -- The type of association. The following are suggested uses for each type. Amazon SageMaker places no restrictions on their use. * ContributedTo - The source contributed to the destination or had a part in enabling the destination. For example, the training data contributed to the training job. * AssociatedWith - The source is connected to the destination. For example, an approval workflow is associated with a model deployment. * DerivedFrom - The destination is a modification of the source. For example, a digest output of a channel input for a processing job is derived from the original inputs. * Produced - The source generated the destination. For example, a training job produced a model artifact. Return type: dict Returns: **Response Syntax** { 'SourceArn': 'string', 'DestinationArn': 'string' } **Response Structure** * *(dict) --* * **SourceArn** *(string) --* The ARN of the source. * **DestinationArn** *(string) --* The Amazon Resource Name (ARN) of the destination. **Exceptions** * "SageMaker.Client.exceptions.ResourceNotFound" * "SageMaker.Client.exceptions.ResourceLimitExceeded" SageMaker / Client / update_image update_image ************ SageMaker.Client.update_image(**kwargs) Updates the properties of a SageMaker AI image. To change the image's tags, use the AddTags and DeleteTags APIs. See also: AWS API Documentation **Request Syntax** response = client.update_image( DeleteProperties=[ 'string', ], Description='string', DisplayName='string', ImageName='string', RoleArn='string' ) Parameters: * **DeleteProperties** (*list*) -- A list of properties to delete. Only the "Description" and "DisplayName" properties can be deleted. * *(string) --* * **Description** (*string*) -- The new description for the image. * **DisplayName** (*string*) -- The new display name for the image. * **ImageName** (*string*) -- **[REQUIRED]** The name of the image to update. * **RoleArn** (*string*) -- The new ARN for the IAM role that enables Amazon SageMaker AI to perform tasks on your behalf. Return type: dict Returns: **Response Syntax** { 'ImageArn': 'string' } **Response Structure** * *(dict) --* * **ImageArn** *(string) --* The ARN of the image. **Exceptions** * "SageMaker.Client.exceptions.ResourceNotFound" * "SageMaker.Client.exceptions.ResourceInUse" SageMaker / Client / create_cluster create_cluster ************** SageMaker.Client.create_cluster(**kwargs) Creates a SageMaker HyperPod cluster. SageMaker HyperPod is a capability of SageMaker for creating and managing persistent clusters for developing large machine learning models, such as large language models (LLMs) and diffusion models. To learn more, see Amazon SageMaker HyperPod in the *Amazon SageMaker Developer Guide*. See also: AWS API Documentation **Request Syntax** response = client.create_cluster( ClusterName='string', InstanceGroups=[ { 'InstanceCount': 123, 'InstanceGroupName': 'string', 'InstanceType': 'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.c5n.large'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.16xlarge'|'ml.g6.12xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.gr6.4xlarge'|'ml.gr6.8xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.16xlarge'|'ml.g6e.12xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.p5e.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.trn2.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.i3en.large'|'ml.i3en.xlarge'|'ml.i3en.2xlarge'|'ml.i3en.3xlarge'|'ml.i3en.6xlarge'|'ml.i3en.12xlarge'|'ml.i3en.24xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge', 'LifeCycleConfig': { 'SourceS3Uri': 'string', 'OnCreate': 'string' }, 'ExecutionRole': 'string', 'ThreadsPerCore': 123, 'InstanceStorageConfigs': [ { 'EbsVolumeConfig': { 'VolumeSizeInGB': 123 } }, ], 'OnStartDeepHealthChecks': [ 'InstanceStress'|'InstanceConnectivity', ], 'TrainingPlanArn': 'string', 'OverrideVpcConfig': { 'SecurityGroupIds': [ 'string', ], 'Subnets': [ 'string', ] }, 'ScheduledUpdateConfig': { 'ScheduleExpression': 'string', 'DeploymentConfig': { 'RollingUpdatePolicy': { 'MaximumBatchSize': { 'Type': 'INSTANCE_COUNT'|'CAPACITY_PERCENTAGE', 'Value': 123 }, 'RollbackMaximumBatchSize': { 'Type': 'INSTANCE_COUNT'|'CAPACITY_PERCENTAGE', 'Value': 123 } }, 'WaitIntervalInSeconds': 123, 'AutoRollbackConfiguration': [ { 'AlarmName': 'string' }, ] } }, 'ImageId': 'string' }, ], RestrictedInstanceGroups=[ { 'InstanceCount': 123, 'InstanceGroupName': 'string', 'InstanceType': 'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.c5n.large'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.16xlarge'|'ml.g6.12xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.gr6.4xlarge'|'ml.gr6.8xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.16xlarge'|'ml.g6e.12xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.p5e.48xlarge'|'ml.p5en.48xlarge'|'ml.p6-b200.48xlarge'|'ml.trn2.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.i3en.large'|'ml.i3en.xlarge'|'ml.i3en.2xlarge'|'ml.i3en.3xlarge'|'ml.i3en.6xlarge'|'ml.i3en.12xlarge'|'ml.i3en.24xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge', 'ExecutionRole': 'string', 'ThreadsPerCore': 123, 'InstanceStorageConfigs': [ { 'EbsVolumeConfig': { 'VolumeSizeInGB': 123 } }, ], 'OnStartDeepHealthChecks': [ 'InstanceStress'|'InstanceConnectivity', ], 'TrainingPlanArn': 'string', 'OverrideVpcConfig': { 'SecurityGroupIds': [ 'string', ], 'Subnets': [ 'string', ] }, 'ScheduledUpdateConfig': { 'ScheduleExpression': 'string', 'DeploymentConfig': { 'RollingUpdatePolicy': { 'MaximumBatchSize': { 'Type': 'INSTANCE_COUNT'|'CAPACITY_PERCENTAGE', 'Value': 123 }, 'RollbackMaximumBatchSize': { 'Type': 'INSTANCE_COUNT'|'CAPACITY_PERCENTAGE', 'Value': 123 } }, 'WaitIntervalInSeconds': 123, 'AutoRollbackConfiguration': [ { 'AlarmName': 'string' }, ] } }, 'EnvironmentConfig': { 'FSxLustreConfig': { 'SizeInGiB': 123, 'PerUnitStorageThroughput': 123 } } }, ], VpcConfig={ 'SecurityGroupIds': [ 'string', ], 'Subnets': [ 'string', ] }, Tags=[ { 'Key': 'string', 'Value': 'string' }, ], Orchestrator={ 'Eks': { 'ClusterArn': 'string' } }, NodeRecovery='Automatic'|'None', NodeProvisioningMode='Continuous' ) Parameters: * **ClusterName** (*string*) -- **[REQUIRED]** The name for the new SageMaker HyperPod cluster. * **InstanceGroups** (*list*) -- The instance groups to be created in the SageMaker HyperPod cluster. * *(dict) --* The specifications of an instance group that you need to define. * **InstanceCount** *(integer) --* **[REQUIRED]** Specifies the number of instances to add to the instance group of a SageMaker HyperPod cluster. * **InstanceGroupName** *(string) --* **[REQUIRED]** Specifies the name of the instance group. * **InstanceType** *(string) --* **[REQUIRED]** Specifies the instance type of the instance group. * **LifeCycleConfig** *(dict) --* **[REQUIRED]** Specifies the LifeCycle configuration for the instance group. * **SourceS3Uri** *(string) --* **[REQUIRED]** An Amazon S3 bucket path where your lifecycle scripts are stored. Warning: Make sure that the S3 bucket path starts with "s3://sagemaker-". The IAM role for SageMaker HyperPod has the managed AmazonSageMakerClusterInstanceRolePolicy attached, which allows access to S3 buckets with the specific prefix "sagemaker-". * **OnCreate** *(string) --* **[REQUIRED]** The file name of the entrypoint script of lifecycle scripts under "SourceS3Uri". This entrypoint script runs during cluster creation. * **ExecutionRole** *(string) --* **[REQUIRED]** Specifies an IAM execution role to be assumed by the instance group. * **ThreadsPerCore** *(integer) --* Specifies the value for **Threads per core**. For instance types that support multithreading, you can specify "1" for disabling multithreading and "2" for enabling multithreading. For instance types that doesn't support multithreading, specify "1". For more information, see the reference table of CPU cores and threads per CPU core per instance type in the *Amazon Elastic Compute Cloud User Guide*. * **InstanceStorageConfigs** *(list) --* Specifies the additional storage configurations for the instances in the SageMaker HyperPod cluster instance group. * *(dict) --* Defines the configuration for attaching additional storage to the instances in the SageMaker HyperPod cluster instance group. To learn more, see SageMaker HyperPod release notes: June 20, 2024. Note: This is a Tagged Union structure. Only one of the following top level keys can be set: "EbsVolumeConfig". * **EbsVolumeConfig** *(dict) --* Defines the configuration for attaching additional Amazon Elastic Block Store (EBS) volumes to the instances in the SageMaker HyperPod cluster instance group. The additional EBS volume is attached to each instance within the SageMaker HyperPod cluster instance group and mounted to "/opt/sagemaker". * **VolumeSizeInGB** *(integer) --* The size in gigabytes (GB) of the additional EBS volume to be attached to the instances in the SageMaker HyperPod cluster instance group. The additional EBS volume is attached to each instance within the SageMaker HyperPod cluster instance group and mounted to "/opt/sagemaker". * **OnStartDeepHealthChecks** *(list) --* A flag indicating whether deep health checks should be performed when the cluster instance group is created or updated. * *(string) --* * **TrainingPlanArn** *(string) --* The Amazon Resource Name (ARN); of the training plan to use for this cluster instance group. For more information about how to reserve GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see >>``<>``<<. * **OverrideVpcConfig** *(dict) --* To configure multi-AZ deployments, customize the Amazon VPC configuration at the instance group level. You can specify different subnets and security groups across different AZs in the instance group specification to override a SageMaker HyperPod cluster's default Amazon VPC configuration. For more information about deploying a cluster in multiple AZs, see Setting up SageMaker HyperPod clusters across multiple AZs. Note: When your Amazon VPC and subnets support IPv6, network communications differ based on the cluster orchestration platform: * Slurm-orchestrated clusters automatically configure nodes with dual IPv6 and IPv4 addresses, allowing immediate IPv6 network communications. * In Amazon EKS-orchestrated clusters, nodes receive dual-stack addressing, but pods can only use IPv6 when the Amazon EKS cluster is explicitly IPv6-enabled. For information about deploying an IPv6 Amazon EKS cluster, see Amazon EKS IPv6 Cluster Deployment. Additional resources for IPv6 configuration: * For information about adding IPv6 support to your VPC, see to IPv6 Support for VPC. * For information about creating a new IPv6-compatible VPC, see Amazon VPC Creation Guide. * To configure SageMaker HyperPod with a custom Amazon VPC, see Custom Amazon VPC Setup for SageMaker HyperPod. * **SecurityGroupIds** *(list) --* **[REQUIRED]** The VPC security group IDs, in the form "sg-xxxxxxxx". Specify the security groups for the VPC that is specified in the "Subnets" field. * *(string) --* * **Subnets** *(list) --* **[REQUIRED]** The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones. * *(string) --* * **ScheduledUpdateConfig** *(dict) --* The configuration object of the schedule that SageMaker uses to update the AMI. * **ScheduleExpression** *(string) --* **[REQUIRED]** A cron expression that specifies the schedule that SageMaker follows when updating the AMI. * **DeploymentConfig** *(dict) --* The configuration to use when updating the AMI versions. * **RollingUpdatePolicy** *(dict) --* The policy that SageMaker uses when updating the AMI versions of the cluster. * **MaximumBatchSize** *(dict) --* **[REQUIRED]** The maximum amount of instances in the cluster that SageMaker can update at a time. * **Type** *(string) --* **[REQUIRED]** Specifies whether SageMaker should process the update by amount or percentage of instances. * **Value** *(integer) --* **[REQUIRED]** Specifies the amount or percentage of instances SageMaker updates at a time. * **RollbackMaximumBatchSize** *(dict) --* The maximum amount of instances in the cluster that SageMaker can roll back at a time. * **Type** *(string) --* **[REQUIRED]** Specifies whether SageMaker should process the update by amount or percentage of instances. * **Value** *(integer) --* **[REQUIRED]** Specifies the amount or percentage of instances SageMaker updates at a time. * **WaitIntervalInSeconds** *(integer) --* The duration in seconds that SageMaker waits before updating more instances in the cluster. * **AutoRollbackConfiguration** *(list) --* An array that contains the alarms that SageMaker monitors to know whether to roll back the AMI update. * *(dict) --* The details of the alarm to monitor during the AMI update. * **AlarmName** *(string) --* **[REQUIRED]** The name of the alarm. * **ImageId** *(string) --* When configuring your HyperPod cluster, you can specify an image ID using one of the following options: * "HyperPodPublicAmiId": Use a HyperPod public AMI * "CustomAmiId": Use your custom AMI * "default": Use the default latest system image f you choose to use a custom AMI ( "CustomAmiId"), ensure it meets the following requirements: * Encryption: The custom AMI must be unencrypted. * Ownership: The custom AMI must be owned by the same Amazon Web Services account that is creating the HyperPod cluster. * Volume support: Only the primary AMI snapshot volume is supported; additional AMI volumes are not supported. When updating the instance group's AMI through the "UpdateClusterSoftware" operation, if an instance group uses a custom AMI, you must provide an "ImageId" or use the default as input. * **RestrictedInstanceGroups** (*list*) -- The specialized instance groups for training models like Amazon Nova to be created in the SageMaker HyperPod cluster. * *(dict) --* The specifications of a restricted instance group that you need to define. * **InstanceCount** *(integer) --* **[REQUIRED]** Specifies the number of instances to add to the restricted instance group of a SageMaker HyperPod cluster. * **InstanceGroupName** *(string) --* **[REQUIRED]** Specifies the name of the restricted instance group. * **InstanceType** *(string) --* **[REQUIRED]** Specifies the instance type of the restricted instance group. * **ExecutionRole** *(string) --* **[REQUIRED]** Specifies an IAM execution role to be assumed by the restricted instance group. * **ThreadsPerCore** *(integer) --* The number you specified to "TreadsPerCore" in "CreateCluster" for enabling or disabling multithreading. For instance types that support multithreading, you can specify 1 for disabling multithreading and 2 for enabling multithreading. For more information, see the reference table of CPU cores and threads per CPU core per instance type in the *Amazon Elastic Compute Cloud User Guide*. * **InstanceStorageConfigs** *(list) --* Specifies the additional storage configurations for the instances in the SageMaker HyperPod cluster restricted instance group. * *(dict) --* Defines the configuration for attaching additional storage to the instances in the SageMaker HyperPod cluster instance group. To learn more, see SageMaker HyperPod release notes: June 20, 2024. Note: This is a Tagged Union structure. Only one of the following top level keys can be set: "EbsVolumeConfig". * **EbsVolumeConfig** *(dict) --* Defines the configuration for attaching additional Amazon Elastic Block Store (EBS) volumes to the instances in the SageMaker HyperPod cluster instance group. The additional EBS volume is attached to each instance within the SageMaker HyperPod cluster instance group and mounted to "/opt/sagemaker". * **VolumeSizeInGB** *(integer) --* The size in gigabytes (GB) of the additional EBS volume to be attached to the instances in the SageMaker HyperPod cluster instance group. The additional EBS volume is attached to each instance within the SageMaker HyperPod cluster instance group and mounted to "/opt/sagemaker". * **OnStartDeepHealthChecks** *(list) --* A flag indicating whether deep health checks should be performed when the cluster restricted instance group is created or updated. * *(string) --* * **TrainingPlanArn** *(string) --* The Amazon Resource Name (ARN) of the training plan to filter clusters by. For more information about reserving GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see >>``<>``<<. * **OverrideVpcConfig** *(dict) --* Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC. * **SecurityGroupIds** *(list) --* **[REQUIRED]** The VPC security group IDs, in the form "sg-xxxxxxxx". Specify the security groups for the VPC that is specified in the "Subnets" field. * *(string) --* * **Subnets** *(list) --* **[REQUIRED]** The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones. * *(string) --* * **ScheduledUpdateConfig** *(dict) --* The configuration object of the schedule that SageMaker follows when updating the AMI. * **ScheduleExpression** *(string) --* **[REQUIRED]** A cron expression that specifies the schedule that SageMaker follows when updating the AMI. * **DeploymentConfig** *(dict) --* The configuration to use when updating the AMI versions. * **RollingUpdatePolicy** *(dict) --* The policy that SageMaker uses when updating the AMI versions of the cluster. * **MaximumBatchSize** *(dict) --* **[REQUIRED]** The maximum amount of instances in the cluster that SageMaker can update at a time. * **Type** *(string) --* **[REQUIRED]** Specifies whether SageMaker should process the update by amount or percentage of instances. * **Value** *(integer) --* **[REQUIRED]** Specifies the amount or percentage of instances SageMaker updates at a time. * **RollbackMaximumBatchSize** *(dict) --* The maximum amount of instances in the cluster that SageMaker can roll back at a time. * **Type** *(string) --* **[REQUIRED]** Specifies whether SageMaker should process the update by amount or percentage of instances. * **Value** *(integer) --* **[REQUIRED]** Specifies the amount or percentage of instances SageMaker updates at a time. * **WaitIntervalInSeconds** *(integer) --* The duration in seconds that SageMaker waits before updating more instances in the cluster. * **AutoRollbackConfiguration** *(list) --* An array that contains the alarms that SageMaker monitors to know whether to roll back the AMI update. * *(dict) --* The details of the alarm to monitor during the AMI update. * **AlarmName** *(string) --* **[REQUIRED]** The name of the alarm. * **EnvironmentConfig** *(dict) --* **[REQUIRED]** The configuration for the restricted instance groups (RIG) environment. * **FSxLustreConfig** *(dict) --* Configuration settings for an Amazon FSx for Lustre file system to be used with the cluster. * **SizeInGiB** *(integer) --* **[REQUIRED]** The storage capacity of the Amazon FSx for Lustre file system, specified in gibibytes (GiB). * **PerUnitStorageThroughput** *(integer) --* **[REQUIRED]** The throughput capacity of the Amazon FSx for Lustre file system, measured in MB/s per TiB of storage. * **VpcConfig** (*dict*) -- Specifies the Amazon Virtual Private Cloud (VPC) that is associated with the Amazon SageMaker HyperPod cluster. You can control access to and from your resources by configuring your VPC. For more information, see Give SageMaker access to resources in your Amazon VPC. Note: When your Amazon VPC and subnets support IPv6, network communications differ based on the cluster orchestration platform: * Slurm-orchestrated clusters automatically configure nodes with dual IPv6 and IPv4 addresses, allowing immediate IPv6 network communications. * In Amazon EKS-orchestrated clusters, nodes receive dual- stack addressing, but pods can only use IPv6 when the Amazon EKS cluster is explicitly IPv6-enabled. For information about deploying an IPv6 Amazon EKS cluster, see Amazon EKS IPv6 Cluster Deployment. Additional resources for IPv6 configuration: * For information about adding IPv6 support to your VPC, see to IPv6 Support for VPC. * For information about creating a new IPv6-compatible VPC, see Amazon VPC Creation Guide. * To configure SageMaker HyperPod with a custom Amazon VPC, see Custom Amazon VPC Setup for SageMaker HyperPod. * **SecurityGroupIds** *(list) --* **[REQUIRED]** The VPC security group IDs, in the form "sg-xxxxxxxx". Specify the security groups for the VPC that is specified in the "Subnets" field. * *(string) --* * **Subnets** *(list) --* **[REQUIRED]** The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones. * *(string) --* * **Tags** (*list*) -- Custom tags for managing the SageMaker HyperPod cluster as an Amazon Web Services resource. You can add tags to your cluster in the same way you add them in other Amazon Web Services services that support tagging. To learn more about tagging Amazon Web Services resources in general, see Tagging Amazon Web Services Resources User Guide. * *(dict) --* A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources. You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags. For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy. * **Key** *(string) --* **[REQUIRED]** The tag key. Tag keys must be unique per resource. * **Value** *(string) --* **[REQUIRED]** The tag value. * **Orchestrator** (*dict*) -- The type of orchestrator to use for the SageMaker HyperPod cluster. Currently, the only supported value is ""eks"", which is to use an Amazon Elastic Kubernetes Service cluster as the orchestrator. * **Eks** *(dict) --* **[REQUIRED]** The Amazon EKS cluster used as the orchestrator for the SageMaker HyperPod cluster. * **ClusterArn** *(string) --* **[REQUIRED]** The Amazon Resource Name (ARN) of the Amazon EKS cluster associated with the SageMaker HyperPod cluster. * **NodeRecovery** (*string*) -- The node recovery mode for the SageMaker HyperPod cluster. When set to "Automatic", SageMaker HyperPod will automatically reboot or replace faulty nodes when issues are detected. When set to "None", cluster administrators will need to manually manage any faulty cluster instances. * **NodeProvisioningMode** (*string*) -- The mode for provisioning nodes in the cluster. You can specify the following modes: * **Continuous**: Scaling behavior that enables 1) concurrent operation execution within instance groups, 2) continuous retry mechanisms for failed operations, 3) enhanced customer visibility into cluster events through detailed event streams, 4) partial provisioning capabilities. Your clusters and instance groups remain "InService" while scaling. This mode is only supported for EKS orchestrated clusters. Return type: dict Returns: **Response Syntax** { 'ClusterArn': 'string' } **Response Structure** * *(dict) --* * **ClusterArn** *(string) --* The Amazon Resource Name (ARN) of the cluster. **Exceptions** * "SageMaker.Client.exceptions.ResourceInUse" * "SageMaker.Client.exceptions.ResourceLimitExceeded" SageMaker / Client / describe_processing_job describe_processing_job *********************** SageMaker.Client.describe_processing_job(**kwargs) Returns a description of a processing job. See also: AWS API Documentation **Request Syntax** response = client.describe_processing_job( ProcessingJobName='string' ) Parameters: **ProcessingJobName** (*string*) -- **[REQUIRED]** The name of the processing job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account. Return type: dict Returns: **Response Syntax** { 'ProcessingInputs': [ { 'InputName': 'string', 'AppManaged': True|False, 'S3Input': { 'S3Uri': 'string', 'LocalPath': 'string', 'S3DataType': 'ManifestFile'|'S3Prefix', 'S3InputMode': 'Pipe'|'File', 'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key', 'S3CompressionType': 'None'|'Gzip' }, 'DatasetDefinition': { 'AthenaDatasetDefinition': { 'Catalog': 'string', 'Database': 'string', 'QueryString': 'string', 'WorkGroup': 'string', 'OutputS3Uri': 'string', 'KmsKeyId': 'string', 'OutputFormat': 'PARQUET'|'ORC'|'AVRO'|'JSON'|'TEXTFILE', 'OutputCompression': 'GZIP'|'SNAPPY'|'ZLIB' }, 'RedshiftDatasetDefinition': { 'ClusterId': 'string', 'Database': 'string', 'DbUser': 'string', 'QueryString': 'string', 'ClusterRoleArn': 'string', 'OutputS3Uri': 'string', 'KmsKeyId': 'string', 'OutputFormat': 'PARQUET'|'CSV', 'OutputCompression': 'None'|'GZIP'|'BZIP2'|'ZSTD'|'SNAPPY' }, 'LocalPath': 'string', 'DataDistributionType': 'FullyReplicated'|'ShardedByS3Key', 'InputMode': 'Pipe'|'File' } }, ], 'ProcessingOutputConfig': { 'Outputs': [ { 'OutputName': 'string', 'S3Output': { 'S3Uri': 'string', 'LocalPath': 'string', 'S3UploadMode': 'Continuous'|'EndOfJob' }, 'FeatureStoreOutput': { 'FeatureGroupName': 'string' }, 'AppManaged': True|False }, ], 'KmsKeyId': 'string' }, 'ProcessingJobName': 'string', 'ProcessingResources': { 'ClusterConfig': { 'InstanceCount': 123, 'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.8xlarge'|'ml.r5d.12xlarge'|'ml.r5d.16xlarge'|'ml.r5d.24xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge', 'VolumeSizeInGB': 123, 'VolumeKmsKeyId': 'string' } }, 'StoppingCondition': { 'MaxRuntimeInSeconds': 123 }, 'AppSpecification': { 'ImageUri': 'string', 'ContainerEntrypoint': [ 'string', ], 'ContainerArguments': [ 'string', ] }, 'Environment': { 'string': 'string' }, 'NetworkConfig': { 'EnableInterContainerTrafficEncryption': True|False, 'EnableNetworkIsolation': True|False, 'VpcConfig': { 'SecurityGroupIds': [ 'string', ], 'Subnets': [ 'string', ] } }, 'RoleArn': 'string', 'ExperimentConfig': { 'ExperimentName': 'string', 'TrialName': 'string', 'TrialComponentDisplayName': 'string', 'RunName': 'string' }, 'ProcessingJobArn': 'string', 'ProcessingJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped', 'ExitMessage': 'string', 'FailureReason': 'string', 'ProcessingEndTime': datetime(2015, 1, 1), 'ProcessingStartTime': datetime(2015, 1, 1), 'LastModifiedTime': datetime(2015, 1, 1), 'CreationTime': datetime(2015, 1, 1), 'MonitoringScheduleArn': 'string', 'AutoMLJobArn': 'string', 'TrainingJobArn': 'string' } **Response Structure** * *(dict) --* * **ProcessingInputs** *(list) --* The inputs for a processing job. * *(dict) --* The inputs for a processing job. The processing input must specify exactly one of either "S3Input" or "DatasetDefinition" types. * **InputName** *(string) --* The name for the processing job input. * **AppManaged** *(boolean) --* When "True", input operations such as data download are managed natively by the processing job application. When "False" (default), input operations are managed by Amazon SageMaker. * **S3Input** *(dict) --* Configuration for downloading input data from Amazon S3 into the processing container. * **S3Uri** *(string) --* The URI of the Amazon S3 prefix Amazon SageMaker downloads data required to run a processing job. * **LocalPath** *(string) --* The local path in your container where you want Amazon SageMaker to write input data to. "LocalPath" is an absolute path to the input data and must begin with "/opt/ml/processing/". "LocalPath" is a required parameter when "AppManaged" is "False" (default). * **S3DataType** *(string) --* Whether you use an "S3Prefix" or a "ManifestFile" for the data type. If you choose "S3Prefix", "S3Uri" identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for the processing job. If you choose "ManifestFile", "S3Uri" identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for the processing job. * **S3InputMode** *(string) --* Whether to use "File" or "Pipe" input mode. In File mode, Amazon SageMaker copies the data from the input source onto the local ML storage volume before starting your processing container. This is the most commonly used input mode. In "Pipe" mode, Amazon SageMaker streams input data from the source directly to your processing container into named pipes without using the ML storage volume. * **S3DataDistributionType** *(string) --* Whether to distribute the data from Amazon S3 to all processing instances with "FullyReplicated", or whether the data from Amazon S3 is shared by Amazon S3 key, downloading one shard of data to each processing instance. * **S3CompressionType** *(string) --* Whether to GZIP-decompress the data in Amazon S3 as it is streamed into the processing container. "Gzip" can only be used when "Pipe" mode is specified as the "S3InputMode". In "Pipe" mode, Amazon SageMaker streams input data from the source directly to your container without using the EBS volume. * **DatasetDefinition** *(dict) --* Configuration for a Dataset Definition input. * **AthenaDatasetDefinition** *(dict) --* Configuration for Athena Dataset Definition input. * **Catalog** *(string) --* The name of the data catalog used in Athena query execution. * **Database** *(string) --* The name of the database used in the Athena query execution. * **QueryString** *(string) --* The SQL query statements, to be executed. * **WorkGroup** *(string) --* The name of the workgroup in which the Athena query is being started. * **OutputS3Uri** *(string) --* The location in Amazon S3 where Athena query results are stored. * **KmsKeyId** *(string) --* The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data generated from an Athena query execution. * **OutputFormat** *(string) --* The data storage format for Athena query results. * **OutputCompression** *(string) --* The compression used for Athena query results. * **RedshiftDatasetDefinition** *(dict) --* Configuration for Redshift Dataset Definition input. * **ClusterId** *(string) --* The Redshift cluster Identifier. * **Database** *(string) --* The name of the Redshift database used in Redshift query execution. * **DbUser** *(string) --* The database user name used in Redshift query execution. * **QueryString** *(string) --* The SQL query statements to be executed. * **ClusterRoleArn** *(string) --* The IAM role attached to your Redshift cluster that Amazon SageMaker uses to generate datasets. * **OutputS3Uri** *(string) --* The location in Amazon S3 where the Redshift query results are stored. * **KmsKeyId** *(string) --* The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data from a Redshift execution. * **OutputFormat** *(string) --* The data storage format for Redshift query results. * **OutputCompression** *(string) --* The compression used for Redshift query results. * **LocalPath** *(string) --* The local path where you want Amazon SageMaker to download the Dataset Definition inputs to run a processing job. "LocalPath" is an absolute path to the input data. This is a required parameter when "AppManaged" is "False" (default). * **DataDistributionType** *(string) --* Whether the generated dataset is "FullyReplicated" or "ShardedByS3Key" (default). * **InputMode** *(string) --* Whether to use "File" or "Pipe" input mode. In "File" (default) mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode. In "Pipe" mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume. * **ProcessingOutputConfig** *(dict) --* Output configuration for the processing job. * **Outputs** *(list) --* An array of outputs configuring the data to upload from the processing container. * *(dict) --* Describes the results of a processing job. The processing output must specify exactly one of either "S3Output" or "FeatureStoreOutput" types. * **OutputName** *(string) --* The name for the processing job output. * **S3Output** *(dict) --* Configuration for processing job outputs in Amazon S3. * **S3Uri** *(string) --* A URI that identifies the Amazon S3 bucket where you want Amazon SageMaker to save the results of a processing job. * **LocalPath** *(string) --* The local path of a directory where you want Amazon SageMaker to upload its contents to Amazon S3. "LocalPath" is an absolute path to a directory containing output files. This directory will be created by the platform and exist when your container's entrypoint is invoked. * **S3UploadMode** *(string) --* Whether to upload the results of the processing job continuously or after the job completes. * **FeatureStoreOutput** *(dict) --* Configuration for processing job outputs in Amazon SageMaker Feature Store. This processing output type is only supported when "AppManaged" is specified. * **FeatureGroupName** *(string) --* The name of the Amazon SageMaker FeatureGroup to use as the destination for processing job output. Note that your processing script is responsible for putting records into your Feature Store. * **AppManaged** *(boolean) --* When "True", output operations such as data upload are managed natively by the processing job application. When "False" (default), output operations are managed by Amazon SageMaker. * **KmsKeyId** *(string) --* The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the processing job output. "KmsKeyId" can be an ID of a KMS key, ARN of a KMS key, or alias of a KMS key. The "KmsKeyId" is applied to all outputs. * **ProcessingJobName** *(string) --* The name of the processing job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account. * **ProcessingResources** *(dict) --* Identifies the resources, ML compute instances, and ML storage volumes to deploy for a processing job. In distributed training, you specify more than one instance. * **ClusterConfig** *(dict) --* The configuration for the resources in a cluster used to run the processing job. * **InstanceCount** *(integer) --* The number of ML compute instances to use in the processing job. For distributed processing jobs, specify a value greater than 1. The default value is 1. * **InstanceType** *(string) --* The ML compute instance type for the processing job. * **VolumeSizeInGB** *(integer) --* The size of the ML storage volume in gigabytes that you want to provision. You must specify sufficient ML storage for your scenario. Note: Certain Nitro-based instances include local storage with a fixed total size, dependent on the instance type. When using these instances for processing, Amazon SageMaker mounts the local instance storage instead of Amazon EBS gp2 storage. You can't request a "VolumeSizeInGB" greater than the total size of the local instance storage.For a list of instance types that support local instance storage, including the total size per instance type, see Instance Store Volumes. * **VolumeKmsKeyId** *(string) --* The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the processing job. Note: Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a "VolumeKmsKeyId" when using an instance type with local storage.For a list of instance types that support local instance storage, see Instance Store Volumes.For more information about local instance storage encryption, see SSD Instance Store Volumes. * **StoppingCondition** *(dict) --* The time limit for how long the processing job is allowed to run. * **MaxRuntimeInSeconds** *(integer) --* Specifies the maximum runtime in seconds. * **AppSpecification** *(dict) --* Configures the processing job to run a specified container image. * **ImageUri** *(string) --* The container image to be run by the processing job. * **ContainerEntrypoint** *(list) --* The entrypoint for a container used to run a processing job. * *(string) --* * **ContainerArguments** *(list) --* The arguments for a container used to run a processing job. * *(string) --* * **Environment** *(dict) --* The environment variables set in the Docker container. * *(string) --* * *(string) --* * **NetworkConfig** *(dict) --* Networking options for a processing job. * **EnableInterContainerTrafficEncryption** *(boolean) --* Whether to encrypt all communications between distributed processing jobs. Choose "True" to encrypt communications. Encryption provides greater security for distributed processing jobs, but the processing might take longer. * **EnableNetworkIsolation** *(boolean) --* Whether to allow inbound and outbound network calls to and from the containers used for the processing job. * **VpcConfig** *(dict) --* Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC. * **SecurityGroupIds** *(list) --* The VPC security group IDs, in the form "sg-xxxxxxxx". Specify the security groups for the VPC that is specified in the "Subnets" field. * *(string) --* * **Subnets** *(list) --* The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones. * *(string) --* * **RoleArn** *(string) --* The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf. * **ExperimentConfig** *(dict) --* The configuration information used to create an experiment. * **ExperimentName** *(string) --* The name of an existing experiment to associate with the trial component. * **TrialName** *(string) --* The name of an existing trial to associate the trial component with. If not specified, a new trial is created. * **TrialComponentDisplayName** *(string) --* The display name for the trial component. If this key isn't specified, the display name is the trial component name. * **RunName** *(string) --* The name of the experiment run to associate with the trial component. * **ProcessingJobArn** *(string) --* The Amazon Resource Name (ARN) of the processing job. * **ProcessingJobStatus** *(string) --* Provides the status of a processing job. * **ExitMessage** *(string) --* An optional string, up to one KB in size, that contains metadata from the processing container when the processing job exits. * **FailureReason** *(string) --* A string, up to one KB in size, that contains the reason a processing job failed, if it failed. * **ProcessingEndTime** *(datetime) --* The time at which the processing job completed. * **ProcessingStartTime** *(datetime) --* The time at which the processing job started. * **LastModifiedTime** *(datetime) --* The time at which the processing job was last modified. * **CreationTime** *(datetime) --* The time at which the processing job was created. * **MonitoringScheduleArn** *(string) --* The ARN of a monitoring schedule for an endpoint associated with this processing job. * **AutoMLJobArn** *(string) --* The ARN of an AutoML job associated with this processing job. * **TrainingJobArn** *(string) --* The ARN of a training job associated with this processing job. **Exceptions** * "SageMaker.Client.exceptions.ResourceNotFound" SageMaker / Client / list_notebook_instances list_notebook_instances *********************** SageMaker.Client.list_notebook_instances(**kwargs) Returns a list of the SageMaker AI notebook instances in the requester's account in an Amazon Web Services Region. See also: AWS API Documentation **Request Syntax** response = client.list_notebook_instances( NextToken='string', MaxResults=123, SortBy='Name'|'CreationTime'|'Status', SortOrder='Ascending'|'Descending', NameContains='string', CreationTimeBefore=datetime(2015, 1, 1), CreationTimeAfter=datetime(2015, 1, 1), LastModifiedTimeBefore=datetime(2015, 1, 1), LastModifiedTimeAfter=datetime(2015, 1, 1), StatusEquals='Pending'|'InService'|'Stopping'|'Stopped'|'Failed'|'Deleting'|'Updating', NotebookInstanceLifecycleConfigNameContains='string', DefaultCodeRepositoryContains='string', AdditionalCodeRepositoryEquals='string' ) Parameters: * **NextToken** (*string*) -- If the previous call to the "ListNotebookInstances" is truncated, the response includes a "NextToken". You can use this token in your subsequent "ListNotebookInstances" request to fetch the next set of notebook instances. Note: You might specify a filter or a sort order in your request. When response is truncated, you must use the same values for the filer and sort order in the next request. * **MaxResults** (*integer*) -- The maximum number of notebook instances to return. * **SortBy** (*string*) -- The field to sort results by. The default is "Name". * **SortOrder** (*string*) -- The sort order for results. * **NameContains** (*string*) -- A string in the notebook instances' name. This filter returns only notebook instances whose name contains the specified string. * **CreationTimeBefore** (*datetime*) -- A filter that returns only notebook instances that were created before the specified time (timestamp). * **CreationTimeAfter** (*datetime*) -- A filter that returns only notebook instances that were created after the specified time (timestamp). * **LastModifiedTimeBefore** (*datetime*) -- A filter that returns only notebook instances that were modified before the specified time (timestamp). * **LastModifiedTimeAfter** (*datetime*) -- A filter that returns only notebook instances that were modified after the specified time (timestamp). * **StatusEquals** (*string*) -- A filter that returns only notebook instances with the specified status. * **NotebookInstanceLifecycleConfigNameContains** (*string*) -- A string in the name of a notebook instances lifecycle configuration associated with this notebook instance. This filter returns only notebook instances associated with a lifecycle configuration with a name that contains the specified string. * **DefaultCodeRepositoryContains** (*string*) -- A string in the name or URL of a Git repository associated with this notebook instance. This filter returns only notebook instances associated with a git repository with a name that contains the specified string. * **AdditionalCodeRepositoryEquals** (*string*) -- A filter that returns only notebook instances with associated with the specified git repository. Return type: dict Returns: **Response Syntax** { 'NextToken': 'string', 'NotebookInstances': [ { 'NotebookInstanceName': 'string', 'NotebookInstanceArn': 'string', 'NotebookInstanceStatus': 'Pending'|'InService'|'Stopping'|'Stopped'|'Failed'|'Deleting'|'Updating', 'Url': 'string', 'InstanceType': 'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge', 'CreationTime': datetime(2015, 1, 1), 'LastModifiedTime': datetime(2015, 1, 1), 'NotebookInstanceLifecycleConfigName': 'string', 'DefaultCodeRepository': 'string', 'AdditionalCodeRepositories': [ 'string', ] }, ] } **Response Structure** * *(dict) --* * **NextToken** *(string) --* If the response to the previous "ListNotebookInstances" request was truncated, SageMaker AI returns this token. To retrieve the next set of notebook instances, use the token in the next request. * **NotebookInstances** *(list) --* An array of "NotebookInstanceSummary" objects, one for each notebook instance. * *(dict) --* Provides summary information for an SageMaker AI notebook instance. * **NotebookInstanceName** *(string) --* The name of the notebook instance that you want a summary for. * **NotebookInstanceArn** *(string) --* The Amazon Resource Name (ARN) of the notebook instance. * **NotebookInstanceStatus** *(string) --* The status of the notebook instance. * **Url** *(string) --* The URL that you use to connect to the Jupyter notebook running in your notebook instance. * **InstanceType** *(string) --* The type of ML compute instance that the notebook instance is running on. * **CreationTime** *(datetime) --* A timestamp that shows when the notebook instance was created. * **LastModifiedTime** *(datetime) --* A timestamp that shows when the notebook instance was last modified. * **NotebookInstanceLifecycleConfigName** *(string) --* The name of a notebook instance lifecycle configuration associated with this notebook instance. For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance. * **DefaultCodeRepository** *(string) --* The Git repository associated with the notebook instance as its default code repository. This can be either the name of a Git repository stored as a resource in your account, or the URL of a Git repository in Amazon Web Services CodeCommit or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see Associating Git Repositories with SageMaker AI Notebook Instances. * **AdditionalCodeRepositories** *(list) --* An array of up to three Git repositories associated with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in Amazon Web Services CodeCommit or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with SageMaker AI Notebook Instances. * *(string) --* SageMaker / Client / delete_model delete_model ************ SageMaker.Client.delete_model(**kwargs) Deletes a model. The "DeleteModel" API deletes only the model entry that was created in SageMaker when you called the "CreateModel" API. It does not delete model artifacts, inference code, or the IAM role that you specified when creating the model. See also: AWS API Documentation **Request Syntax** response = client.delete_model( ModelName='string' ) Parameters: **ModelName** (*string*) -- **[REQUIRED]** The name of the model to delete. Returns: None SageMaker / Client / list_model_package_groups list_model_package_groups ************************* SageMaker.Client.list_model_package_groups(**kwargs) Gets a list of the model groups in your Amazon Web Services account. See also: AWS API Documentation **Request Syntax** response = client.list_model_package_groups( CreationTimeAfter=datetime(2015, 1, 1), CreationTimeBefore=datetime(2015, 1, 1), MaxResults=123, NameContains='string', NextToken='string', SortBy='Name'|'CreationTime', SortOrder='Ascending'|'Descending', CrossAccountFilterOption='SameAccount'|'CrossAccount' ) Parameters: * **CreationTimeAfter** (*datetime*) -- A filter that returns only model groups created after the specified time. * **CreationTimeBefore** (*datetime*) -- A filter that returns only model groups created before the specified time. * **MaxResults** (*integer*) -- The maximum number of results to return in the response. * **NameContains** (*string*) -- A string in the model group name. This filter returns only model groups whose name contains the specified string. * **NextToken** (*string*) -- If the result of the previous "ListModelPackageGroups" request was truncated, the response includes a "NextToken". To retrieve the next set of model groups, use the token in the next request. * **SortBy** (*string*) -- The field to sort results by. The default is "CreationTime". * **SortOrder** (*string*) -- The sort order for results. The default is "Ascending". * **CrossAccountFilterOption** (*string*) -- A filter that returns either model groups shared with you or model groups in your own account. When the value is "CrossAccount", the results show the resources made discoverable to you from other accounts. When the value is "SameAccount" or "null", the results show resources from your account. The default is "SameAccount". Return type: dict Returns: **Response Syntax** { 'ModelPackageGroupSummaryList': [ { 'ModelPackageGroupName': 'string', 'ModelPackageGroupArn': 'string', 'ModelPackageGroupDescription': 'string', 'CreationTime': datetime(2015, 1, 1), 'ModelPackageGroupStatus': 'Pending'|'InProgress'|'Completed'|'Failed'|'Deleting'|'DeleteFailed' }, ], 'NextToken': 'string' } **Response Structure** * *(dict) --* * **ModelPackageGroupSummaryList** *(list) --* A list of summaries of the model groups in your Amazon Web Services account. * *(dict) --* Summary information about a model group. * **ModelPackageGroupName** *(string) --* The name of the model group. * **ModelPackageGroupArn** *(string) --* The Amazon Resource Name (ARN) of the model group. * **ModelPackageGroupDescription** *(string) --* A description of the model group. * **CreationTime** *(datetime) --* The time that the model group was created. * **ModelPackageGroupStatus** *(string) --* The status of the model group. * **NextToken** *(string) --* If the response is truncated, SageMaker returns this token. To retrieve the next set of model groups, use it in the subsequent request. SageMaker / Client / describe_domain describe_domain *************** SageMaker.Client.describe_domain(**kwargs) The description of the domain. See also: AWS API Documentation **Request Syntax** response = client.describe_domain( DomainId='string' ) Parameters: **DomainId** (*string*) -- **[REQUIRED]** The domain ID. Return type: dict Returns: **Response Syntax** { 'DomainArn': 'string', 'DomainId': 'string', 'DomainName': 'string', 'HomeEfsFileSystemId': 'string', 'SingleSignOnManagedApplicationInstanceId': 'string', 'SingleSignOnApplicationArn': 'string', 'Status': 'Deleting'|'Failed'|'InService'|'Pending'|'Updating'|'Update_Failed'|'Delete_Failed', 'CreationTime': datetime(2015, 1, 1), 'LastModifiedTime': datetime(2015, 1, 1), 'FailureReason': 'string', 'SecurityGroupIdForDomainBoundary': 'string', 'AuthMode': 'SSO'|'IAM', 'DefaultUserSettings': { 'ExecutionRole': 'string', 'SecurityGroups': [ 'string', ], 'SharingSettings': { 'NotebookOutputOption': 'Allowed'|'Disabled', 'S3OutputPath': 'string', 'S3KmsKeyId': 'string' }, 'JupyterServerAppSettings': { 'DefaultResourceSpec': { 'SageMakerImageArn': 'string', 'SageMakerImageVersionArn': 'string', 'SageMakerImageVersionAlias': 'string', 'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge', 'LifecycleConfigArn': 'string' }, 'LifecycleConfigArns': [ 'string', ], 'CodeRepositories': [ { 'RepositoryUrl': 'string' }, ] }, 'KernelGatewayAppSettings': { 'DefaultResourceSpec': { 'SageMakerImageArn': 'string', 'SageMakerImageVersionArn': 'string', 'SageMakerImageVersionAlias': 'string', 'InstanceType': 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'LifecycleConfigArn': 'string' }, 'CustomImages': [ { 'ImageName': 'string', 'ImageVersionNumber': 123, 'AppImageConfigName': 'string' }, ], 'LifecycleConfigArns': [ 'string', ] }, 'TensorBoardAppSettings': { 'DefaultResourceSpec': { 'SageMakerImageArn': 'string', 'SageMakerImageVersionArn': 'string', 'SageMakerImageVersionAlias': 'string', 'InstanceType': 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'LifecycleConfigArn': 'string' }, 'CustomImages': [ { 'ImageName': 'string', 'ImageVersionNumber': 123, 'AppImageConfigName': 'string' }, ], 'LifecycleConfigArns': [ 'string', ] }, 'JupyterLabAppSettings': { 'DefaultResourceSpec': { 'SageMakerImageArn': 'string', 'SageMakerImageVersionArn': 'string', 'SageMakerImageVersionAlias': 'string', 'InstanceType': 'system'|'ml.t3.micro'|'ml.t3.small'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.8xlarge'|'ml.m5d.12xlarge'|'ml.m5d.16xlarge'|'ml.m5d.24xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.geospatial.interactive'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.p5.48xlarge'|'ml.p5en.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.m6id.large'|'ml.m6id.xlarge'|'ml.m6id.2xlarge'|'ml.m6id.4xlarge'|'ml.m6id.8xlarge'|'ml.m6id.12xlarge'|'ml.m6id.16xlarge'|'ml.m6id.24xlarge'|'ml.m6id.32xlarge'|'ml.c6id.large'|'ml.c6id.xlarge'|'ml.c6id.2xlarge'|'ml.c6id.4xlarge'|'ml.c6id.8xlarge'|'ml.c6id.12xlarge'|'ml.c6id.16xlarge'|'ml.c6id.24xlarge'|'ml.c6id.32xlarge'|'ml.r6id.large'|'ml.r6id.xlarge'|'ml.r6id.2xlarge'|'ml.r6id.4xlarge'|'ml.r6id.8xlarge'|'ml.r6id.12xlarge'|'ml.r6id.16xlarge'|'ml.r6id.24xlarge'|'ml.r6id.32xlarge', 'LifecycleConfigArn': 'string' }, 'CustomImages': [ { 'ImageName': 'string', 'ImageVersionNumber': 123, 'AppImageConfigName': 'string' }, ], 'LifecycleConfigArns': [ 'string', ], 'CodeRepositories': [ { 'RepositoryUrl': 'string' }, ], 'AppLifecycleManagement': { 'IdleSettings': { 'LifecycleManagement': 'ENABLED'|'DISABLED', 'IdleTimeoutInMinutes': 123, 'MinIdleTimeoutInMinutes': 123, 'MaxIdleTimeoutInMinutes': 123 } }, 'EmrSettings': { 'AssumableRoleArns': [ 'string', ], 'ExecutionRoleArns': [ 'string', ] }, 'BuiltInLifecycleConfigArn': 'string' }, 'SpaceStorageSettings': { 'DefaultEbsStorageSettings': { 'DefaultEbsVolumeSizeInGb': 123, 'MaximumEbsVolumeSizeInGb': 123 } }, 'CustomPosixUserConfig': { 'Uid': 123, 'Gid': 123 }, 'CustomFileSystemConfigs': [ { 'EFSFileSystemConfig': { 'FileSystemId': 'string', 'FileSystemPath': 'string' }, 'FSxLustreFileSystemConfig': { 'FileSystemId': 'string', 'FileSystemPath': 'string' }, 'S3FileSystemConfig': { 'MountPath': 'string', 'S3Uri': 'string' } }, ] } } **Response Structure** * *(dict) --* * **DomainArn** *(string) --* The domain's Amazon Resource Name (ARN). * **DomainId** *(string) --* The domain ID. * **DomainName** *(string) --* The domain name. * **HomeEfsFileSystemId** *(string) --* The ID of the Amazon Elastic File System managed by this Domain. * **SingleSignOnManagedApplicationInstanceId** *(string) --* The IAM Identity Center managed application instance ID. * **SingleSignOnApplicationArn** *(string) --* The ARN of the application managed by SageMaker AI in IAM Identity Center. This value is only returned for domains created after October 1, 2023. * **Status** *(string) --* The status. * **CreationTime** *(datetime) --* The creation time. * **LastModifiedTime** *(datetime) --* The last modified time. * **FailureReason** *(string) --* The failure reason. * **SecurityGroupIdForDomainBoundary** *(string) --* The ID of the security group that authorizes traffic between the "RSessionGateway" apps and the "RStudioServerPro" app. * **AuthMode** *(string) --* The domain's authentication mode. * **DefaultUserSettings** *(dict) --* Settings which are applied to UserProfiles in this domain if settings are not explicitly specified in a given UserProfile. * **ExecutionRole** *(string) --* The execution role for the user. SageMaker applies this setting only to private spaces that the user creates in the domain. SageMaker doesn't apply this setting to shared spaces. * **SecurityGroups** *(list) --* The security groups for the Amazon Virtual Private Cloud (VPC) that the domain uses for communication. Optional when the "CreateDomain.AppNetworkAccessType" parameter is set to "PublicInternetOnly". Required when the "CreateDomain.AppNetworkAccessType" parameter is set to "VpcOnly", unless specified as part of the "DefaultUserSettings" for the domain. Amazon SageMaker AI adds a security group to allow NFS traffic from Amazon SageMaker AI Studio. Therefore, the number of security groups that you can specify is one less than the maximum number shown. SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces. * *(string) --* * **SharingSettings** *(dict) --* Specifies options for sharing Amazon SageMaker AI Studio notebooks. * **NotebookOutputOption** *(string) --* Whether to include the notebook cell output when sharing the notebook. The default is "Disabled". * **S3OutputPath** *(string) --* When "NotebookOutputOption" is "Allowed", the Amazon S3 bucket used to store the shared notebook snapshots. * **S3KmsKeyId** *(string) --* When "NotebookOutputOption" is "Allowed", the Amazon Web Services Key Management Service (KMS) encryption key ID used to encrypt the notebook cell output in the Amazon S3 bucket. * **JupyterServerAppSettings** *(dict) --* The Jupyter server's app settings. * **DefaultResourceSpec** *(dict) --* The default instance type and the Amazon Resource Name (ARN) of the default SageMaker AI image used by the JupyterServer app. If you use the "LifecycleConfigArns" parameter, then this parameter is also required. * **SageMakerImageArn** *(string) --* The ARN of the SageMaker AI image that the image version belongs to. * **SageMakerImageVersionArn** *(string) --* The ARN of the image version created on the instance. To clear the value set for "SageMakerImageVersionArn", pass "None" as the value. * **SageMakerImageVersionAlias** *(string) --* The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format. * **InstanceType** *(string) --* The instance type that the image version runs on. Note: **JupyterServer apps** only support the "system" value.For **KernelGateway apps**, the "system" value is translated to "ml.t3.medium". KernelGateway apps also support all other values for available instance types. * **LifecycleConfigArn** *(string) --* The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource. * **LifecycleConfigArns** *(list) --* The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the "DefaultResourceSpec" parameter is also required. Note: To remove a Lifecycle Config, you must set "LifecycleConfigArns" to an empty list. * *(string) --* * **CodeRepositories** *(list) --* A list of Git repositories that SageMaker AI automatically displays to users for cloning in the JupyterServer application. * *(dict) --* A Git repository that SageMaker AI automatically displays to users for cloning in the JupyterServer application. * **RepositoryUrl** *(string) --* The URL of the Git repository. * **KernelGatewayAppSettings** *(dict) --* The kernel gateway app settings. * **DefaultResourceSpec** *(dict) --* The default instance type and the Amazon Resource Name (ARN) of the default SageMaker AI image used by the KernelGateway app. Note: The Amazon SageMaker AI Studio UI does not use the default instance type value set here. The default instance type set here is used when Apps are created using the CLI or CloudFormation and the instance type parameter value is not passed. * **SageMakerImageArn** *(string) --* The ARN of the SageMaker AI image that the image version belongs to. * **SageMakerImageVersionArn** *(string) --* The ARN of the image version created on the instance. To clear the value set for "SageMakerImageVersionArn", pass "None" as the value. * **SageMakerImageVersionAlias** *(string) --* The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format. * **InstanceType** *(string) --* The instance type that the image version runs on. Note: **JupyterServer apps** only support the "system" value.For **KernelGateway apps**, the "system" value is translated to "ml.t3.medium". KernelGateway apps also support all other values for available instance types. * **LifecycleConfigArn** *(string) --* The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource. * **CustomImages** *(list) --* A list of custom SageMaker AI images that are configured to run as a KernelGateway app. The maximum number of custom images are as follows. * On a domain level: 200 * On a space level: 5 * On a user profile level: 5 * *(dict) --* A custom SageMaker AI image. For more information, see Bring your own SageMaker AI image. * **ImageName** *(string) --* The name of the CustomImage. Must be unique to your account. * **ImageVersionNumber** *(integer) --* The version number of the CustomImage. * **AppImageConfigName** *(string) --* The name of the AppImageConfig. * **LifecycleConfigArns** *(list) --* The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain. Note: To remove a Lifecycle Config, you must set "LifecycleConfigArns" to an empty list. * *(string) --* * **TensorBoardAppSettings** *(dict) --* The TensorBoard app settings. * **DefaultResourceSpec** *(dict) --* The default instance type and the Amazon Resource Name (ARN) of the SageMaker AI image created on the instance. * **SageMakerImageArn** *(string) --* The ARN of the SageMaker AI image that the image version belongs to. * **SageMakerImageVersionArn** *(string) --* The ARN of the image version created on the instance. To clear the value set for "SageMakerImageVersionArn", pass "None" as the value. * **SageMakerImageVersionAlias** *(string) --* The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format. * **InstanceType** *(string) --* The instance type that the image version runs on. Note: **JupyterServer apps** only support the "system" value.For **KernelGateway apps**, the "system" value is translated to "ml.t3.medium". KernelGateway apps also support all other values for available instance types. * **LifecycleConfigArn** *(string) --* The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource. * **RStudioServerProAppSettings** *(dict) --* A collection of settings that configure user interaction with the "RStudioServerPro" app. * **AccessStatus** *(string) --* Indicates whether the current user has access to the "RStudioServerPro" app. * **UserGroup** *(string) --* The level of permissions that the user has within the "RStudioServerPro" app. This value defaults to *User*. The *Admin* value allows the user access to the RStudio Administrative Dashboard. * **RSessionAppSettings** *(dict) --* A collection of settings that configure the "RSessionGateway" app. * **DefaultResourceSpec** *(dict) --* Specifies the ARN's of a SageMaker AI image and SageMaker AI image version, and the instance type that the version runs on. Note: When both "SageMakerImageVersionArn" and "SageMakerImageArn" are passed, "SageMakerImageVersionArn" is used. Any updates to "SageMakerImageArn" will not take effect if "SageMakerImageVersionArn" already exists in the "ResourceSpec" because "SageMakerImageVersionArn" always takes precedence. To clear the value set for "SageMakerImageVersionArn", pass "None" as the value. * **SageMakerImageArn** *(string) --* The ARN of the SageMaker AI image that the image version belongs to. * **SageMakerImageVersionArn** *(string) --* The ARN of the image version created on the instance. To clear the value set for "SageMakerImageVersionArn", pass "None" as the value. * **SageMakerImageVersionAlias** *(string) --* The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format. * **InstanceType** *(string) --* The instance type that the image version runs on. Note: **JupyterServer apps** only support the "system" value.For **KernelGateway apps**, the "system" value is translated to "ml.t3.medium". KernelGateway apps also support all other values for available instance types. * **LifecycleConfigArn** *(string) --* The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource. * **CustomImages** *(list) --* A list of custom SageMaker AI images that are configured to run as a RSession app. * *(dict) --* A custom SageMaker AI image. For more information, see Bring your own SageMaker AI image. * **ImageName** *(string) --* The name of the CustomImage. Must be unique to your account. * **ImageVersionNumber** *(integer) --* The version number of the CustomImage. * **AppImageConfigName** *(string) --* The name of the AppImageConfig. * **CanvasAppSettings** *(dict) --* The Canvas app settings. SageMaker applies these settings only to private spaces that SageMaker creates for the Canvas app. * **TimeSeriesForecastingSettings** *(dict) --* Time series forecast settings for the SageMaker Canvas application. * **Status** *(string) --* Describes whether time series forecasting is enabled or disabled in the Canvas application. * **AmazonForecastRoleArn** *(string) --* The IAM role that Canvas passes to Amazon Forecast for time series forecasting. By default, Canvas uses the execution role specified in the "UserProfile" that launches the Canvas application. If an execution role is not specified in the "UserProfile", Canvas uses the execution role specified in the Domain that owns the "UserProfile". To allow time series forecasting, this IAM role should have the AmazonSageMakerCanvasForecastAccess policy attached and "forecast.amazonaws.com" added in the trust relationship as a service principal. * **ModelRegisterSettings** *(dict) --* The model registry settings for the SageMaker Canvas application. * **Status** *(string) --* Describes whether the integration to the model registry is enabled or disabled in the Canvas application. * **CrossAccountModelRegisterRoleArn** *(string) --* The Amazon Resource Name (ARN) of the SageMaker model registry account. Required only to register model versions created by a different SageMaker Canvas Amazon Web Services account than the Amazon Web Services account in which SageMaker model registry is set up. * **WorkspaceSettings** *(dict) --* The workspace settings for the SageMaker Canvas application. * **S3ArtifactPath** *(string) --* The Amazon S3 bucket used to store artifacts generated by Canvas. Updating the Amazon S3 location impacts existing configuration settings, and Canvas users no longer have access to their artifacts. Canvas users must log out and log back in to apply the new location. * **S3KmsKeyId** *(string) --* The Amazon Web Services Key Management Service (KMS) encryption key ID that is used to encrypt artifacts generated by Canvas in the Amazon S3 bucket. * **IdentityProviderOAuthSettings** *(list) --* The settings for connecting to an external data source with OAuth. * *(dict) --* The Amazon SageMaker Canvas application setting where you configure OAuth for connecting to an external data source, such as Snowflake. * **DataSourceName** *(string) --* The name of the data source that you're connecting to. Canvas currently supports OAuth for Snowflake and Salesforce Data Cloud. * **Status** *(string) --* Describes whether OAuth for a data source is enabled or disabled in the Canvas application. * **SecretArn** *(string) --* The ARN of an Amazon Web Services Secrets Manager secret that stores the credentials from your identity provider, such as the client ID and secret, authorization URL, and token URL. * **DirectDeploySettings** *(dict) --* The model deployment settings for the SageMaker Canvas application. * **Status** *(string) --* Describes whether model deployment permissions are enabled or disabled in the Canvas application. * **KendraSettings** *(dict) --* The settings for document querying. * **Status** *(string) --* Describes whether the document querying feature is enabled or disabled in the Canvas application. * **GenerativeAiSettings** *(dict) --* The generative AI settings for the SageMaker Canvas application. * **AmazonBedrockRoleArn** *(string) --* The ARN of an Amazon Web Services IAM role that allows fine-tuning of large language models (LLMs) in Amazon Bedrock. The IAM role should have Amazon S3 read and write permissions, as well as a trust relationship that establishes "bedrock.amazonaws.com" as a service principal. * **EmrServerlessSettings** *(dict) --* The settings for running Amazon EMR Serverless data processing jobs in SageMaker Canvas. * **ExecutionRoleArn** *(string) --* The Amazon Resource Name (ARN) of the Amazon Web Services IAM role that is assumed for running Amazon EMR Serverless jobs in SageMaker Canvas. This role should have the necessary permissions to read and write data attached and a trust relationship with EMR Serverless. * **Status** *(string) --* Describes whether Amazon EMR Serverless job capabilities are enabled or disabled in the SageMaker Canvas application. * **CodeEditorAppSettings** *(dict) --* The Code Editor application settings. SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces. * **DefaultResourceSpec** *(dict) --* Specifies the ARN's of a SageMaker AI image and SageMaker AI image version, and the instance type that the version runs on. Note: When both "SageMakerImageVersionArn" and "SageMakerImageArn" are passed, "SageMakerImageVersionArn" is used. Any updates to "SageMakerImageArn" will not take effect if "SageMakerImageVersionArn" already exists in the "ResourceSpec" because "SageMakerImageVersionArn" always takes precedence. To clear the value set for "SageMakerImageVersionArn", pass "None" as the value. * **SageMakerImageArn** *(string) --* The ARN of the SageMaker AI image that the image version belongs to. * **SageMakerImageVersionArn** *(string) --* The ARN of the image version created on the instance. To clear the value set for "SageMakerImageVersionArn", pass "None" as the value. * **SageMakerImageVersionAlias** *(string) --* The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format. * **InstanceType** *(string) --* The instance type that the image version runs on. Note: **JupyterServer apps** only support the "system" value.For **KernelGateway apps**, the "system" value is translated to "ml.t3.medium". KernelGateway apps also support all other values for available instance types. * **LifecycleConfigArn** *(string) --* The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource. * **CustomImages** *(list) --* A list of custom SageMaker images that are configured to run as a Code Editor app. * *(dict) --* A custom SageMaker AI image. For more information, see Bring your own SageMaker AI image. * **ImageName** *(string) --* The name of the CustomImage. Must be unique to your account. * **ImageVersionNumber** *(integer) --* The version number of the CustomImage. * **AppImageConfigName** *(string) --* The name of the AppImageConfig. * **LifecycleConfigArns** *(list) --* The Amazon Resource Name (ARN) of the Code Editor application lifecycle configuration. * *(string) --* * **AppLifecycleManagement** *(dict) --* Settings that are used to configure and manage the lifecycle of CodeEditor applications. * **IdleSettings** *(dict) --* Settings related to idle shutdown of Studio applications. * **LifecycleManagement** *(string) --* Indicates whether idle shutdown is activated for the application type. * **IdleTimeoutInMinutes** *(integer) --* The time that SageMaker waits after the application becomes idle before shutting it down. * **MinIdleTimeoutInMinutes** *(integer) --* The minimum value in minutes that custom idle shutdown can be set to by the user. * **MaxIdleTimeoutInMinutes** *(integer) --* The maximum value in minutes that custom idle shutdown can be set to by the user. * **BuiltInLifecycleConfigArn** *(string) --* The lifecycle configuration that runs before the default lifecycle configuration. It can override changes made in the default lifecycle configuration. * **JupyterLabAppSettings** *(dict) --* The settings for the JupyterLab application. SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces. * **DefaultResourceSpec** *(dict) --* Specifies the ARN's of a SageMaker AI image and SageMaker AI image version, and the instance type that the version runs on. Note: When both "SageMakerImageVersionArn" and "SageMakerImageArn" are passed, "SageMakerImageVersionArn" is used. Any updates to "SageMakerImageArn" will not take effect if "SageMakerImageVersionArn" already exists in the "ResourceSpec" because "SageMakerImageVersionArn" always takes precedence. To clear the value set for "SageMakerImageVersionArn", pass "None" as the value. * **SageMakerImageArn** *(string) --* The ARN of the SageMaker AI image that the image version belongs to. * **SageMakerImageVersionArn** *(string) --* The ARN of the image version created on the instance. To clear the value set for "SageMakerImageVersionArn", pass "None" as the value. * **SageMakerImageVersionAlias** *(string) --* The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format. * **InstanceType** *(string) --* The instance type that the image version runs on. Note: **JupyterServer apps** only support the "system" value.For **KernelGateway apps**, the "system" value is translated to "ml.t3.medium". KernelGateway apps also support all other values for available instance types. * **LifecycleConfigArn** *(string) --* The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource. * **CustomImages** *(list) --* A list of custom SageMaker images that are configured to run as a JupyterLab app. * *(dict) --* A custom SageMaker AI image. For more information, see Bring your own SageMaker AI image. * **ImageName** *(string) --* The name of the CustomImage. Must be unique to your account. * **ImageVersionNumber** *(integer) --* The version number of the CustomImage. * **AppImageConfigName** *(string) --* The name of the AppImageConfig. * **LifecycleConfigArns** *(list) --* The Amazon Resource Name (ARN) of the lifecycle configurations attached to the user profile or domain. To remove a lifecycle config, you must set "LifecycleConfigArns" to an empty list. * *(string) --* * **CodeRepositories** *(list) --* A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterLab application. * *(dict) --* A Git repository that SageMaker AI automatically displays to users for cloning in the JupyterServer application. * **RepositoryUrl** *(string) --* The URL of the Git repository. * **AppLifecycleManagement** *(dict) --* Indicates whether idle shutdown is activated for JupyterLab applications. * **IdleSettings** *(dict) --* Settings related to idle shutdown of Studio applications. * **LifecycleManagement** *(string) --* Indicates whether idle shutdown is activated for the application type. * **IdleTimeoutInMinutes** *(integer) --* The time that SageMaker waits after the application becomes idle before shutting it down. * **MinIdleTimeoutInMinutes** *(integer) --* The minimum value in minutes that custom idle shutdown can be set to by the user. * **MaxIdleTimeoutInMinutes** *(integer) --* The maximum value in minutes that custom idle shutdown can be set to by the user. * **EmrSettings** *(dict) --* The configuration parameters that specify the IAM roles assumed by the execution role of SageMaker (assumable roles) and the cluster instances or job execution environments (execution roles or runtime roles) to manage and access resources required for running Amazon EMR clusters or Amazon EMR Serverless applications. * **AssumableRoleArns** *(list) --* An array of Amazon Resource Names (ARNs) of the IAM roles that the execution role of SageMaker can assume for performing operations or tasks related to Amazon EMR clusters or Amazon EMR Serverless applications. These roles define the permissions and access policies required when performing Amazon EMR-related operations, such as listing, connecting to, or terminating Amazon EMR clusters or Amazon EMR Serverless applications. They are typically used in cross-account access scenarios, where the Amazon EMR resources (clusters or serverless applications) are located in a different Amazon Web Services account than the SageMaker domain. * *(string) --* * **ExecutionRoleArns** *(list) --* An array of Amazon Resource Names (ARNs) of the IAM roles used by the Amazon EMR cluster instances or job execution environments to access other Amazon Web Services services and resources needed during the runtime of your Amazon EMR or Amazon EMR Serverless workloads, such as Amazon S3 for data access, Amazon CloudWatch for logging, or other Amazon Web Services services based on the particular workload requirements. * *(string) --* * **BuiltInLifecycleConfigArn** *(string) --* The lifecycle configuration that runs before the default lifecycle configuration. It can override changes made in the default lifecycle configuration. * **SpaceStorageSettings** *(dict) --* The storage settings for a space. SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces. * **DefaultEbsStorageSettings** *(dict) --* The default EBS storage settings for a space. * **DefaultEbsVolumeSizeInGb** *(integer) --* The default size of the EBS storage volume for a space. * **MaximumEbsVolumeSizeInGb** *(integer) --* The maximum size of the EBS storage volume for a space. * **DefaultLandingUri** *(string) --* The default experience that the user is directed to when accessing the domain. The supported values are: * "studio::": Indicates that Studio is the default experience. This value can only be passed if "StudioWebPortal" is set to "ENABLED". * "app:JupyterServer:": Indicates that Studio Classic is the default experience. * **StudioWebPortal** *(string) --* Whether the user can access Studio. If this value is set to "DISABLED", the user cannot access Studio, even if that is the default experience for the domain. * **CustomPosixUserConfig** *(dict) --* Details about the POSIX identity that is used for file system operations. SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces. * **Uid** *(integer) --* The POSIX user ID. * **Gid** *(integer) --* The POSIX group ID. * **CustomFileSystemConfigs** *(list) --* The settings for assigning a custom file system to a user profile. Permitted users can access this file system in Amazon SageMaker AI Studio. SageMaker applies these settings only to private spaces that the user creates in the domain. SageMaker doesn't apply these settings to shared spaces. * *(dict) --* The settings for assigning a custom file system to a user profile or space for an Amazon SageMaker AI Domain. Permitted users can access this file system in Amazon SageMaker AI Studio. Note: This is a Tagged Union structure. Only one of the following top level keys will be set: "EFSFileSystemConfig", "FSxLustreFileSystemConfig", "S3FileSystemConfig". If a client receives an unknown member it will set "SDK_UNKNOWN_MEMBER" as the top level key, which maps to the name or tag of the unknown member. The structure of "SDK_UNKNOWN_MEMBER" is as follows: 'SDK_UNKNOWN_MEMBER': {'name': 'UnknownMemberName'} * **EFSFileSystemConfig** *(dict) --* The settings for a custom Amazon EFS file system. * **FileSystemId** *(string) --* The ID of your Amazon EFS file system. * **FileSystemPath** *(string) --* The path to the file system directory that is accessible in Amazon SageMaker AI Studio. Permitted users can access only this directory and below. * **FSxLustreFileSystemConfig** *(dict) --* The settings for a custom Amazon FSx for Lustre file system. * **FileSystemId** *(string) --* The globally unique, 17-digit, ID of the file system, assigned by Amazon FSx for Lustre. * **FileSystemPath** *(string) --* The path to the file system directory that is accessible in Amazon SageMaker Studio. Permitted users can access only this directory and below. * **S3FileSystemConfig** *(dict) --* Configuration settings for a custom Amazon S3 file system. * **MountPath** *(string) --* The file system path where the Amazon S3 storage location will be mounted within the Amazon SageMaker Studio environment. * **S3Uri** *(string) --* The Amazon S3 URI of the S3 file system configuration. * **StudioWebPortalSettings** *(dict) --* Studio settings. If these settings are applied on a user level, they take priority over the settings applied on a domain level. * **HiddenMlTools** *(list) --* The machine learning tools that are hidden from the Studio left navigation pane. * *(string) --* * **HiddenAppTypes** *(list) --* The Applications supported in Studio that are hidden from the Studio left navigation pane. * *(string) --* * **HiddenInstanceTypes** *(list) --* The instance types you are hiding from the Studio user interface. * *(string) --* * **HiddenSageMakerImageVersionAliases** *(list) --* The version aliases you are hiding from the Studio user interface. * *(dict) --* The SageMaker images that are hidden from the Studio user interface. You must specify the SageMaker image name and version aliases. * **SageMakerImageName** *(string) --* The SageMaker image name that you are hiding from the Studio user interface. * **VersionAliases** *(list) --* The version aliases you are hiding from the Studio user interface. * *(string) --* * **AutoMountHomeEFS** *(string) --* Indicates whether auto-mounting of an EFS volume is supported for the user profile. The "DefaultAsDomain" value is only supported for user profiles. Do not use the "DefaultAsDomain" value when setting this parameter for a domain. SageMaker applies this setting only to private spaces that the user creates in the domain. SageMaker doesn't apply this setting to shared spaces. * **DomainSettings** *(dict) --* A collection of "Domain" settings. * **SecurityGroupIds** *(list) --* The security groups for the Amazon Virtual Private Cloud that the "Domain" uses for communication between Domain- level apps and user apps. * *(string) --* * **RStudioServerProDomainSettings** *(dict) --* A collection of settings that configure the "RStudioServerPro" Domain-level app. * **DomainExecutionRoleArn** *(string) --* The ARN of the execution role for the "RStudioServerPro" Domain-level app. * **RStudioConnectUrl** *(string) --* A URL pointing to an RStudio Connect server. * **RStudioPackageManagerUrl** *(string) --* A URL pointing to an RStudio Package Manager server. * **DefaultResourceSpec** *(dict) --* Specifies the ARN's of a SageMaker AI image and SageMaker AI image version, and the instance type that the version runs on. Note: When both "SageMakerImageVersionArn" and "SageMakerImageArn" are passed, "SageMakerImageVersionArn" is used. Any updates to "SageMakerImageArn" will not take effect if "SageMakerImageVersionArn" already exists in the "ResourceSpec" because "SageMakerImageVersionArn" always takes precedence. To clear the value set for "SageMakerImageVersionArn", pass "None" as the value. * **SageMakerImageArn** *(string) --* The ARN of the SageMaker AI image that the image version belongs to. * **SageMakerImageVersionArn** *(string) --* The ARN of the image version created on the instance. To clear the value set for "SageMakerImageVersionArn", pass "None" as the value. * **SageMakerImageVersionAlias** *(string) --* The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format. * **InstanceType** *(string) --* The instance type that the image version runs on. Note: **JupyterServer apps** only support the "system" value.For **KernelGateway apps**, the "system" value is translated to "ml.t3.medium". KernelGateway apps also support all other values for available instance types. * **LifecycleConfigArn** *(string) --* The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource. * **ExecutionRoleIdentityConfig** *(string) --* The configuration for attaching a SageMaker AI user profile name to the execution role as a sts:SourceIdentity key. * **DockerSettings** *(dict) --* A collection of settings that configure the domain's Docker interaction. * **EnableDockerAccess** *(string) --* Indicates whether the domain can access Docker. * **VpcOnlyTrustedAccounts** *(list) --* The list of Amazon Web Services accounts that are trusted when the domain is created in VPC-only mode. * *(string) --* * **AmazonQSettings** *(dict) --* A collection of settings that configure the Amazon Q experience within the domain. The "AuthMode" that you use to create the domain must be "SSO". * **Status** *(string) --* Whether Amazon Q has been enabled within the domain. * **QProfileArn** *(string) --* The ARN of the Amazon Q profile used within the domain. * **UnifiedStudioSettings** *(dict) --* The settings that apply to an SageMaker AI domain when you use it in Amazon SageMaker Unified Studio. * **StudioWebPortalAccess** *(string) --* Sets whether you can access the domain in Amazon SageMaker Studio: ENABLED You can access the domain in Amazon SageMaker Studio. If you migrate the domain to Amazon SageMaker Unified Studio, you can access it in both studio interfaces. DISABLED You can't access the domain in Amazon SageMaker Studio. If you migrate the domain to Amazon SageMaker Unified Studio, you can access it only in that studio interface. To migrate a domain to Amazon SageMaker Unified Studio, you specify the UnifiedStudioSettings data type when you use the UpdateDomain action. * **DomainAccountId** *(string) --* The ID of the Amazon Web Services account that has the Amazon SageMaker Unified Studio domain. The default value, if you don't specify an ID, is the ID of the account that has the Amazon SageMaker AI domain. * **DomainRegion** *(string) --* The Amazon Web Services Region where the domain is located in Amazon SageMaker Unified Studio. The default value, if you don't specify a Region, is the Region where the Amazon SageMaker AI domain is located. * **DomainId** *(string) --* The ID of the Amazon SageMaker Unified Studio domain associated with this domain. * **ProjectId** *(string) --* The ID of the Amazon SageMaker Unified Studio project that corresponds to the domain. * **EnvironmentId** *(string) --* The ID of the environment that Amazon SageMaker Unified Studio associates with the domain. * **ProjectS3Path** *(string) --* The location where Amazon S3 stores temporary execution data and other artifacts for the project that corresponds to the domain. * **SingleSignOnApplicationArn** *(string) --* The ARN of the Amazon DataZone application managed by Amazon SageMaker Unified Studio in the Amazon Web Services IAM Identity Center. * **AppNetworkAccessType** *(string) --* Specifies the VPC used for non-EFS traffic. The default value is "PublicInternetOnly". * "PublicInternetOnly" - Non-EFS traffic is through a VPC managed by Amazon SageMaker AI, which allows direct internet access * "VpcOnly" - All traffic is through the specified VPC and subnets * **HomeEfsFileSystemKmsKeyId** *(string) --* Use "KmsKeyId". * **SubnetIds** *(list) --* The VPC subnets that the domain uses for communication. * *(string) --* * **Url** *(string) --* The domain's URL. * **VpcId** *(string) --* The ID of the Amazon Virtual Private Cloud (VPC) that the domain uses for communication. * **KmsKeyId** *(string) --* The Amazon Web Services KMS customer managed key used to encrypt the EFS volume attached to the domain. * **AppSecurityGroupManagement** *(string) --* The entity that creates and manages the required security groups for inter-app communication in "VPCOnly" mode. Required when "CreateDomain.AppNetworkAccessType" is "VPCOnly" and "DomainSettings.RStudioServerProDomainSetting s.DomainExecutionRoleArn" is provided. * **TagPropagation** *(string) --* Indicates whether custom tag propagation is supported for the domain. * **DefaultSpaceSettings** *(dict) --* The default settings for shared spaces that users create in the domain. * **ExecutionRole** *(string) --* The ARN of the execution role for the space. * **SecurityGroups** *(list) --* The security group IDs for the Amazon VPC that the space uses for communication. * *(string) --* * **JupyterServerAppSettings** *(dict) --* The JupyterServer app settings. * **DefaultResourceSpec** *(dict) --* The default instance type and the Amazon Resource Name (ARN) of the default SageMaker AI image used by the JupyterServer app. If you use the "LifecycleConfigArns" parameter, then this parameter is also required. * **SageMakerImageArn** *(string) --* The ARN of the SageMaker AI image that the image version belongs to. * **SageMakerImageVersionArn** *(string) --* The ARN of the image version created on the instance. To clear the value set for "SageMakerImageVersionArn", pass "None" as the value. * **SageMakerImageVersionAlias** *(string) --* The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format. * **InstanceType** *(string) --* The instance type that the image version runs on. Note: **JupyterServer apps** only support the "system" value.For **KernelGateway apps**, the "system" value is translated to "ml.t3.medium". KernelGateway apps also support all other values for available instance types. * **LifecycleConfigArn** *(string) --* The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource. * **LifecycleConfigArns** *(list) --* The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the "DefaultResourceSpec" parameter is also required. Note: To remove a Lifecycle Config, you must set "LifecycleConfigArns" to an empty list. * *(string) --* * **CodeRepositories** *(list) --* A list of Git repositories that SageMaker AI automatically displays to users for cloning in the JupyterServer application. * *(dict) --* A Git repository that SageMaker AI automatically displays to users for cloning in the JupyterServer application. * **RepositoryUrl** *(string) --* The URL of the Git repository. * **KernelGatewayAppSettings** *(dict) --* The KernelGateway app settings. * **DefaultResourceSpec** *(dict) --* The default instance type and the Amazon Resource Name (ARN) of the default SageMaker AI image used by the KernelGateway app. Note: The Amazon SageMaker AI Studio UI does not use the default instance type value set here. The default instance type set here is used when Apps are created using the CLI or CloudFormation and the instance type parameter value is not passed. * **SageMakerImageArn** *(string) --* The ARN of the SageMaker AI image that the image version belongs to. * **SageMakerImageVersionArn** *(string) --* The ARN of the image version created on the instance. To clear the value set for "SageMakerImageVersionArn", pass "None" as the value. * **SageMakerImageVersionAlias** *(string) --* The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format. * **InstanceType** *(string) --* The instance type that the image version runs on. Note: **JupyterServer apps** only support the "system" value.For **KernelGateway apps**, the "system" value is translated to "ml.t3.medium". KernelGateway apps also support all other values for available instance types. * **LifecycleConfigArn** *(string) --* The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource. * **CustomImages** *(list) --* A list of custom SageMaker AI images that are configured to run as a KernelGateway app. The maximum number of custom images are as follows. * On a domain level: 200 * On a space level: 5 * On a user profile level: 5 * *(dict) --* A custom SageMaker AI image. For more information, see Bring your own SageMaker AI image. * **ImageName** *(string) --* The name of the CustomImage. Must be unique to your account. * **ImageVersionNumber** *(integer) --* The version number of the CustomImage. * **AppImageConfigName** *(string) --* The name of the AppImageConfig. * **LifecycleConfigArns** *(list) --* The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain. Note: To remove a Lifecycle Config, you must set "LifecycleConfigArns" to an empty list. * *(string) --* * **JupyterLabAppSettings** *(dict) --* The settings for the JupyterLab application. * **DefaultResourceSpec** *(dict) --* Specifies the ARN's of a SageMaker AI image and SageMaker AI image version, and the instance type that the version runs on. Note: When both "SageMakerImageVersionArn" and "SageMakerImageArn" are passed, "SageMakerImageVersionArn" is used. Any updates to "SageMakerImageArn" will not take effect if "SageMakerImageVersionArn" already exists in the "ResourceSpec" because "SageMakerImageVersionArn" always takes precedence. To clear the value set for "SageMakerImageVersionArn", pass "None" as the value. * **SageMakerImageArn** *(string) --* The ARN of the SageMaker AI image that the image version belongs to. * **SageMakerImageVersionArn** *(string) --* The ARN of the image version created on the instance. To clear the value set for "SageMakerImageVersionArn", pass "None" as the value. * **SageMakerImageVersionAlias** *(string) --* The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format. * **InstanceType** *(string) --* The instance type that the image version runs on. Note: **JupyterServer apps** only support the "system" value.For **KernelGateway apps**, the "system" value is translated to "ml.t3.medium". KernelGateway apps also support all other values for available instance types. * **LifecycleConfigArn** *(string) --* The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource. * **CustomImages** *(list) --* A list of custom SageMaker images that are configured to run as a JupyterLab app. * *(dict) --* A custom SageMaker AI image. For more information, see Bring your own SageMaker AI image. * **ImageName** *(string) --* The name of the CustomImage. Must be unique to your account. * **ImageVersionNumber** *(integer) --* The version number of the CustomImage. * **AppImageConfigName** *(string) --* The name of the AppImageConfig. * **LifecycleConfigArns** *(list) --* The Amazon Resource Name (ARN) of the lifecycle configurations attached to the user profile or domain. To remove a lifecycle config, you must set "LifecycleConfigArns" to an empty list. * *(string) --* * **CodeRepositories** *(list) --* A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterLab application. * *(dict) --* A Git repository that SageMaker AI automatically displays to users for cloning in the JupyterServer application. * **RepositoryUrl** *(string) --* The URL of the Git repository. * **AppLifecycleManagement** *(dict) --* Indicates whether idle shutdown is activated for JupyterLab applications. * **IdleSettings** *(dict) --* Settings related to idle shutdown of Studio applications. * **LifecycleManagement** *(string) --* Indicates whether idle shutdown is activated for the application type. * **IdleTimeoutInMinutes** *(integer) --* The time that SageMaker waits after the application becomes idle before shutting it down. * **MinIdleTimeoutInMinutes** *(integer) --* The minimum value in minutes that custom idle shutdown can be set to by the user. * **MaxIdleTimeoutInMinutes** *(integer) --* The maximum value in minutes that custom idle shutdown can be set to by the user. * **EmrSettings** *(dict) --* The configuration parameters that specify the IAM roles assumed by the execution role of SageMaker (assumable roles) and the cluster instances or job execution environments (execution roles or runtime roles) to manage and access resources required for running Amazon EMR clusters or Amazon EMR Serverless applications. * **AssumableRoleArns** *(list) --* An array of Amazon Resource Names (ARNs) of the IAM roles that the execution role of SageMaker can assume for performing operations or tasks related to Amazon EMR clusters or Amazon EMR Serverless applications. These roles define the permissions and access policies required when performing Amazon EMR-related operations, such as listing, connecting to, or terminating Amazon EMR clusters or Amazon EMR Serverless applications. They are typically used in cross-account access scenarios, where the Amazon EMR resources (clusters or serverless applications) are located in a different Amazon Web Services account than the SageMaker domain. * *(string) --* * **ExecutionRoleArns** *(list) --* An array of Amazon Resource Names (ARNs) of the IAM roles used by the Amazon EMR cluster instances or job execution environments to access other Amazon Web Services services and resources needed during the runtime of your Amazon EMR or Amazon EMR Serverless workloads, such as Amazon S3 for data access, Amazon CloudWatch for logging, or other Amazon Web Services services based on the particular workload requirements. * *(string) --* * **BuiltInLifecycleConfigArn** *(string) --* The lifecycle configuration that runs before the default lifecycle configuration. It can override changes made in the default lifecycle configuration. * **SpaceStorageSettings** *(dict) --* The default storage settings for a space. * **DefaultEbsStorageSettings** *(dict) --* The default EBS storage settings for a space. * **DefaultEbsVolumeSizeInGb** *(integer) --* The default size of the EBS storage volume for a space. * **MaximumEbsVolumeSizeInGb** *(integer) --* The maximum size of the EBS storage volume for a space. * **CustomPosixUserConfig** *(dict) --* Details about the POSIX identity that is used for file system operations. * **Uid** *(integer) --* The POSIX user ID. * **Gid** *(integer) --* The POSIX group ID. * **CustomFileSystemConfigs** *(list) --* The settings for assigning a custom file system to a domain. Permitted users can access this file system in Amazon SageMaker AI Studio. * *(dict) --* The settings for assigning a custom file system to a user profile or space for an Amazon SageMaker AI Domain. Permitted users can access this file system in Amazon SageMaker AI Studio. Note: This is a Tagged Union structure. Only one of the following top level keys will be set: "EFSFileSystemConfig", "FSxLustreFileSystemConfig", "S3FileSystemConfig". If a client receives an unknown member it will set "SDK_UNKNOWN_MEMBER" as the top level key, which maps to the name or tag of the unknown member. The structure of "SDK_UNKNOWN_MEMBER" is as follows: 'SDK_UNKNOWN_MEMBER': {'name': 'UnknownMemberName'} * **EFSFileSystemConfig** *(dict) --* The settings for a custom Amazon EFS file system. * **FileSystemId** *(string) --* The ID of your Amazon EFS file system. * **FileSystemPath** *(string) --* The path to the file system directory that is accessible in Amazon SageMaker AI Studio. Permitted users can access only this directory and below. * **FSxLustreFileSystemConfig** *(dict) --* The settings for a custom Amazon FSx for Lustre file system. * **FileSystemId** *(string) --* The globally unique, 17-digit, ID of the file system, assigned by Amazon FSx for Lustre. * **FileSystemPath** *(string) --* The path to the file system directory that is accessible in Amazon SageMaker Studio. Permitted users can access only this directory and below. * **S3FileSystemConfig** *(dict) --* Configuration settings for a custom Amazon S3 file system. * **MountPath** *(string) --* The file system path where the Amazon S3 storage location will be mounted within the Amazon SageMaker Studio environment. * **S3Uri** *(string) --* The Amazon S3 URI of the S3 file system configuration. **Exceptions** * "SageMaker.Client.exceptions.ResourceNotFound" SageMaker / Client / describe_model describe_model ************** SageMaker.Client.describe_model(**kwargs) Describes a model that you created using the "CreateModel" API. See also: AWS API Documentation **Request Syntax** response = client.describe_model( ModelName='string' ) Parameters: **ModelName** (*string*) -- **[REQUIRED]** The name of the model. Return type: dict Returns: **Response Syntax** { 'ModelName': 'string', 'PrimaryContainer': { 'ContainerHostname': 'string', 'Image': 'string', 'ImageConfig': { 'RepositoryAccessMode': 'Platform'|'Vpc', 'RepositoryAuthConfig': { 'RepositoryCredentialsProviderArn': 'string' } }, 'Mode': 'SingleModel'|'MultiModel', 'ModelDataUrl': 'string', 'ModelDataSource': { 'S3DataSource': { 'S3Uri': 'string', 'S3DataType': 'S3Prefix'|'S3Object', 'CompressionType': 'None'|'Gzip', 'ModelAccessConfig': { 'AcceptEula': True|False }, 'HubAccessConfig': { 'HubContentArn': 'string' }, 'ManifestS3Uri': 'string', 'ETag': 'string', 'ManifestEtag': 'string' } }, 'AdditionalModelDataSources': [ { 'ChannelName': 'string', 'S3DataSource': { 'S3Uri': 'string', 'S3DataType': 'S3Prefix'|'S3Object', 'CompressionType': 'None'|'Gzip', 'ModelAccessConfig': { 'AcceptEula': True|False }, 'HubAccessConfig': { 'HubContentArn': 'string' }, 'ManifestS3Uri': 'string', 'ETag': 'string', 'ManifestEtag': 'string' } }, ], 'Environment': { 'string': 'string' }, 'ModelPackageName': 'string', 'InferenceSpecificationName': 'string', 'MultiModelConfig': { 'ModelCacheSetting': 'Enabled'|'Disabled' } }, 'Containers': [ { 'ContainerHostname': 'string', 'Image': 'string', 'ImageConfig': { 'RepositoryAccessMode': 'Platform'|'Vpc', 'RepositoryAuthConfig': { 'RepositoryCredentialsProviderArn': 'string' } }, 'Mode': 'SingleModel'|'MultiModel', 'ModelDataUrl': 'string', 'ModelDataSource': { 'S3DataSource': { 'S3Uri': 'string', 'S3DataType': 'S3Prefix'|'S3Object', 'CompressionType': 'None'|'Gzip', 'ModelAccessConfig': { 'AcceptEula': True|False }, 'HubAccessConfig': { 'HubContentArn': 'string' }, 'ManifestS3Uri': 'string', 'ETag': 'string', 'ManifestEtag': 'string' } }, 'AdditionalModelDataSources': [ { 'ChannelName': 'string', 'S3DataSource': { 'S3Uri': 'string', 'S3DataType': 'S3Prefix'|'S3Object', 'CompressionType': 'None'|'Gzip', 'ModelAccessConfig': { 'AcceptEula': True|False }, 'HubAccessConfig': { 'HubContentArn': 'string' }, 'ManifestS3Uri': 'string', 'ETag': 'string', 'ManifestEtag': 'string' } }, ], 'Environment': { 'string': 'string' }, 'ModelPackageName': 'string', 'InferenceSpecificationName': 'string', 'MultiModelConfig': { 'ModelCacheSetting': 'Enabled'|'Disabled' } }, ], 'InferenceExecutionConfig': { 'Mode': 'Serial'|'Direct' }, 'ExecutionRoleArn': 'string', 'VpcConfig': { 'SecurityGroupIds': [ 'string', ], 'Subnets': [ 'string', ] }, 'CreationTime': datetime(2015, 1, 1), 'ModelArn': 'string', 'EnableNetworkIsolation': True|False, 'DeploymentRecommendation': { 'RecommendationStatus': 'IN_PROGRESS'|'COMPLETED'|'FAILED'|'NOT_APPLICABLE', 'RealTimeInferenceRecommendations': [ { 'RecommendationId': 'string', 'InstanceType': 'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.dl1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.r8g.medium'|'ml.r8g.large'|'ml.r8g.xlarge'|'ml.r8g.2xlarge'|'ml.r8g.4xlarge'|'ml.r8g.8xlarge'|'ml.r8g.12xlarge'|'ml.r8g.16xlarge'|'ml.r8g.24xlarge'|'ml.r8g.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.trn2.48xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.p5e.48xlarge'|'ml.p5en.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.c8g.medium'|'ml.c8g.large'|'ml.c8g.xlarge'|'ml.c8g.2xlarge'|'ml.c8g.4xlarge'|'ml.c8g.8xlarge'|'ml.c8g.12xlarge'|'ml.c8g.16xlarge'|'ml.c8g.24xlarge'|'ml.c8g.48xlarge'|'ml.r7gd.medium'|'ml.r7gd.large'|'ml.r7gd.xlarge'|'ml.r7gd.2xlarge'|'ml.r7gd.4xlarge'|'ml.r7gd.8xlarge'|'ml.r7gd.12xlarge'|'ml.r7gd.16xlarge'|'ml.m8g.medium'|'ml.m8g.large'|'ml.m8g.xlarge'|'ml.m8g.2xlarge'|'ml.m8g.4xlarge'|'ml.m8g.8xlarge'|'ml.m8g.12xlarge'|'ml.m8g.16xlarge'|'ml.m8g.24xlarge'|'ml.m8g.48xlarge'|'ml.c6in.large'|'ml.c6in.xlarge'|'ml.c6in.2xlarge'|'ml.c6in.4xlarge'|'ml.c6in.8xlarge'|'ml.c6in.12xlarge'|'ml.c6in.16xlarge'|'ml.c6in.24xlarge'|'ml.c6in.32xlarge'|'ml.p6-b200.48xlarge'|'ml.p6e-gb200.36xlarge', 'Environment': { 'string': 'string' } }, ] } } **Response Structure** * *(dict) --* * **ModelName** *(string) --* Name of the SageMaker model. * **PrimaryContainer** *(dict) --* The location of the primary inference code, associated artifacts, and custom environment map that the inference code uses when it is deployed in production. * **ContainerHostname** *(string) --* This parameter is ignored for models that contain only a "PrimaryContainer". When a "ContainerDefinition" is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline. If you don't specify a value for this parameter for a "ContainerDefinition" that is part of an inference pipeline, a unique name is automatically assigned based on the position of the "ContainerDefinition" in the pipeline. If you specify a value for the "ContainerHostName" for any "ContainerDefinition" that is part of an inference pipeline, you must specify a value for the "ContainerHostName" parameter of every "ContainerDefinition" in that pipeline. * **Image** *(string) --* The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both "registry/repository[:tag]" and "registry/repository[@digest]" image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker. Note: The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating. * **ImageConfig** *(dict) --* Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers. Note: The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating. * **RepositoryAccessMode** *(string) --* Set this to one of the following values: * "Platform" - The model image is hosted in Amazon ECR. * "Vpc" - The model image is hosted in a private Docker registry in your VPC. * **RepositoryAuthConfig** *(dict) --* (Optional) Specifies an authentication configuration for the private docker registry where your model image is hosted. Specify a value for this property only if you specified "Vpc" as the value for the "RepositoryAccessMode" field, and the private Docker registry where the model image is hosted requires authentication. * **RepositoryCredentialsProviderArn** *(string) --* The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function that provides credentials to authenticate to the private Docker registry where your model image is hosted. For information about how to create an Amazon Web Services Lambda function, see Create a Lambda function with the console in the *Amazon Web Services Lambda Developer Guide*. * **Mode** *(string) --* Whether the container hosts a single model or multiple models. * **ModelDataUrl** *(string) --* The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). The S3 path is required for SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters. Note: The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating. If you provide a value for this parameter, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provide. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the *Amazon Web Services Identity and Access Management User Guide*. Warning: If you use a built-in algorithm to create a model, SageMaker requires that you provide a S3 path to the model artifacts in "ModelDataUrl". * **ModelDataSource** *(dict) --* Specifies the location of ML model data to deploy. Note: Currently you cannot use "ModelDataSource" in conjunction with SageMaker batch transform, SageMaker serverless endpoints, SageMaker multi-model endpoints, and SageMaker Marketplace. * **S3DataSource** *(dict) --* Specifies the S3 location of ML model data to deploy. * **S3Uri** *(string) --* Specifies the S3 path of ML model data to deploy. * **S3DataType** *(string) --* Specifies the type of ML model data to deploy. If you choose "S3Prefix", "S3Uri" identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by "S3Uri" always ends with a forward slash (/). If you choose "S3Object", "S3Uri" identifies an object that is the ML model data to deploy. * **CompressionType** *(string) --* Specifies how the ML model data is prepared. If you choose "Gzip" and choose "S3Object" as the value of "S3DataType", "S3Uri" identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment. If you choose "None" and chooose "S3Object" as the value of "S3DataType", "S3Uri" identifies an object that represents an uncompressed ML model to deploy. If you choose None and choose "S3Prefix" as the value of "S3DataType", "S3Uri" identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy. If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code: * If you choose "S3Object" as the value of "S3DataType", then SageMaker will split the key of the S3 object referenced by "S3Uri" by slash (/), and use the last part as the filename of the file holding the content of the S3 object. * If you choose "S3Prefix" as the value of "S3DataType", then for each S3 object under the key name pefix referenced by "S3Uri", SageMaker will trim its key by the prefix, and use the remainder as the path (relative to "/opt/ml/model") of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object. * Do not use any of the following as file names or directory names: * An empty or blank string * A string which contains null bytes * A string longer than 255 bytes * A single dot ( ".") * A double dot ( "..") * Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects "s3://mybucket/model/weights" and "s3://mybucket/model/weights/part1" and you specify "s3://mybucket/model/" as the value of "S3Uri" and "S3Prefix" as the value of "S3DataType", then it will result in name clash between "/opt/ml/model/weights" (a regular file) and "/opt/ml/model/weights/" (a directory). * Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure. * **ModelAccessConfig** *(dict) --* Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the "ModelAccessConfig". You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model. * **AcceptEula** *(boolean) --* Specifies agreement to the model end-user license agreement (EULA). The "AcceptEula" value must be explicitly defined as "True" in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model. * **HubAccessConfig** *(dict) --* Configuration information for hub access. * **HubContentArn** *(string) --* The ARN of the hub content for which deployment access is allowed. * **ManifestS3Uri** *(string) --* The Amazon S3 URI of the manifest file. The manifest file is a CSV file that stores the artifact locations. * **ETag** *(string) --* The ETag associated with S3 URI. * **ManifestEtag** *(string) --* The ETag associated with Manifest S3 URI. * **AdditionalModelDataSources** *(list) --* Data sources that are available to your model in addition to the one that you specify for "ModelDataSource" when you use the "CreateModel" action. * *(dict) --* Data sources that are available to your model in addition to the one that you specify for "ModelDataSource" when you use the "CreateModel" action. * **ChannelName** *(string) --* A custom name for this "AdditionalModelDataSource" object. * **S3DataSource** *(dict) --* Specifies the S3 location of ML model data to deploy. * **S3Uri** *(string) --* Specifies the S3 path of ML model data to deploy. * **S3DataType** *(string) --* Specifies the type of ML model data to deploy. If you choose "S3Prefix", "S3Uri" identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by "S3Uri" always ends with a forward slash (/). If you choose "S3Object", "S3Uri" identifies an object that is the ML model data to deploy. * **CompressionType** *(string) --* Specifies how the ML model data is prepared. If you choose "Gzip" and choose "S3Object" as the value of "S3DataType", "S3Uri" identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment. If you choose "None" and chooose "S3Object" as the value of "S3DataType", "S3Uri" identifies an object that represents an uncompressed ML model to deploy. If you choose None and choose "S3Prefix" as the value of "S3DataType", "S3Uri" identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy. If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code: * If you choose "S3Object" as the value of "S3DataType", then SageMaker will split the key of the S3 object referenced by "S3Uri" by slash (/), and use the last part as the filename of the file holding the content of the S3 object. * If you choose "S3Prefix" as the value of "S3DataType", then for each S3 object under the key name pefix referenced by "S3Uri", SageMaker will trim its key by the prefix, and use the remainder as the path (relative to "/opt/ml/model") of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object. * Do not use any of the following as file names or directory names: * An empty or blank string * A string which contains null bytes * A string longer than 255 bytes * A single dot ( ".") * A double dot ( "..") * Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects "s3://mybucket/model/weights" and "s3://mybucket/model/weights/part1" and you specify "s3://mybucket/model/" as the value of "S3Uri" and "S3Prefix" as the value of "S3DataType", then it will result in name clash between "/opt/ml/model/weights" (a regular file) and "/opt/ml/model/weights/" (a directory). * Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure. * **ModelAccessConfig** *(dict) --* Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the "ModelAccessConfig". You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model. * **AcceptEula** *(boolean) --* Specifies agreement to the model end-user license agreement (EULA). The "AcceptEula" value must be explicitly defined as "True" in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model. * **HubAccessConfig** *(dict) --* Configuration information for hub access. * **HubContentArn** *(string) --* The ARN of the hub content for which deployment access is allowed. * **ManifestS3Uri** *(string) --* The Amazon S3 URI of the manifest file. The manifest file is a CSV file that stores the artifact locations. * **ETag** *(string) --* The ETag associated with S3 URI. * **ManifestEtag** *(string) --* The ETag associated with Manifest S3 URI. * **Environment** *(dict) --* The environment variables to set in the Docker container. Don't include any sensitive data in your environment variables. The maximum length of each key and value in the "Environment" map is 1024 bytes. The maximum length of all keys and values in the map, combined, is 32 KB. If you pass multiple containers to a "CreateModel" request, then the maximum length of all of their maps, combined, is also 32 KB. * *(string) --* * *(string) --* * **ModelPackageName** *(string) --* The name or Amazon Resource Name (ARN) of the model package to use to create the model. * **InferenceSpecificationName** *(string) --* The inference specification name in the model package version. * **MultiModelConfig** *(dict) --* Specifies additional configuration for multi-model endpoints. * **ModelCacheSetting** *(string) --* Whether to cache models for a multi-model endpoint. By default, multi-model endpoints cache models so that a model does not have to be loaded into memory each time it is invoked. Some use cases do not benefit from model caching. For example, if an endpoint hosts a large number of models that are each invoked infrequently, the endpoint might perform better if you disable model caching. To disable model caching, set the value of this parameter to "Disabled". * **Containers** *(list) --* The containers in the inference pipeline. * *(dict) --* Describes the container, as part of model definition. * **ContainerHostname** *(string) --* This parameter is ignored for models that contain only a "PrimaryContainer". When a "ContainerDefinition" is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline. If you don't specify a value for this parameter for a "ContainerDefinition" that is part of an inference pipeline, a unique name is automatically assigned based on the position of the "ContainerDefinition" in the pipeline. If you specify a value for the "ContainerHostName" for any "ContainerDefinition" that is part of an inference pipeline, you must specify a value for the "ContainerHostName" parameter of every "ContainerDefinition" in that pipeline. * **Image** *(string) --* The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both "registry/repository[:tag]" and "registry/repository[@digest]" image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker. Note: The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating. * **ImageConfig** *(dict) --* Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers. Note: The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating. * **RepositoryAccessMode** *(string) --* Set this to one of the following values: * "Platform" - The model image is hosted in Amazon ECR. * "Vpc" - The model image is hosted in a private Docker registry in your VPC. * **RepositoryAuthConfig** *(dict) --* (Optional) Specifies an authentication configuration for the private docker registry where your model image is hosted. Specify a value for this property only if you specified "Vpc" as the value for the "RepositoryAccessMode" field, and the private Docker registry where the model image is hosted requires authentication. * **RepositoryCredentialsProviderArn** *(string) --* The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function that provides credentials to authenticate to the private Docker registry where your model image is hosted. For information about how to create an Amazon Web Services Lambda function, see Create a Lambda function with the console in the *Amazon Web Services Lambda Developer Guide*. * **Mode** *(string) --* Whether the container hosts a single model or multiple models. * **ModelDataUrl** *(string) --* The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). The S3 path is required for SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters. Note: The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating. If you provide a value for this parameter, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provide. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the *Amazon Web Services Identity and Access Management User Guide*. Warning: If you use a built-in algorithm to create a model, SageMaker requires that you provide a S3 path to the model artifacts in "ModelDataUrl". * **ModelDataSource** *(dict) --* Specifies the location of ML model data to deploy. Note: Currently you cannot use "ModelDataSource" in conjunction with SageMaker batch transform, SageMaker serverless endpoints, SageMaker multi-model endpoints, and SageMaker Marketplace. * **S3DataSource** *(dict) --* Specifies the S3 location of ML model data to deploy. * **S3Uri** *(string) --* Specifies the S3 path of ML model data to deploy. * **S3DataType** *(string) --* Specifies the type of ML model data to deploy. If you choose "S3Prefix", "S3Uri" identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by "S3Uri" always ends with a forward slash (/). If you choose "S3Object", "S3Uri" identifies an object that is the ML model data to deploy. * **CompressionType** *(string) --* Specifies how the ML model data is prepared. If you choose "Gzip" and choose "S3Object" as the value of "S3DataType", "S3Uri" identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment. If you choose "None" and chooose "S3Object" as the value of "S3DataType", "S3Uri" identifies an object that represents an uncompressed ML model to deploy. If you choose None and choose "S3Prefix" as the value of "S3DataType", "S3Uri" identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy. If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code: * If you choose "S3Object" as the value of "S3DataType", then SageMaker will split the key of the S3 object referenced by "S3Uri" by slash (/), and use the last part as the filename of the file holding the content of the S3 object. * If you choose "S3Prefix" as the value of "S3DataType", then for each S3 object under the key name pefix referenced by "S3Uri", SageMaker will trim its key by the prefix, and use the remainder as the path (relative to "/opt/ml/model") of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object. * Do not use any of the following as file names or directory names: * An empty or blank string * A string which contains null bytes * A string longer than 255 bytes * A single dot ( ".") * A double dot ( "..") * Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects "s3://mybucket/model/weights" and "s3://mybucket/model/weights/part1" and you specify "s3://mybucket/model/" as the value of "S3Uri" and "S3Prefix" as the value of "S3DataType", then it will result in name clash between "/opt/ml/model/weights" (a regular file) and "/opt/ml/model/weights/" (a directory). * Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure. * **ModelAccessConfig** *(dict) --* Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the "ModelAccessConfig". You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model. * **AcceptEula** *(boolean) --* Specifies agreement to the model end-user license agreement (EULA). The "AcceptEula" value must be explicitly defined as "True" in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model. * **HubAccessConfig** *(dict) --* Configuration information for hub access. * **HubContentArn** *(string) --* The ARN of the hub content for which deployment access is allowed. * **ManifestS3Uri** *(string) --* The Amazon S3 URI of the manifest file. The manifest file is a CSV file that stores the artifact locations. * **ETag** *(string) --* The ETag associated with S3 URI. * **ManifestEtag** *(string) --* The ETag associated with Manifest S3 URI. * **AdditionalModelDataSources** *(list) --* Data sources that are available to your model in addition to the one that you specify for "ModelDataSource" when you use the "CreateModel" action. * *(dict) --* Data sources that are available to your model in addition to the one that you specify for "ModelDataSource" when you use the "CreateModel" action. * **ChannelName** *(string) --* A custom name for this "AdditionalModelDataSource" object. * **S3DataSource** *(dict) --* Specifies the S3 location of ML model data to deploy. * **S3Uri** *(string) --* Specifies the S3 path of ML model data to deploy. * **S3DataType** *(string) --* Specifies the type of ML model data to deploy. If you choose "S3Prefix", "S3Uri" identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by "S3Uri" always ends with a forward slash (/). If you choose "S3Object", "S3Uri" identifies an object that is the ML model data to deploy. * **CompressionType** *(string) --* Specifies how the ML model data is prepared. If you choose "Gzip" and choose "S3Object" as the value of "S3DataType", "S3Uri" identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment. If you choose "None" and chooose "S3Object" as the value of "S3DataType", "S3Uri" identifies an object that represents an uncompressed ML model to deploy. If you choose None and choose "S3Prefix" as the value of "S3DataType", "S3Uri" identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy. If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code: * If you choose "S3Object" as the value of "S3DataType", then SageMaker will split the key of the S3 object referenced by "S3Uri" by slash (/), and use the last part as the filename of the file holding the content of the S3 object. * If you choose "S3Prefix" as the value of "S3DataType", then for each S3 object under the key name pefix referenced by "S3Uri", SageMaker will trim its key by the prefix, and use the remainder as the path (relative to "/opt/ml/model") of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object. * Do not use any of the following as file names or directory names: * An empty or blank string * A string which contains null bytes * A string longer than 255 bytes * A single dot ( ".") * A double dot ( "..") * Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects "s3://mybucket/model/weights" and "s3://mybucket/model/weights/part1" and you specify "s3://mybucket/model/" as the value of "S3Uri" and "S3Prefix" as the value of "S3DataType", then it will result in name clash between "/opt/ml/model/weights" (a regular file) and "/opt/ml/model/weights/" (a directory). * Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure. * **ModelAccessConfig** *(dict) --* Specifies the access configuration file for the ML model. You can explicitly accept the model end- user license agreement (EULA) within the "ModelAccessConfig". You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model. * **AcceptEula** *(boolean) --* Specifies agreement to the model end-user license agreement (EULA). The "AcceptEula" value must be explicitly defined as "True" in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model. * **HubAccessConfig** *(dict) --* Configuration information for hub access. * **HubContentArn** *(string) --* The ARN of the hub content for which deployment access is allowed. * **ManifestS3Uri** *(string) --* The Amazon S3 URI of the manifest file. The manifest file is a CSV file that stores the artifact locations. * **ETag** *(string) --* The ETag associated with S3 URI. * **ManifestEtag** *(string) --* The ETag associated with Manifest S3 URI. * **Environment** *(dict) --* The environment variables to set in the Docker container. Don't include any sensitive data in your environment variables. The maximum length of each key and value in the "Environment" map is 1024 bytes. The maximum length of all keys and values in the map, combined, is 32 KB. If you pass multiple containers to a "CreateModel" request, then the maximum length of all of their maps, combined, is also 32 KB. * *(string) --* * *(string) --* * **ModelPackageName** *(string) --* The name or Amazon Resource Name (ARN) of the model package to use to create the model. * **InferenceSpecificationName** *(string) --* The inference specification name in the model package version. * **MultiModelConfig** *(dict) --* Specifies additional configuration for multi-model endpoints. * **ModelCacheSetting** *(string) --* Whether to cache models for a multi-model endpoint. By default, multi-model endpoints cache models so that a model does not have to be loaded into memory each time it is invoked. Some use cases do not benefit from model caching. For example, if an endpoint hosts a large number of models that are each invoked infrequently, the endpoint might perform better if you disable model caching. To disable model caching, set the value of this parameter to "Disabled". * **InferenceExecutionConfig** *(dict) --* Specifies details of how containers in a multi-container endpoint are called. * **Mode** *(string) --* How containers in a multi-container are run. The following values are valid. * "SERIAL" - Containers run as a serial pipeline. * "DIRECT" - Only the individual container that you specify is run. * **ExecutionRoleArn** *(string) --* The Amazon Resource Name (ARN) of the IAM role that you specified for the model. * **VpcConfig** *(dict) --* A VpcConfig object that specifies the VPC that this model has access to. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud * **SecurityGroupIds** *(list) --* The VPC security group IDs, in the form "sg-xxxxxxxx". Specify the security groups for the VPC that is specified in the "Subnets" field. * *(string) --* * **Subnets** *(list) --* The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones. * *(string) --* * **CreationTime** *(datetime) --* A timestamp that shows when the model was created. * **ModelArn** *(string) --* The Amazon Resource Name (ARN) of the model. * **EnableNetworkIsolation** *(boolean) --* If "True", no inbound or outbound network calls can be made to or from the model container. * **DeploymentRecommendation** *(dict) --* A set of recommended deployment configurations for the model. * **RecommendationStatus** *(string) --* Status of the deployment recommendation. The status "NOT_APPLICABLE" means that SageMaker is unable to provide a default recommendation for the model using the information provided. If the deployment status is "IN_PROGRESS", retry your API call after a few seconds to get a "COMPLETED" deployment recommendation. * **RealTimeInferenceRecommendations** *(list) --* A list of RealTimeInferenceRecommendation items. * *(dict) --* The recommended configuration to use for Real-Time Inference. * **RecommendationId** *(string) --* The recommendation ID which uniquely identifies each recommendation. * **InstanceType** *(string) --* The recommended instance type for Real-Time Inference. * **Environment** *(dict) --* The recommended environment variables to set in the model container for Real-Time Inference. * *(string) --* * *(string) --* SageMaker / Client / create_training_plan create_training_plan ******************** SageMaker.Client.create_training_plan(**kwargs) Creates a new training plan in SageMaker to reserve compute capacity. Amazon SageMaker Training Plan is a capability within SageMaker that allows customers to reserve and manage GPU capacity for large- scale AI model training. It provides a way to secure predictable access to computational resources within specific timelines and budgets, without the need to manage underlying infrastructure. **How it works** Plans can be created for specific resources such as SageMaker Training Jobs or SageMaker HyperPod clusters, automatically provisioning resources, setting up infrastructure, executing workloads, and handling infrastructure failures. **Plan creation workflow** * Users search for available plan offerings based on their requirements (e.g., instance type, count, start time, duration) using the "SearchTrainingPlanOfferings" API operation. * They create a plan that best matches their needs using the ID of the plan offering they want to use. * After successful upfront payment, the plan's status becomes "Scheduled". * The plan can be used to: * Queue training jobs. * Allocate to an instance group of a SageMaker HyperPod cluster. * When the plan start date arrives, it becomes "Active". Based on available reserved capacity: * Training jobs are launched. * Instance groups are provisioned. **Plan composition** A plan can consist of one or more Reserved Capacities, each defined by a specific instance type, quantity, Availability Zone, duration, and start and end times. For more information about Reserved Capacity, see >>``<>``<<. See also: AWS API Documentation **Request Syntax** response = client.create_training_plan( TrainingPlanName='string', TrainingPlanOfferingId='string', SpareInstanceCountPerUltraServer=123, Tags=[ { 'Key': 'string', 'Value': 'string' }, ] ) Parameters: * **TrainingPlanName** (*string*) -- **[REQUIRED]** The name of the training plan to create. * **TrainingPlanOfferingId** (*string*) -- **[REQUIRED]** The unique identifier of the training plan offering to use for creating this plan. * **SpareInstanceCountPerUltraServer** (*integer*) -- Number of spare instances to reserve per UltraServer for enhanced resiliency. Default is 1. * **Tags** (*list*) -- An array of key-value pairs to apply to this training plan. * *(dict) --* A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources. You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags. For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy. * **Key** *(string) --* **[REQUIRED]** The tag key. Tag keys must be unique per resource. * **Value** *(string) --* **[REQUIRED]** The tag value. Return type: dict Returns: **Response Syntax** { 'TrainingPlanArn': 'string' } **Response Structure** * *(dict) --* * **TrainingPlanArn** *(string) --* The Amazon Resource Name (ARN); of the created training plan. **Exceptions** * "SageMaker.Client.exceptions.ResourceNotFound" * "SageMaker.Client.exceptions.ResourceInUse" * "SageMaker.Client.exceptions.ResourceLimitExceeded" SageMaker / Client / describe_feature_group describe_feature_group ********************** SageMaker.Client.describe_feature_group(**kwargs) Use this operation to describe a "FeatureGroup". The response includes information on the creation time, "FeatureGroup" name, the unique identifier for each "FeatureGroup", and more. See also: AWS API Documentation **Request Syntax** response = client.describe_feature_group( FeatureGroupName='string', NextToken='string' ) Parameters: * **FeatureGroupName** (*string*) -- **[REQUIRED]** The name or Amazon Resource Name (ARN) of the "FeatureGroup" you want described. * **NextToken** (*string*) -- A token to resume pagination of the list of "Features" ( "FeatureDefinitions"). 2,500 "Features" are returned by default. Return type: dict Returns: **Response Syntax** { 'FeatureGroupArn': 'string', 'FeatureGroupName': 'string', 'RecordIdentifierFeatureName': 'string', 'EventTimeFeatureName': 'string', 'FeatureDefinitions': [ { 'FeatureName': 'string', 'FeatureType': 'Integral'|'Fractional'|'String', 'CollectionType': 'List'|'Set'|'Vector', 'CollectionConfig': { 'VectorConfig': { 'Dimension': 123 } } }, ], 'CreationTime': datetime(2015, 1, 1), 'LastModifiedTime': datetime(2015, 1, 1), 'OnlineStoreConfig': { 'SecurityConfig': { 'KmsKeyId': 'string' }, 'EnableOnlineStore': True|False, 'TtlDuration': { 'Unit': 'Seconds'|'Minutes'|'Hours'|'Days'|'Weeks', 'Value': 123 }, 'StorageType': 'Standard'|'InMemory' }, 'OfflineStoreConfig': { 'S3StorageConfig': { 'S3Uri': 'string', 'KmsKeyId': 'string', 'ResolvedOutputS3Uri': 'string' }, 'DisableGlueTableCreation': True|False, 'DataCatalogConfig': { 'TableName': 'string', 'Catalog': 'string', 'Database': 'string' }, 'TableFormat': 'Default'|'Glue'|'Iceberg' }, 'ThroughputConfig': { 'ThroughputMode': 'OnDemand'|'Provisioned', 'ProvisionedReadCapacityUnits': 123, 'ProvisionedWriteCapacityUnits': 123 }, 'RoleArn': 'string', 'FeatureGroupStatus': 'Creating'|'Created'|'CreateFailed'|'Deleting'|'DeleteFailed', 'OfflineStoreStatus': { 'Status': 'Active'|'Blocked'|'Disabled', 'BlockedReason': 'string' }, 'LastUpdateStatus': { 'Status': 'Successful'|'Failed'|'InProgress', 'FailureReason': 'string' }, 'FailureReason': 'string', 'Description': 'string', 'NextToken': 'string', 'OnlineStoreTotalSizeBytes': 123 } **Response Structure** * *(dict) --* * **FeatureGroupArn** *(string) --* The Amazon Resource Name (ARN) of the "FeatureGroup". * **FeatureGroupName** *(string) --* he name of the "FeatureGroup". * **RecordIdentifierFeatureName** *(string) --* The name of the "Feature" used for "RecordIdentifier", whose value uniquely identifies a record stored in the feature store. * **EventTimeFeatureName** *(string) --* The name of the feature that stores the "EventTime" of a Record in a "FeatureGroup". An "EventTime" is a point in time when a new event occurs that corresponds to the creation or update of a "Record" in a "FeatureGroup". All "Records" in the "FeatureGroup" have a corresponding "EventTime". * **FeatureDefinitions** *(list) --* A list of the "Features" in the "FeatureGroup". Each feature is defined by a "FeatureName" and "FeatureType". * *(dict) --* A list of features. You must include "FeatureName" and "FeatureType". Valid feature "FeatureType``s are ``Integral", "Fractional" and "String". * **FeatureName** *(string) --* The name of a feature. The type must be a string. "FeatureName" cannot be any of the following: "is_deleted", "write_time", "api_invocation_time". The name: * Must start with an alphanumeric character. * Can only include alphanumeric characters, underscores, and hyphens. Spaces are not allowed. * **FeatureType** *(string) --* The value type of a feature. Valid values are Integral, Fractional, or String. * **CollectionType** *(string) --* A grouping of elements where each element within the collection must have the same feature type ( "String", "Integral", or "Fractional"). * "List": An ordered collection of elements. * "Set": An unordered collection of unique elements. * "Vector": A specialized list that represents a fixed- size array of elements. The vector dimension is determined by you. Must have elements with fractional feature types. * **CollectionConfig** *(dict) --* Configuration for your collection. Note: This is a Tagged Union structure. Only one of the following top level keys will be set: "VectorConfig". If a client receives an unknown member it will set "SDK_UNKNOWN_MEMBER" as the top level key, which maps to the name or tag of the unknown member. The structure of "SDK_UNKNOWN_MEMBER" is as follows: 'SDK_UNKNOWN_MEMBER': {'name': 'UnknownMemberName'} * **VectorConfig** *(dict) --* Configuration for your vector collection type. * "Dimension": The number of elements in your vector. * **Dimension** *(integer) --* The number of elements in your vector. * **CreationTime** *(datetime) --* A timestamp indicating when SageMaker created the "FeatureGroup". * **LastModifiedTime** *(datetime) --* A timestamp indicating when the feature group was last updated. * **OnlineStoreConfig** *(dict) --* The configuration for the "OnlineStore". * **SecurityConfig** *(dict) --* Use to specify KMS Key ID ( "KMSKeyId") for at-rest encryption of your "OnlineStore". * **KmsKeyId** *(string) --* The Amazon Web Services Key Management Service (KMS) key ARN that SageMaker Feature Store uses to encrypt the Amazon S3 objects at rest using Amazon S3 server-side encryption. The caller (either user or IAM role) of "CreateFeatureGroup" must have below permissions to the "OnlineStore" "KmsKeyId": * ""kms:Encrypt"" * ""kms:Decrypt"" * ""kms:DescribeKey"" * ""kms:CreateGrant"" * ""kms:RetireGrant"" * ""kms:ReEncryptFrom"" * ""kms:ReEncryptTo"" * ""kms:GenerateDataKey"" * ""kms:ListAliases"" * ""kms:ListGrants"" * ""kms:RevokeGrant"" The caller (either user or IAM role) to all DataPlane operations ( "PutRecord", "GetRecord", "DeleteRecord") must have the following permissions to the "KmsKeyId": * ""kms:Decrypt"" * **EnableOnlineStore** *(boolean) --* Turn "OnlineStore" off by specifying "False" for the "EnableOnlineStore" flag. Turn "OnlineStore" on by specifying "True" for the "EnableOnlineStore" flag. The default value is "False". * **TtlDuration** *(dict) --* Time to live duration, where the record is hard deleted after the expiration time is reached; "ExpiresAt" = "EventTime" + "TtlDuration". For information on HardDelete, see the DeleteRecord API in the Amazon SageMaker API Reference guide. * **Unit** *(string) --* "TtlDuration" time unit. * **Value** *(integer) --* "TtlDuration" time value. * **StorageType** *(string) --* Option for different tiers of low latency storage for real-time data retrieval. * "Standard": A managed low latency data store for feature groups. * "InMemory": A managed data store for feature groups that supports very low latency retrieval. * **OfflineStoreConfig** *(dict) --* The configuration of the offline store. It includes the following configurations: * Amazon S3 location of the offline store. * Configuration of the Glue data catalog. * Table format of the offline store. * Option to disable the automatic creation of a Glue table for the offline store. * Encryption configuration. * **S3StorageConfig** *(dict) --* The Amazon Simple Storage (Amazon S3) location of "OfflineStore". * **S3Uri** *(string) --* The S3 URI, or location in Amazon S3, of "OfflineStore". S3 URIs have a format similar to the following: "s3 ://example-bucket/prefix/". * **KmsKeyId** *(string) --* The Amazon Web Services Key Management Service (KMS) key ARN of the key used to encrypt any objects written into the "OfflineStore" S3 location. The IAM "roleARN" that is passed as a parameter to "CreateFeatureGroup" must have below permissions to the "KmsKeyId": * ""kms:GenerateDataKey"" * **ResolvedOutputS3Uri** *(string) --* The S3 path where offline records are written. * **DisableGlueTableCreation** *(boolean) --* Set to "True" to disable the automatic creation of an Amazon Web Services Glue table when configuring an "OfflineStore". If set to "False", Feature Store will name the "OfflineStore" Glue table following Athena's naming recommendations. The default value is "False". * **DataCatalogConfig** *(dict) --* The meta data of the Glue table that is autogenerated when an "OfflineStore" is created. * **TableName** *(string) --* The name of the Glue table. * **Catalog** *(string) --* The name of the Glue table catalog. * **Database** *(string) --* The name of the Glue table database. * **TableFormat** *(string) --* Format for the offline store table. Supported formats are Glue (Default) and Apache Iceberg. * **ThroughputConfig** *(dict) --* Active throughput configuration of the feature group. There are two modes: "ON_DEMAND" and "PROVISIONED". With on-demand mode, you are charged for data reads and writes that your application performs on your feature group. You do not need to specify read and write throughput because Feature Store accommodates your workloads as they ramp up and down. You can switch a feature group to on-demand only once in a 24 hour period. With provisioned throughput mode, you specify the read and write capacity per second that you expect your application to require, and you are billed based on those limits. Exceeding provisioned throughput will result in your requests being throttled. Note: "PROVISIONED" throughput mode is supported only for feature groups that are offline-only, or use the Standard tier online store. * **ThroughputMode** *(string) --* The mode used for your feature group throughput: "ON_DEMAND" or "PROVISIONED". * **ProvisionedReadCapacityUnits** *(integer) --* For provisioned feature groups with online store enabled, this indicates the read throughput you are billed for and can consume without throttling. This field is not applicable for on-demand feature groups. * **ProvisionedWriteCapacityUnits** *(integer) --* For provisioned feature groups, this indicates the write throughput you are billed for and can consume without throttling. This field is not applicable for on-demand feature groups. * **RoleArn** *(string) --* The Amazon Resource Name (ARN) of the IAM execution role used to persist data into the OfflineStore if an OfflineStoreConfig is provided. * **FeatureGroupStatus** *(string) --* The status of the feature group. * **OfflineStoreStatus** *(dict) --* The status of the "OfflineStore". Notifies you if replicating data into the "OfflineStore" has failed. Returns either: "Active" or "Blocked" * **Status** *(string) --* An "OfflineStore" status. * **BlockedReason** *(string) --* The justification for why the OfflineStoreStatus is Blocked (if applicable). * **LastUpdateStatus** *(dict) --* A value indicating whether the update made to the feature group was successful. * **Status** *(string) --* A value that indicates whether the update was made successful. * **FailureReason** *(string) --* If the update wasn't successful, indicates the reason why it failed. * **FailureReason** *(string) --* The reason that the "FeatureGroup" failed to be replicated in the "OfflineStore". This is failure can occur because: * The "FeatureGroup" could not be created in the "OfflineStore". * The "FeatureGroup" could not be deleted from the "OfflineStore". * **Description** *(string) --* A free form description of the feature group. * **NextToken** *(string) --* A token to resume pagination of the list of "Features" ( "FeatureDefinitions"). * **OnlineStoreTotalSizeBytes** *(integer) --* The size of the "OnlineStore" in bytes. **Exceptions** * "SageMaker.Client.exceptions.ResourceNotFound" SageMaker / Client / describe_transform_job describe_transform_job ********************** SageMaker.Client.describe_transform_job(**kwargs) Returns information about a transform job. See also: AWS API Documentation **Request Syntax** response = client.describe_transform_job( TransformJobName='string' ) Parameters: **TransformJobName** (*string*) -- **[REQUIRED]** The name of the transform job that you want to view details of. Return type: dict Returns: **Response Syntax** { 'TransformJobName': 'string', 'TransformJobArn': 'string', 'TransformJobStatus': 'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped', 'FailureReason': 'string', 'ModelName': 'string', 'MaxConcurrentTransforms': 123, 'ModelClientConfig': { 'InvocationsTimeoutInSeconds': 123, 'InvocationsMaxRetries': 123 }, 'MaxPayloadInMB': 123, 'BatchStrategy': 'MultiRecord'|'SingleRecord', 'Environment': { 'string': 'string' }, 'TransformInput': { 'DataSource': { 'S3DataSource': { 'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile'|'Converse', 'S3Uri': 'string' } }, 'ContentType': 'string', 'CompressionType': 'None'|'Gzip', 'SplitType': 'None'|'Line'|'RecordIO'|'TFRecord' }, 'TransformOutput': { 'S3OutputPath': 'string', 'Accept': 'string', 'AssembleWith': 'None'|'Line', 'KmsKeyId': 'string' }, 'DataCaptureConfig': { 'DestinationS3Uri': 'string', 'KmsKeyId': 'string', 'GenerateInferenceId': True|False }, 'TransformResources': { 'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge', 'InstanceCount': 123, 'VolumeKmsKeyId': 'string', 'TransformAmiVersion': 'string' }, 'CreationTime': datetime(2015, 1, 1), 'TransformStartTime': datetime(2015, 1, 1), 'TransformEndTime': datetime(2015, 1, 1), 'LabelingJobArn': 'string', 'AutoMLJobArn': 'string', 'DataProcessing': { 'InputFilter': 'string', 'OutputFilter': 'string', 'JoinSource': 'Input'|'None' }, 'ExperimentConfig': { 'ExperimentName': 'string', 'TrialName': 'string', 'TrialComponentDisplayName': 'string', 'RunName': 'string' } } **Response Structure** * *(dict) --* * **TransformJobName** *(string) --* The name of the transform job. * **TransformJobArn** *(string) --* The Amazon Resource Name (ARN) of the transform job. * **TransformJobStatus** *(string) --* The status of the transform job. If the transform job failed, the reason is returned in the "FailureReason" field. * **FailureReason** *(string) --* If the transform job failed, "FailureReason" describes why it failed. A transform job creates a log file, which includes error messages, and stores it as an Amazon S3 object. For more information, see Log Amazon SageMaker Events with Amazon CloudWatch. * **ModelName** *(string) --* The name of the model used in the transform job. * **MaxConcurrentTransforms** *(integer) --* The maximum number of parallel requests on each instance node that can be launched in a transform job. The default value is 1. * **ModelClientConfig** *(dict) --* The timeout and maximum number of retries for processing a transform job invocation. * **InvocationsTimeoutInSeconds** *(integer) --* The timeout value in seconds for an invocation request. The default value is 600. * **InvocationsMaxRetries** *(integer) --* The maximum number of retries when invocation requests are failing. The default value is 3. * **MaxPayloadInMB** *(integer) --* The maximum payload size, in MB, used in the transform job. * **BatchStrategy** *(string) --* Specifies the number of records to include in a mini-batch for an HTTP inference request. A *record* is a single unit of input data that inference can be made on. For example, a single line in a CSV file is a record. To enable the batch strategy, you must set "SplitType" to "Line", "RecordIO", or "TFRecord". * **Environment** *(dict) --* The environment variables to set in the Docker container. We support up to 16 key and values entries in the map. * *(string) --* * *(string) --* * **TransformInput** *(dict) --* Describes the dataset to be transformed and the Amazon S3 location where it is stored. * **DataSource** *(dict) --* Describes the location of the channel data, which is, the S3 location of the input data that the model can consume. * **S3DataSource** *(dict) --* The S3 location of the data source that is associated with a channel. * **S3DataType** *(string) --* If you choose "S3Prefix", "S3Uri" identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform. If you choose "ManifestFile", "S3Uri" identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform. The following values are compatible: "ManifestFile", "S3Prefix" The following value is not compatible: "AugmentedManifestFile" * **S3Uri** *(string) --* Depending on the value specified for the "S3DataType", identifies either a key name prefix or a manifest. For example: * A key name prefix might look like this: "s3://bucketname/exampleprefix/". * A manifest might look like this: "s3://bucketname/example.manifest" The manifest is an S3 object which is a JSON file with the following format: "[ {"prefix": "s3://customer_bucket/some/prefix/"}," ""relative/path/to/custdata-1"," ""relative/path/custdata-2"," "..." ""relative/path/custdata-N"" "]" The preceding JSON matches the following "S3Uris": "s3://customer_buck et/some/prefix/relative/path/to/custdata-1" "s3://c ustomer_bucket/some/prefix/relative/path/custdata-2" "..." "s3://customer_bucket/some/prefix/relative/pa th/custdata-N" The complete set of "S3Uris" in this manifest constitutes the input data for the channel for this datasource. The object that each "S3Uris" points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf. * **ContentType** *(string) --* The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job. * **CompressionType** *(string) --* If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is "None". * **SplitType** *(string) --* The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini- batches. The default value for "SplitType" is "None", which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to "Line" to split records on a newline character boundary. "SplitType" also supports a number of record-oriented binary data formats. Currently, the supported record formats are: * RecordIO * TFRecord When splitting is enabled, the size of a mini-batch depends on the values of the "BatchStrategy" and "MaxPayloadInMB" parameters. When the value of "BatchStrategy" is "MultiRecord", Amazon SageMaker sends the maximum number of records in each request, up to the "MaxPayloadInMB" limit. If the value of "BatchStrategy" is "SingleRecord", Amazon SageMaker sends individual records in each request. Note: Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of "BatchStrategy" is set to "SingleRecord". Padding is not removed if the value of "BatchStrategy" is set to "MultiRecord".For more information about "RecordIO", see Create a Dataset Using RecordIO in the MXNet documentation. For more information about "TFRecord", see Consuming TFRecord data in the TensorFlow documentation. * **TransformOutput** *(dict) --* Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job. * **S3OutputPath** *(string) --* The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example, "s3 ://bucket-name/key-name-prefix". For every S3 object used as input for the transform job, batch transform stores the transformed data with an . "out" suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored at "s3://bucket-name/input-name- prefix/dataset01/data.csv", batch transform stores the transformed data at "s3://bucket-name/output-name-prefix /input-name-prefix/data.csv.out". Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an . "out" file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation. * **Accept** *(string) --* The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job. * **AssembleWith** *(string) --* Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify "None". To add a newline character at the end of every transformed record, specify "Line". * **KmsKeyId** *(string) --* The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The "KmsKeyId" can be any of the following formats: * Key ID: "1234abcd-12ab-34cd-56ef-1234567890ab" * Key ARN: "arn:aws:kms:us-west-2:111122223333:key /1234abcd-12ab-34cd-56ef-1234567890ab" * Alias name: "alias/ExampleAlias" * Alias name ARN: "arn:aws:kms:us- west-2:111122223333:alias/ExampleAlias" If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the *Amazon Simple Storage Service Developer Guide.* The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the *Amazon Web Services Key Management Service Developer Guide*. * **DataCaptureConfig** *(dict) --* Configuration to control how SageMaker captures inference data. * **DestinationS3Uri** *(string) --* The Amazon S3 location being used to capture the data. * **KmsKeyId** *(string) --* The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the batch transform job. The KmsKeyId can be any of the following formats: * Key ID: "1234abcd-12ab-34cd-56ef-1234567890ab" * Key ARN: "arn:aws:kms:us-west-2:111122223333:key /1234abcd-12ab-34cd-56ef-1234567890ab" * Alias name: "alias/ExampleAlias" * Alias name ARN: "arn:aws:kms:us- west-2:111122223333:alias/ExampleAlias" * **GenerateInferenceId** *(boolean) --* Flag that indicates whether to append inference id to the output. * **TransformResources** *(dict) --* Describes the resources, including ML instance types and ML instance count, to use for the transform job. * **InstanceType** *(string) --* The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or >>``<>``< * *(dict) --* Defines a hyperparameter to be used by an algorithm. * **Name** *(string) --* The name of this hyperparameter. The name must be unique. * **Description** *(string) --* A brief description of the hyperparameter. * **Type** *(string) --* The type of this hyperparameter. The valid types are "Integer", "Continuous", "Categorical", and "FreeText". * **Range** *(dict) --* The allowed range for this hyperparameter. * **IntegerParameterRangeSpecification** *(dict) --* A "IntegerParameterRangeSpecification" object that defines the possible values for an integer hyperparameter. * **MinValue** *(string) --* The minimum integer value allowed. * **MaxValue** *(string) --* The maximum integer value allowed. * **ContinuousParameterRangeSpecification** *(dict) --* A "ContinuousParameterRangeSpecification" object that defines the possible values for a continuous hyperparameter. * **MinValue** *(string) --* The minimum floating-point value allowed. * **MaxValue** *(string) --* The maximum floating-point value allowed. * **CategoricalParameterRangeSpecification** *(dict) --* A "CategoricalParameterRangeSpecification" object that defines the possible values for a categorical hyperparameter. * **Values** *(list) --* The allowed categories for the hyperparameter. * *(string) --* * **IsTunable** *(boolean) --* Indicates whether this hyperparameter is tunable in a hyperparameter tuning job. * **IsRequired** *(boolean) --* Indicates whether this hyperparameter is required. * **DefaultValue** *(string) --* The default value for this hyperparameter. If a default value is specified, a hyperparameter cannot be required. * **SupportedTrainingInstanceTypes** *(list) --* A list of the instance types that this algorithm can use for training. * *(string) --* * **SupportsDistributedTraining** *(boolean) --* Indicates whether the algorithm supports distributed training. If set to false, buyers can't request more than one instance during training. * **MetricDefinitions** *(list) --* A list of "MetricDefinition" objects, which are used for parsing metrics generated by the algorithm. * *(dict) --* Specifies a metric that the training algorithm writes to "stderr" or "stdout". You can view these logs to understand how your training job performs and check for any errors encountered during training. SageMaker hyperparameter tuning captures all defined metrics. Specify one of the defined metrics to use as an objective metric using the TuningObjective parameter in the "HyperParameterTrainingJobDefinition" API to evaluate job performance during hyperparameter tuning. * **Name** *(string) --* The name of the metric. * **Regex** *(string) --* A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables. * **TrainingChannels** *(list) --* A list of "ChannelSpecification" objects, which specify the input sources to be used by the algorithm. * *(dict) --* Defines a named input source, called a channel, to be used by an algorithm. * **Name** *(string) --* The name of the channel. * **Description** *(string) --* A brief description of the channel. * **IsRequired** *(boolean) --* Indicates whether the channel is required by the algorithm. * **SupportedContentTypes** *(list) --* The supported MIME types for the data. * *(string) --* * **SupportedCompressionTypes** *(list) --* The allowed compression types, if data compression is used. * *(string) --* * **SupportedInputModes** *(list) --* The allowed input mode, either FILE or PIPE. In FILE mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode. In PIPE mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume. * *(string) --* The training input mode that the algorithm supports. For more information about input modes, see Algorithms. **Pipe mode** If an algorithm supports "Pipe" mode, Amazon SageMaker streams data directly from Amazon S3 to the container. **File mode** If an algorithm supports "File" mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container. You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any. For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training. **FastFile mode** If an algorithm supports "FastFile" mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk. "FastFile" mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided. * **SupportedTuningJobObjectiveMetrics** *(list) --* A list of the metrics that the algorithm emits that can be used as the objective metric in a hyperparameter tuning job. * *(dict) --* Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the "Type" parameter. If you want to define a custom objective metric, see Define metrics and environment variables. * **Type** *(string) --* Whether to minimize or maximize the objective metric. * **MetricName** *(string) --* The name of the metric to use for the objective metric. * **AdditionalS3DataSource** *(dict) --* The additional data source used during the training job. * **S3DataType** *(string) --* The data type of the additional data source that you specify for use in inference or training. * **S3Uri** *(string) --* The uniform resource identifier (URI) used to identify an additional data source used in inference or training. * **CompressionType** *(string) --* The type of compression used for an additional data source used in inference or training. Specify "None" if your additional data source is not compressed. * **ETag** *(string) --* The ETag associated with S3 URI. * **InferenceSpecification** *(dict) --* Details about inference jobs that the algorithm runs. * **Containers** *(list) --* The Amazon ECR registry path of the Docker image that contains the inference code. * *(dict) --* Describes the Docker container for the model package. * **ContainerHostname** *(string) --* The DNS host name for the Docker container. * **Image** *(string) --* The Amazon Elastic Container Registry (Amazon ECR) path where inference code is stored. If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both "registry/repository[:tag]" and "registry/repository[@digest]" image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker. * **ImageDigest** *(string) --* An MD5 hash of the training algorithm that identifies the Docker image used for training. * **ModelDataUrl** *(string) --* The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single "gzip" compressed tar archive ( ".tar.gz" suffix). Note: The model artifacts must be in an S3 bucket that is in the same region as the model package. * **ModelDataSource** *(dict) --* Specifies the location of ML model data to deploy during endpoint creation. * **S3DataSource** *(dict) --* Specifies the S3 location of ML model data to deploy. * **S3Uri** *(string) --* Specifies the S3 path of ML model data to deploy. * **S3DataType** *(string) --* Specifies the type of ML model data to deploy. If you choose "S3Prefix", "S3Uri" identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by "S3Uri" always ends with a forward slash (/). If you choose "S3Object", "S3Uri" identifies an object that is the ML model data to deploy. * **CompressionType** *(string) --* Specifies how the ML model data is prepared. If you choose "Gzip" and choose "S3Object" as the value of "S3DataType", "S3Uri" identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment. If you choose "None" and chooose "S3Object" as the value of "S3DataType", "S3Uri" identifies an object that represents an uncompressed ML model to deploy. If you choose None and choose "S3Prefix" as the value of "S3DataType", "S3Uri" identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy. If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code: * If you choose "S3Object" as the value of "S3DataType", then SageMaker will split the key of the S3 object referenced by "S3Uri" by slash (/), and use the last part as the filename of the file holding the content of the S3 object. * If you choose "S3Prefix" as the value of "S3DataType", then for each S3 object under the key name pefix referenced by "S3Uri", SageMaker will trim its key by the prefix, and use the remainder as the path (relative to "/opt/ml/model") of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object. * Do not use any of the following as file names or directory names: * An empty or blank string * A string which contains null bytes * A string longer than 255 bytes * A single dot ( ".") * A double dot ( "..") * Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects "s3://mybucket/model/weights" and "s3://mybucket/model/weights/part1" and you specify "s3://mybucket/model/" as the value of "S3Uri" and "S3Prefix" as the value of "S3DataType", then it will result in name clash between "/opt/ml/model/weights" (a regular file) and "/opt/ml/model/weights/" (a directory). * Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure. * **ModelAccessConfig** *(dict) --* Specifies the access configuration file for the ML model. You can explicitly accept the model end- user license agreement (EULA) within the "ModelAccessConfig". You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model. * **AcceptEula** *(boolean) --* Specifies agreement to the model end-user license agreement (EULA). The "AcceptEula" value must be explicitly defined as "True" in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model. * **HubAccessConfig** *(dict) --* Configuration information for hub access. * **HubContentArn** *(string) --* The ARN of the hub content for which deployment access is allowed. * **ManifestS3Uri** *(string) --* The Amazon S3 URI of the manifest file. The manifest file is a CSV file that stores the artifact locations. * **ETag** *(string) --* The ETag associated with S3 URI. * **ManifestEtag** *(string) --* The ETag associated with Manifest S3 URI. * **ProductId** *(string) --* The Amazon Web Services Marketplace product ID of the model package. * **Environment** *(dict) --* The environment variables to set in the Docker container. Each key and value in the "Environment" string to string map can have length of up to 1024. We support up to 16 entries in the map. * *(string) --* * *(string) --* * **ModelInput** *(dict) --* A structure with Model Input details. * **DataInputConfig** *(string) --* The input configuration object for the model. * **Framework** *(string) --* The machine learning framework of the model package container image. * **FrameworkVersion** *(string) --* The framework version of the Model Package Container Image. * **NearestModelName** *(string) --* The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling "ListModelMetadata". * **AdditionalS3DataSource** *(dict) --* The additional data source that is used during inference in the Docker container for your model package. * **S3DataType** *(string) --* The data type of the additional data source that you specify for use in inference or training. * **S3Uri** *(string) --* The uniform resource identifier (URI) used to identify an additional data source used in inference or training. * **CompressionType** *(string) --* The type of compression used for an additional data source used in inference or training. Specify "None" if your additional data source is not compressed. * **ETag** *(string) --* The ETag associated with S3 URI. * **ModelDataETag** *(string) --* The ETag associated with Model Data URL. * **SupportedTransformInstanceTypes** *(list) --* A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed. This parameter is required for unversioned models, and optional for versioned models. * *(string) --* * **SupportedRealtimeInferenceInstanceTypes** *(list) --* A list of the instance types that are used to generate inferences in real-time. This parameter is required for unversioned models, and optional for versioned models. * *(string) --* * **SupportedContentTypes** *(list) --* The supported MIME types for the input data. * *(string) --* * **SupportedResponseMIMETypes** *(list) --* The supported MIME types for the output data. * *(string) --* * **ValidationSpecification** *(dict) --* Details about configurations for one or more training jobs that SageMaker runs to test the algorithm. * **ValidationRole** *(string) --* The IAM roles that SageMaker uses to run the training jobs. * **ValidationProfiles** *(list) --* An array of "AlgorithmValidationProfile" objects, each of which specifies a training job and batch transform job that SageMaker runs to validate your algorithm. * *(dict) --* Defines a training job and a batch transform job that SageMaker runs to validate your algorithm. The data provided in the validation profile is made available to your buyers on Amazon Web Services Marketplace. * **ProfileName** *(string) --* The name of the profile for the algorithm. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen). * **TrainingJobDefinition** *(dict) --* The "TrainingJobDefinition" object that describes the training job that SageMaker runs to validate your algorithm. * **TrainingInputMode** *(string) --* The training input mode that the algorithm supports. For more information about input modes, see Algorithms. **Pipe mode** If an algorithm supports "Pipe" mode, Amazon SageMaker streams data directly from Amazon S3 to the container. **File mode** If an algorithm supports "File" mode, SageMaker downloads the training data from S3 to the provisioned ML storage volume, and mounts the directory to the Docker volume for the training container. You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any. For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training. **FastFile mode** If an algorithm supports "FastFile" mode, SageMaker streams data directly from S3 to the container with no code changes, and provides file system access to the data. Users can author their training script to interact with these files as if they were stored on disk. "FastFile" mode works best when the data is read sequentially. Augmented manifest files aren't supported. The startup time is lower when there are fewer files in the S3 bucket provided. * **HyperParameters** *(dict) --* The hyperparameters used for the training job. * *(string) --* * *(string) --* * **InputDataConfig** *(list) --* An array of "Channel" objects, each of which specifies an input source. * *(dict) --* A channel is a named input source that training algorithms can consume. * **ChannelName** *(string) --* The name of the channel. * **DataSource** *(dict) --* The location of the channel data. * **S3DataSource** *(dict) --* The S3 location of the data source that is associated with a channel. * **S3DataType** *(string) --* If you choose "S3Prefix", "S3Uri" identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training. If you choose "ManifestFile", "S3Uri" identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training. If you choose "AugmentedManifestFile", "S3Uri" identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. "AugmentedManifestFile" can only be used if the Channel's input mode is "Pipe". If you choose "Converse", "S3Uri" identifies an Amazon S3 location that contains data formatted according to Converse format. This format structures conversational messages with specific roles and content types used for training and fine-tuning foundational models. * **S3Uri** *(string) --* Depending on the value specified for the "S3DataType", identifies either a key name prefix or a manifest. For example: * A key name prefix might look like this: "s3://bucketname/exampleprefix/" * A manifest might look like this: "s3://bucketname/example.manifest" A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of "S3Uri". Note that the prefix must be a valid non- empty "S3Uri" that precludes users from specifying a manifest whose individual "S3Uri" is sourced from different S3 buckets. The following code example shows a valid manifest format: "[ {"prefix": "s3://customer_bucket/some/prefix/"}," ""relative/path/to/custdata-1"," ""relative/path/custdata-2"," "..." ""relative/path/custdata-N"" "]" This JSON is equivalent to the following "S3Uri" list: "s3://customer_bucket/some/prefix/r elative/path/to/custdata-1" "s3://custome r_bucket/some/prefix/relative/path/custda ta-2" "..." "s3://customer_bucket/some/pr efix/relative/path/custdata-N" The complete set of "S3Uri" in this manifest is the input data for the channel for this data source. The object that each "S3Uri" points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf. Your input bucket must be located in same Amazon Web Services region as your training job. * **S3DataDistributionType** *(string) --* If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify "FullyReplicated". If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify "ShardedByS3Key". If there are *n* ML compute instances launched for a training job, each instance gets approximately 1/*n* of the number of S3 objects. In this case, model training on each machine uses only the subset of training data. Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms. In distributed training, where you use multiple ML compute EC2 instances, you might choose "ShardedByS3Key". If the algorithm requires copying training data to the ML storage volume (when "TrainingInputMode" is set to "File"), this copies 1/*n* of the number of objects. * **AttributeNames** *(list) --* A list of one or more attribute names to use that are found in a specified augmented manifest file. * *(string) --* * **InstanceGroupNames** *(list) --* A list of names of instance groups that get data from the S3 data source. * *(string) --* * **ModelAccessConfig** *(dict) --* The access configuration file to control access to the ML model. You can explicitly accept the model end-user license agreement (EULA) within the "ModelAccessConfig". * If you are a Jumpstart user, see the End- user license agreements section for more details on accepting the EULA. * If you are an AutoML user, see the *Optional Parameters* section of *Create an AutoML job to fine-tune text generation models using the API* for details on How to set the EULA acceptance when fine- tuning a model using the AutoML API. * **AcceptEula** *(boolean) --* Specifies agreement to the model end-user license agreement (EULA). The "AcceptEula" value must be explicitly defined as "True" in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model. * **HubAccessConfig** *(dict) --* The configuration for a private hub model reference that points to a SageMaker JumpStart public hub model. * **HubContentArn** *(string) --* The ARN of your private model hub content. This should be a "ModelReference" resource type that points to a SageMaker JumpStart public hub model. * **FileSystemDataSource** *(dict) --* The file system that is associated with a channel. * **FileSystemId** *(string) --* The file system id. * **FileSystemAccessMode** *(string) --* The access mode of the mount of the directory associated with the channel. A directory can be mounted either in "ro" (read-only) or "rw" (read-write) mode. * **FileSystemType** *(string) --* The file system type. * **DirectoryPath** *(string) --* The full path to the directory to associate with the channel. * **ContentType** *(string) --* The MIME type of the data. * **CompressionType** *(string) --* If training data is compressed, the compression type. The default value is "None". "CompressionType" is used only in Pipe input mode. In File mode, leave this field unset or set it to None. * **RecordWrapperType** *(string) --* Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO. In File mode, leave this field unset or set it to None. * **InputMode** *(string) --* (Optional) The input mode to use for the data channel in a training job. If you don't set a value for "InputMode", SageMaker uses the value set for "TrainingInputMode". Use this parameter to override the "TrainingInputMode" setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use "File" input mode. To stream data directly from Amazon S3 to the container, choose "Pipe" input mode. To use a model for incremental training, choose "File" input model. * **ShuffleConfig** *(dict) --* A configuration for a shuffle option for input data in a channel. If you use "S3Prefix" for "S3DataType", this shuffles the results of the S3 key prefix matches. If you use "ManifestFile", the order of the S3 object references in the "ManifestFile" is shuffled. If you use "AugmentedManifestFile", the order of the JSON lines in the "AugmentedManifestFile" is shuffled. The shuffling order is determined using the "Seed" value. For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with "S3DataDistributionType" of "ShardedByS3Key", the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch. * **Seed** *(integer) --* Determines the shuffling order in "ShuffleConfig" value. * **OutputDataConfig** *(dict) --* the path to the S3 bucket where you want to store model artifacts. SageMaker creates subfolders for the artifacts. * **KmsKeyId** *(string) --* The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The "KmsKeyId" can be any of the following formats: * // KMS Key ID ""1234abcd-12ab-34cd-56ef- 1234567890ab"" * // Amazon Resource Name (ARN) of a KMS Key ""arn:aws:kms:us-west-2:111122223333:key /1234abcd-12ab-34cd-56ef-1234567890ab"" * // KMS Key Alias ""alias/ExampleAlias"" * // Amazon Resource Name (ARN) of a KMS Key Alias ""arn:aws:kms:us- west-2:111122223333:alias/ExampleAlias"" If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call "kms:Encrypt". If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the *Amazon Simple Storage Service Developer Guide*. If the output data is stored in Amazon S3 Express One Zone, it is encrypted with server-side encryption with Amazon S3 managed keys (SSE-S3). KMS key is not supported for Amazon S3 Express One Zone The KMS key policy must grant permission to the IAM role that you specify in your "CreateTrainingJob", "CreateTransformJob", or "CreateHyperParameterTuningJob" requests. For more information, see Using Key Policies in Amazon Web Services KMS in the *Amazon Web Services Key Management Service Developer Guide*. * **S3OutputPath** *(string) --* Identifies the S3 path where you want SageMaker to store the model artifacts. For example, "s3 ://bucket-name/key-name-prefix". * **CompressionType** *(string) --* The model output compression type. Select "None" to output an uncompressed model, recommended for large model outputs. Defaults to gzip. * **ResourceConfig** *(dict) --* The resources, including the ML compute instances and ML storage volumes, to use for model training. * **InstanceType** *(string) --* The ML compute instance type. * **InstanceCount** *(integer) --* The number of ML compute instances to use. For distributed training, provide a value greater than 1. * **VolumeSizeInGB** *(integer) --* The size of the ML storage volume that you want to provision. ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose "File" as the "TrainingInputMode" in the algorithm specification. When using an ML instance with NVMe SSD volumes, SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include "ml.p4d", "ml.g4dn", and "ml.g5". When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through "VolumeSizeInGB" in the "ResourceConfig" API. For example, ML instance families that use EBS volumes include "ml.c5" and "ml.p2". To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types. To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs. * **VolumeKmsKeyId** *(string) --* The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job. Note: Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a "VolumeKmsKeyId" when using an instance type with local storage.For a list of instance types that support local instance storage, see Instance Store Volumes.For more information about local instance storage encryption, see SSD Instance Store Volumes. The "VolumeKmsKeyId" can be in any of the following formats: * // KMS Key ID ""1234abcd-12ab-34cd-56ef- 1234567890ab"" * // Amazon Resource Name (ARN) of a KMS Key ""arn:aws:kms:us-west-2:111122223333:key /1234abcd-12ab-34cd-56ef-1234567890ab"" * **KeepAlivePeriodInSeconds** *(integer) --* The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs. * **InstanceGroups** *(list) --* The configuration of a heterogeneous cluster in JSON format. * *(dict) --* Defines an instance group for heterogeneous cluster training. When requesting a training job using the CreateTrainingJob API, you can configure multiple instance groups . * **InstanceType** *(string) --* Specifies the instance type of the instance group. * **InstanceCount** *(integer) --* Specifies the number of instances of the instance group. * **InstanceGroupName** *(string) --* Specifies the name of the instance group. * **TrainingPlanArn** *(string) --* The Amazon Resource Name (ARN); of the training plan to use for this resource configuration. * **InstancePlacementConfig** *(dict) --* Configuration for how training job instances are placed and allocated within UltraServers. Only applicable for UltraServer capacity. * **EnableMultipleJobs** *(boolean) --* If set to true, allows multiple jobs to share the same UltraServer instances. If set to false, ensures this job's instances are placed on an UltraServer exclusively, with no other jobs sharing the same UltraServer. Default is false. * **PlacementSpecifications** *(list) --* A list of specifications for how instances should be placed on specific UltraServers. Maximum of 10 items is supported. * *(dict) --* Specifies how instances should be placed on a specific UltraServer. * **UltraServerId** *(string) --* The unique identifier of the UltraServer where instances should be placed. * **InstanceCount** *(integer) --* The number of ML compute instances required to be placed together on the same UltraServer. Minimum value of 1. * **StoppingCondition** *(dict) --* Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs. To stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts. * **MaxRuntimeInSeconds** *(integer) --* The maximum length of time, in seconds, that a training or compilation job can run before it is stopped. For compilation jobs, if the job does not complete during this time, a "TimeOut" error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model. For all other jobs, if the job does not complete during this time, SageMaker ends the job. When "RetryStrategy" is specified in the job request, "MaxRuntimeInSeconds" specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days. The maximum time that a "TrainingJob" can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days. * **MaxWaitTimeInSeconds** *(integer) --* The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than "MaxRuntimeInSeconds". If the job does not complete during this time, SageMaker ends the job. When "RetryStrategy" is specified in the job request, "MaxWaitTimeInSeconds" specifies the maximum time for all of the attempts in total, not each individual attempt. * **MaxPendingTimeInSeconds** *(integer) --* The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped. Note: When working with training jobs that use capacity from training plans, not all "Pending" job states count against the "MaxPendingTimeInSeconds" limit. The following scenarios do not increment the "MaxPendingTimeInSeconds" counter: * The plan is in a "Scheduled" state: Jobs queued (in "Pending" status) before a plan's start date (waiting for scheduled start time) * Between capacity reservations: Jobs temporarily back to "Pending" status between two capacity reservation periods "MaxPendingTimeInSeconds" only increments when jobs are actively waiting for capacity in an "Active" plan. * **TransformJobDefinition** *(dict) --* The "TransformJobDefinition" object that describes the transform job that SageMaker runs to validate your algorithm. * **MaxConcurrentTransforms** *(integer) --* The maximum number of parallel requests that can be sent to each instance in a transform job. The default value is 1. * **MaxPayloadInMB** *(integer) --* The maximum payload size allowed, in MB. A payload is the data portion of a record (without metadata). * **BatchStrategy** *(string) --* A string that determines the number of records included in a single mini-batch. "SingleRecord" means only one record is used per mini-batch. "MultiRecord" means a mini-batch is set to contain as many records that can fit within the "MaxPayloadInMB" limit. * **Environment** *(dict) --* The environment variables to set in the Docker container. We support up to 16 key and values entries in the map. * *(string) --* * *(string) --* * **TransformInput** *(dict) --* A description of the input source and the way the transform job consumes it. * **DataSource** *(dict) --* Describes the location of the channel data, which is, the S3 location of the input data that the model can consume. * **S3DataSource** *(dict) --* The S3 location of the data source that is associated with a channel. * **S3DataType** *(string) --* If you choose "S3Prefix", "S3Uri" identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform. If you choose "ManifestFile", "S3Uri" identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform. The following values are compatible: "ManifestFile", "S3Prefix" The following value is not compatible: "AugmentedManifestFile" * **S3Uri** *(string) --* Depending on the value specified for the "S3DataType", identifies either a key name prefix or a manifest. For example: * A key name prefix might look like this: "s3://bucketname/exampleprefix/". * A manifest might look like this: "s3://bucketname/example.manifest" The manifest is an S3 object which is a JSON file with the following format: "[ {"prefix": "s3://customer_bucket/some/prefix/"}," ""relative/path/to/custdata-1"," ""relative/path/custdata-2"," "..." ""relative/path/custdata-N"" "]" The preceding JSON matches the following "S3Uris": "s3://customer_bucket/some/prefix /relative/path/to/custdata-1" "s3://custome r_bucket/some/prefix/relative/path/custdata -2" "..." "s3://customer_bucket/some/prefix /relative/path/custdata-N" The complete set of "S3Uris" in this manifest constitutes the input data for the channel for this datasource. The object that each "S3Uris" points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf. * **ContentType** *(string) --* The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job. * **CompressionType** *(string) --* If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is "None". * **SplitType** *(string) --* The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for "SplitType" is "None", which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to "Line" to split records on a newline character boundary. "SplitType" also supports a number of record-oriented binary data formats. Currently, the supported record formats are: * RecordIO * TFRecord When splitting is enabled, the size of a mini- batch depends on the values of the "BatchStrategy" and "MaxPayloadInMB" parameters. When the value of "BatchStrategy" is "MultiRecord", Amazon SageMaker sends the maximum number of records in each request, up to the "MaxPayloadInMB" limit. If the value of "BatchStrategy" is "SingleRecord", Amazon SageMaker sends individual records in each request. Note: Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of "BatchStrategy" is set to "SingleRecord". Padding is not removed if the value of "BatchStrategy" is set to "MultiRecord".For more information about "RecordIO", see Create a Dataset Using RecordIO in the MXNet documentation. For more information about "TFRecord", see Consuming TFRecord data in the TensorFlow documentation. * **TransformOutput** *(dict) --* Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job. * **S3OutputPath** *(string) --* The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example, "s3://bucket-name/key-name-prefix". For every S3 object used as input for the transform job, batch transform stores the transformed data with an . "out" suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored at "s3://bucket-name/input-name- prefix/dataset01/data.csv", batch transform stores the transformed data at "s3://bucket-name/output- name-prefix/input-name-prefix/data.csv.out". Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an . "out" file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation. * **Accept** *(string) --* The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job. * **AssembleWith** *(string) --* Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify "None". To add a newline character at the end of every transformed record, specify "Line". * **KmsKeyId** *(string) --* The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The "KmsKeyId" can be any of the following formats: * Key ID: "1234abcd-12ab-34cd-56ef-1234567890ab" * Key ARN: "arn:aws:kms:us-west-2:111122223333:key /1234abcd-12ab-34cd-56ef-1234567890ab" * Alias name: "alias/ExampleAlias" * Alias name ARN: "arn:aws:kms:us- west-2:111122223333:alias/ExampleAlias" If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the *Amazon Simple Storage Service Developer Guide.* The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the *Amazon Web Services Key Management Service Developer Guide*. * **TransformResources** *(dict) --* Identifies the ML compute instances for the transform job. * **InstanceType** *(string) --* The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or >>``<>``<>``<<. * **Name** *(string) --* **[REQUIRED]** The name of the filter field (e.g., Status, InstanceType). * **Value** *(string) --* **[REQUIRED]** The value to filter by for the specified field. Return type: dict Returns: **Response Syntax** { 'NextToken': 'string', 'TrainingPlanSummaries': [ { 'TrainingPlanArn': 'string', 'TrainingPlanName': 'string', 'Status': 'Pending'|'Active'|'Scheduled'|'Expired'|'Failed', 'StatusMessage': 'string', 'DurationHours': 123, 'DurationMinutes': 123, 'StartTime': datetime(2015, 1, 1), 'EndTime': datetime(2015, 1, 1), 'UpfrontFee': 'string', 'CurrencyCode': 'string', 'TotalInstanceCount': 123, 'AvailableInstanceCount': 123, 'InUseInstanceCount': 123, 'TotalUltraServerCount': 123, 'TargetResources': [ 'training-job'|'hyperpod-cluster', ], 'ReservedCapacitySummaries': [ { 'ReservedCapacityArn': 'string', 'ReservedCapacityType': 'UltraServer'|'Instance', 'UltraServerType': 'string', 'UltraServerCount': 123, 'InstanceType': 'ml.p4d.24xlarge'|'ml.p5.48xlarge'|'ml.p5e.48xlarge'|'ml.p5en.48xlarge'|'ml.trn1.32xlarge'|'ml.trn2.48xlarge'|'ml.p6-b200.48xlarge'|'ml.p4de.24xlarge'|'ml.p6e-gb200.36xlarge', 'TotalInstanceCount': 123, 'Status': 'Pending'|'Active'|'Scheduled'|'Expired'|'Failed', 'AvailabilityZone': 'string', 'DurationHours': 123, 'DurationMinutes': 123, 'StartTime': datetime(2015, 1, 1), 'EndTime': datetime(2015, 1, 1) }, ] }, ] } **Response Structure** * *(dict) --* * **NextToken** *(string) --* A token to continue pagination if more results are available. * **TrainingPlanSummaries** *(list) --* A list of summary information for the training plans. * *(dict) --* Details of the training plan. For more information about how to reserve GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see >>``<>``<<. * **TrainingPlanArn** *(string) --* The Amazon Resource Name (ARN); of the training plan. * **TrainingPlanName** *(string) --* The name of the training plan. * **Status** *(string) --* The current status of the training plan (e.g., Pending, Active, Expired). To see the complete list of status values available for a training plan, refer to the "Status" attribute within the "TrainingPlanSummary" object. * **StatusMessage** *(string) --* A message providing additional information about the current status of the training plan. * **DurationHours** *(integer) --* The number of whole hours in the total duration for this training plan. * **DurationMinutes** *(integer) --* The additional minutes beyond whole hours in the total duration for this training plan. * **StartTime** *(datetime) --* The start time of the training plan. * **EndTime** *(datetime) --* The end time of the training plan. * **UpfrontFee** *(string) --* The upfront fee for the training plan. * **CurrencyCode** *(string) --* The currency code for the upfront fee (e.g., USD). * **TotalInstanceCount** *(integer) --* The total number of instances reserved in this training plan. * **AvailableInstanceCount** *(integer) --* The number of instances currently available for use in this training plan. * **InUseInstanceCount** *(integer) --* The number of instances currently in use from this training plan. * **TotalUltraServerCount** *(integer) --* The total number of UltraServers allocated to this training plan. * **TargetResources** *(list) --* The target resources (e.g., training jobs, HyperPod clusters) that can use this training plan. Training plans are specific to their target resource. * A training plan designed for SageMaker training jobs can only be used to schedule and run training jobs. * A training plan for HyperPod clusters can be used exclusively to provide compute resources to a cluster's instance group. * *(string) --* * **ReservedCapacitySummaries** *(list) --* A list of reserved capacities associated with this training plan, including details such as instance types, counts, and availability zones. * *(dict) --* Details of a reserved capacity for the training plan. For more information about how to reserve GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see >>``<>``<<. * **ReservedCapacityArn** *(string) --* The Amazon Resource Name (ARN); of the reserved capacity. * **ReservedCapacityType** *(string) --* The type of reserved capacity. * **UltraServerType** *(string) --* The type of UltraServer included in this reserved capacity, such as ml.u-p6e-gb200x72. * **UltraServerCount** *(integer) --* The number of UltraServers included in this reserved capacity. * **InstanceType** *(string) --* The instance type for the reserved capacity. * **TotalInstanceCount** *(integer) --* The total number of instances in the reserved capacity. * **Status** *(string) --* The current status of the reserved capacity. * **AvailabilityZone** *(string) --* The availability zone for the reserved capacity. * **DurationHours** *(integer) --* The number of whole hours in the total duration for this reserved capacity. * **DurationMinutes** *(integer) --* The additional minutes beyond whole hours in the total duration for this reserved capacity. * **StartTime** *(datetime) --* The start time of the reserved capacity. * **EndTime** *(datetime) --* The end time of the reserved capacity. SageMaker / Client / list_code_repositories list_code_repositories ********************** SageMaker.Client.list_code_repositories(**kwargs) Gets a list of the Git repositories in your account. See also: AWS API Documentation **Request Syntax** response = client.list_code_repositories( CreationTimeAfter=datetime(2015, 1, 1), CreationTimeBefore=datetime(2015, 1, 1), LastModifiedTimeAfter=datetime(2015, 1, 1), LastModifiedTimeBefore=datetime(2015, 1, 1), MaxResults=123, NameContains='string', NextToken='string', SortBy='Name'|'CreationTime'|'LastModifiedTime', SortOrder='Ascending'|'Descending' ) Parameters: * **CreationTimeAfter** (*datetime*) -- A filter that returns only Git repositories that were created after the specified time. * **CreationTimeBefore** (*datetime*) -- A filter that returns only Git repositories that were created before the specified time. * **LastModifiedTimeAfter** (*datetime*) -- A filter that returns only Git repositories that were last modified after the specified time. * **LastModifiedTimeBefore** (*datetime*) -- A filter that returns only Git repositories that were last modified before the specified time. * **MaxResults** (*integer*) -- The maximum number of Git repositories to return in the response. * **NameContains** (*string*) -- A string in the Git repositories name. This filter returns only repositories whose name contains the specified string. * **NextToken** (*string*) -- If the result of a "ListCodeRepositoriesOutput" request was truncated, the response includes a "NextToken". To get the next set of Git repositories, use the token in the next request. * **SortBy** (*string*) -- The field to sort results by. The default is "Name". * **SortOrder** (*string*) -- The sort order for results. The default is "Ascending". Return type: dict Returns: **Response Syntax** { 'CodeRepositorySummaryList': [ { 'CodeRepositoryName': 'string', 'CodeRepositoryArn': 'string', 'CreationTime': datetime(2015, 1, 1), 'LastModifiedTime': datetime(2015, 1, 1), 'GitConfig': { 'RepositoryUrl': 'string', 'Branch': 'string', 'SecretArn': 'string' } }, ], 'NextToken': 'string' } **Response Structure** * *(dict) --* * **CodeRepositorySummaryList** *(list) --* Gets a list of summaries of the Git repositories. Each summary specifies the following values for the repository: * Name * Amazon Resource Name (ARN) * Creation time * Last modified time * Configuration information, including the URL location of the repository and the ARN of the Amazon Web Services Secrets Manager secret that contains the credentials used to access the repository. * *(dict) --* Specifies summary information about a Git repository. * **CodeRepositoryName** *(string) --* The name of the Git repository. * **CodeRepositoryArn** *(string) --* The Amazon Resource Name (ARN) of the Git repository. * **CreationTime** *(datetime) --* The date and time that the Git repository was created. * **LastModifiedTime** *(datetime) --* The date and time that the Git repository was last modified. * **GitConfig** *(dict) --* Configuration details for the Git repository, including the URL where it is located and the ARN of the Amazon Web Services Secrets Manager secret that contains the credentials used to access the repository. * **RepositoryUrl** *(string) --* The URL where the Git repository is located. * **Branch** *(string) --* The default branch for the Git repository. * **SecretArn** *(string) --* The Amazon Resource Name (ARN) of the Amazon Web Services Secrets Manager secret that contains the credentials used to access the git repository. The secret must have a staging label of "AWSCURRENT" and must be in the following format: "{"username": UserName, "password": Password}" * **NextToken** *(string) --* If the result of a "ListCodeRepositoriesOutput" request was truncated, the response includes a "NextToken". To get the next set of Git repositories, use the token in the next request. SageMaker / Client / describe_compilation_job describe_compilation_job ************************ SageMaker.Client.describe_compilation_job(**kwargs) Returns information about a model compilation job. To create a model compilation job, use CreateCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs. See also: AWS API Documentation **Request Syntax** response = client.describe_compilation_job( CompilationJobName='string' ) Parameters: **CompilationJobName** (*string*) -- **[REQUIRED]** The name of the model compilation job that you want information about. Return type: dict Returns: **Response Syntax** { 'CompilationJobName': 'string', 'CompilationJobArn': 'string', 'CompilationJobStatus': 'INPROGRESS'|'COMPLETED'|'FAILED'|'STARTING'|'STOPPING'|'STOPPED', 'CompilationStartTime': datetime(2015, 1, 1), 'CompilationEndTime': datetime(2015, 1, 1), 'StoppingCondition': { 'MaxRuntimeInSeconds': 123, 'MaxWaitTimeInSeconds': 123, 'MaxPendingTimeInSeconds': 123 }, 'InferenceImage': 'string', 'ModelPackageVersionArn': 'string', 'CreationTime': datetime(2015, 1, 1), 'LastModifiedTime': datetime(2015, 1, 1), 'FailureReason': 'string', 'ModelArtifacts': { 'S3ModelArtifacts': 'string' }, 'ModelDigests': { 'ArtifactDigest': 'string' }, 'RoleArn': 'string', 'InputConfig': { 'S3Uri': 'string', 'DataInputConfig': 'string', 'Framework': 'TENSORFLOW'|'KERAS'|'MXNET'|'ONNX'|'PYTORCH'|'XGBOOST'|'TFLITE'|'DARKNET'|'SKLEARN', 'FrameworkVersion': 'string' }, 'OutputConfig': { 'S3OutputLocation': 'string', 'TargetDevice': 'lambda'|'ml_m4'|'ml_m5'|'ml_m6g'|'ml_c4'|'ml_c5'|'ml_c6g'|'ml_p2'|'ml_p3'|'ml_g4dn'|'ml_inf1'|'ml_inf2'|'ml_trn1'|'ml_eia2'|'jetson_tx1'|'jetson_tx2'|'jetson_nano'|'jetson_xavier'|'rasp3b'|'rasp4b'|'imx8qm'|'deeplens'|'rk3399'|'rk3288'|'aisage'|'sbe_c'|'qcs605'|'qcs603'|'sitara_am57x'|'amba_cv2'|'amba_cv22'|'amba_cv25'|'x86_win32'|'x86_win64'|'coreml'|'jacinto_tda4vm'|'imx8mplus', 'TargetPlatform': { 'Os': 'ANDROID'|'LINUX', 'Arch': 'X86_64'|'X86'|'ARM64'|'ARM_EABI'|'ARM_EABIHF', 'Accelerator': 'INTEL_GRAPHICS'|'MALI'|'NVIDIA'|'NNA' }, 'CompilerOptions': 'string', 'KmsKeyId': 'string' }, 'VpcConfig': { 'SecurityGroupIds': [ 'string', ], 'Subnets': [ 'string', ] }, 'DerivedInformation': { 'DerivedDataInputConfig': 'string' } } **Response Structure** * *(dict) --* * **CompilationJobName** *(string) --* The name of the model compilation job. * **CompilationJobArn** *(string) --* The Amazon Resource Name (ARN) of the model compilation job. * **CompilationJobStatus** *(string) --* The status of the model compilation job. * **CompilationStartTime** *(datetime) --* The time when the model compilation job started the "CompilationJob" instances. You are billed for the time between this timestamp and the timestamp in the "CompilationEndTime" field. In Amazon CloudWatch Logs, the start time might be later than this time. That's because it takes time to download the compilation job, which depends on the size of the compilation job container. * **CompilationEndTime** *(datetime) --* The time when the model compilation job on a compilation job instance ended. For a successful or stopped job, this is when the job's model artifacts have finished uploading. For a failed job, this is when Amazon SageMaker AI detected that the job failed. * **StoppingCondition** *(dict) --* Specifies a limit to how long a model compilation job can run. When the job reaches the time limit, Amazon SageMaker AI ends the compilation job. Use this API to cap model training costs. * **MaxRuntimeInSeconds** *(integer) --* The maximum length of time, in seconds, that a training or compilation job can run before it is stopped. For compilation jobs, if the job does not complete during this time, a "TimeOut" error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model. For all other jobs, if the job does not complete during this time, SageMaker ends the job. When "RetryStrategy" is specified in the job request, "MaxRuntimeInSeconds" specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days. The maximum time that a "TrainingJob" can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days. * **MaxWaitTimeInSeconds** *(integer) --* The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than "MaxRuntimeInSeconds". If the job does not complete during this time, SageMaker ends the job. When "RetryStrategy" is specified in the job request, "MaxWaitTimeInSeconds" specifies the maximum time for all of the attempts in total, not each individual attempt. * **MaxPendingTimeInSeconds** *(integer) --* The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped. Note: When working with training jobs that use capacity from training plans, not all "Pending" job states count against the "MaxPendingTimeInSeconds" limit. The following scenarios do not increment the "MaxPendingTimeInSeconds" counter: * The plan is in a "Scheduled" state: Jobs queued (in "Pending" status) before a plan's start date (waiting for scheduled start time) * Between capacity reservations: Jobs temporarily back to "Pending" status between two capacity reservation periods "MaxPendingTimeInSeconds" only increments when jobs are actively waiting for capacity in an "Active" plan. * **InferenceImage** *(string) --* The inference image to use when compiling a model. Specify an image only if the target device is a cloud instance. * **ModelPackageVersionArn** *(string) --* The Amazon Resource Name (ARN) of the versioned model package that was provided to SageMaker Neo when you initiated a compilation job. * **CreationTime** *(datetime) --* The time that the model compilation job was created. * **LastModifiedTime** *(datetime) --* The time that the status of the model compilation job was last modified. * **FailureReason** *(string) --* If a model compilation job failed, the reason it failed. * **ModelArtifacts** *(dict) --* Information about the location in Amazon S3 that has been configured for storing the model artifacts used in the compilation job. * **S3ModelArtifacts** *(string) --* The path of the S3 object that contains the model artifacts. For example, "s3://bucket- name/keynameprefix/model.tar.gz". * **ModelDigests** *(dict) --* Provides a BLAKE2 hash value that identifies the compiled model artifacts in Amazon S3. * **ArtifactDigest** *(string) --* Provides a hash value that uniquely identifies the stored model artifacts. * **RoleArn** *(string) --* The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker AI assumes to perform the model compilation job. * **InputConfig** *(dict) --* Information about the location in Amazon S3 of the input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained. * **S3Uri** *(string) --* The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). * **DataInputConfig** *(string) --* Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. The data inputs are "Framework" specific. * "TensorFlow": You must specify the name and shape (NHWC format) of the expected data inputs using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different. * Examples for one input: * If using the console, "{"input":[1,1024,1024,3]}" * If using the CLI, "{\"input\":[1,1024,1024,3]}" * Examples for two inputs: * If using the console, "{"data1": [1,28,28,1], "data2":[1,28,28,1]}" * If using the CLI, "{\"data1\": [1,28,28,1], \"data2\":[1,28,28,1]}" * "KERAS": You must specify the name and shape (NCHW format) of expected data inputs using a dictionary format for your trained model. Note that while Keras model artifacts should be uploaded in NHWC (channel- last) format, "DataInputConfig" should be specified in NCHW (channel-first) format. The dictionary formats required for the console and CLI are different. * Examples for one input: * If using the console, "{"input_1":[1,3,224,224]}" * If using the CLI, "{\"input_1\":[1,3,224,224]}" * Examples for two inputs: * If using the console, "{"input_1": [1,3,224,224], "input_2":[1,3,224,224]}" * If using the CLI, "{\"input_1\": [1,3,224,224], \"input_2\":[1,3,224,224]}" * "MXNET/ONNX/DARKNET": You must specify the name and shape (NCHW format) of the expected data inputs in order using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different. * Examples for one input: * If using the console, "{"data":[1,3,1024,1024]}" * If using the CLI, "{\"data\":[1,3,1024,1024]}" * Examples for two inputs: * If using the console, "{"var1": [1,1,28,28], "var2":[1,1,28,28]}" * If using the CLI, "{\"var1\": [1,1,28,28], \"var2\":[1,1,28,28]}" * "PyTorch": You can either specify the name and shape (NCHW format) of expected data inputs in order using a dictionary format for your trained model or you can specify the shape only using a list format. The dictionary formats required for the console and CLI are different. The list formats for the console and CLI are the same. * Examples for one input in dictionary format: * If using the console, "{"input0":[1,3,224,224]}" * If using the CLI, "{\"input0\":[1,3,224,224]}" * Example for one input in list format: "[[1,3,224,224]]" * Examples for two inputs in dictionary format: * If using the console, "{"input0":[1,3,224,224], "input1":[1,3,224,224]}" * If using the CLI, "{\"input0\":[1,3,224,224], \"input1\":[1,3,224,224]}" * Example for two inputs in list format: "[[1,3,224,224], [1,3,224,224]]" * "XGBOOST": input data name and shape are not needed. "DataInputConfig" supports the following parameters for "CoreML" "TargetDevice" (ML Model format): * "shape": Input shape, for example "{"input_1": {"shape": [1,224,224,3]}}". In addition to static input shapes, CoreML converter supports Flexible input shapes: * Range Dimension. You can use the Range Dimension feature if you know the input shape will be within some specific interval in that dimension, for example: "{"input_1": {"shape": ["1..10", 224, 224, 3]}}" * Enumerated shapes. Sometimes, the models are trained to work only on a select set of inputs. You can enumerate all supported input shapes, for example: "{"input_1": {"shape": [[1, 224, 224, 3], [1, 160, 160, 3]]}}" * "default_shape": Default input shape. You can set a default shape during conversion for both Range Dimension and Enumerated Shapes. For example "{"input_1": {"shape": ["1..10", 224, 224, 3], "default_shape": [1, 224, 224, 3]}}" * "type": Input type. Allowed values: "Image" and "Tensor". By default, the converter generates an ML Model with inputs of type Tensor (MultiArray). User can set input type to be Image. Image input type requires additional input parameters such as "bias" and "scale". * "bias": If the input type is an Image, you need to provide the bias vector. * "scale": If the input type is an Image, you need to provide a scale factor. CoreML "ClassifierConfig" parameters can be specified using OutputConfig "CompilerOptions". CoreML converter supports Tensorflow and PyTorch models. CoreML conversion examples: * Tensor type input: * ""DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3]}}" * Tensor type input without input name (PyTorch): * ""DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "default_shape": [1,3,224,224]}]" * Image type input: * ""DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}}" * ""CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}" * Image type input without input name (PyTorch): * ""DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "default_shape": [1,3,224,224], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}]" * ""CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}" Depending on the model format, "DataInputConfig" requires the following parameters for "ml_eia2" OutputConfig:TargetDevice. * For TensorFlow models saved in the SavedModel format, specify the input names from "signature_def_key" and the input model shapes for "DataInputConfig". Specify the "signature_def_key" in OutputConfig:CompilerOptions if the model does not use TensorFlow's default signature def key. For example: * ""DataInputConfig": {"inputs": [1, 224, 224, 3]}" * ""CompilerOptions": {"signature_def_key": "serving_custom"}" * For TensorFlow models saved as a frozen graph, specify the input tensor names and shapes in "DataInputConfig" and the output tensor names for "output_names" in OutputConfig:CompilerOptions. For example: * ""DataInputConfig": {"input_tensor:0": [1, 224, 224, 3]}" * ""CompilerOptions": {"output_names": ["output_tensor:0"]}" * **Framework** *(string) --* Identifies the framework in which the model was trained. For example: TENSORFLOW. * **FrameworkVersion** *(string) --* Specifies the framework version to use. This API field is only supported for the MXNet, PyTorch, TensorFlow and TensorFlow Lite frameworks. For information about framework versions supported for cloud targets and edge devices, see Cloud Supported Instance Types and Frameworks and Edge Supported Frameworks. * **OutputConfig** *(dict) --* Information about the output location for the compiled model and the target device that the model runs on. * **S3OutputLocation** *(string) --* Identifies the S3 bucket where you want Amazon SageMaker AI to store the model artifacts. For example, "s3 ://bucket-name/key-name-prefix". * **TargetDevice** *(string) --* Identifies the target device or the machine learning instance that you want to run your model on after the compilation has completed. Alternatively, you can specify OS, architecture, and accelerator using TargetPlatform fields. It can be used instead of "TargetPlatform". Note: Currently "ml_trn1" is available only in US East (N. Virginia) Region, and "ml_inf2" is available only in US East (Ohio) Region. * **TargetPlatform** *(dict) --* Contains information about a target platform that you want your model to run on, such as OS, architecture, and accelerators. It is an alternative of "TargetDevice". The following examples show how to configure the "TargetPlatform" and "CompilerOptions" JSON strings for popular target platforms: * Raspberry Pi 3 Model B+ ""TargetPlatform": {"Os": "LINUX", "Arch": "ARM_EABIHF"}," ""CompilerOptions": {'mattr': ['+neon']}" * Jetson TX2 ""TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "NVIDIA"}," ""CompilerOptions": {'gpu-code': 'sm_62', 'trt-ver': '6.0.1', 'cuda-ver': '10.0'}" * EC2 m5.2xlarge instance OS ""TargetPlatform": {"Os": "LINUX", "Arch": "X86_64", "Accelerator": "NVIDIA"}," ""CompilerOptions": {'mcpu': 'skylake-avx512'}" * RK3399 ""TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "MALI"}" * ARMv7 phone (CPU) ""TargetPlatform": {"Os": "ANDROID", "Arch": "ARM_EABI"}," ""CompilerOptions": {'ANDROID_PLATFORM': 25, 'mattr': ['+neon']}" * ARMv8 phone (CPU) ""TargetPlatform": {"Os": "ANDROID", "Arch": "ARM64"}," ""CompilerOptions": {'ANDROID_PLATFORM': 29}" * **Os** *(string) --* Specifies a target platform OS. * "LINUX": Linux-based operating systems. * "ANDROID": Android operating systems. Android API level can be specified using the "ANDROID_PLATFORM" compiler option. For example, ""CompilerOptions": {'ANDROID_PLATFORM': 28}" * **Arch** *(string) --* Specifies a target platform architecture. * "X86_64": 64-bit version of the x86 instruction set. * "X86": 32-bit version of the x86 instruction set. * "ARM64": ARMv8 64-bit CPU. * "ARM_EABIHF": ARMv7 32-bit, Hard Float. * "ARM_EABI": ARMv7 32-bit, Soft Float. Used by Android 32-bit ARM platform. * **Accelerator** *(string) --* Specifies a target platform accelerator (optional). * "NVIDIA": Nvidia graphics processing unit. It also requires "gpu-code", "trt-ver", "cuda-ver" compiler options * "MALI": ARM Mali graphics processor * "INTEL_GRAPHICS": Integrated Intel graphics * **CompilerOptions** *(string) --* Specifies additional parameters for compiler options in JSON format. The compiler options are "TargetPlatform" specific. It is required for NVIDIA accelerators and highly recommended for CPU compilations. For any other cases, it is optional to specify "CompilerOptions." * "DTYPE": Specifies the data type for the input. When compiling for "ml_*" (except for "ml_inf") instances using PyTorch framework, provide the data type (dtype) of the model's input. ""float32"" is used if ""DTYPE"" is not specified. Options for data type are: * float32: Use either ""float"" or ""float32"". * int64: Use either ""int64"" or ""long"". For example, "{"dtype" : "float32"}". * "CPU": Compilation for CPU supports the following compiler options. * "mcpu": CPU micro-architecture. For example, "{'mcpu': 'skylake-avx512'}" * "mattr": CPU flags. For example, "{'mattr': ['+neon', '+vfpv4']}" * "ARM": Details of ARM CPU compilations. * "NEON": NEON is an implementation of the Advanced SIMD extension used in ARMv7 processors. For example, add "{'mattr': ['+neon']}" to the compiler options if compiling for ARM 32-bit platform with the NEON support. * "NVIDIA": Compilation for NVIDIA GPU supports the following compiler options. * "gpu_code": Specifies the targeted architecture. * "trt-ver": Specifies the TensorRT versions in x.y.z. format. * "cuda-ver": Specifies the CUDA version in x.y format. For example, "{'gpu-code': 'sm_72', 'trt-ver': '6.0.1', 'cuda-ver': '10.1'}" * "ANDROID": Compilation for the Android OS supports the following compiler options: * "ANDROID_PLATFORM": Specifies the Android API levels. Available levels range from 21 to 29. For example, "{'ANDROID_PLATFORM': 28}". * "mattr": Add "{'mattr': ['+neon']}" to compiler options if compiling for ARM 32-bit platform with NEON support. * "INFERENTIA": Compilation for target ml_inf1 uses compiler options passed in as a JSON string. For example, ""CompilerOptions": "\"--verbose 1 --num- neuroncores 2 -O2\""". For information about supported compiler options, see Neuron Compiler CLI Reference Guide. * "CoreML": Compilation for the CoreML OutputConfig "TargetDevice" supports the following compiler options: * "class_labels": Specifies the classification labels file name inside input tar.gz file. For example, "{"class_labels": "imagenet_labels_1000.txt"}". Labels inside the txt file should be separated by newlines. * **KmsKeyId** *(string) --* The Amazon Web Services Key Management Service key (Amazon Web Services KMS) that Amazon SageMaker AI uses to encrypt your output models with Amazon S3 server-side encryption after compilation job. If you don't provide a KMS key ID, Amazon SageMaker AI uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS- Managed Encryption Keys in the *Amazon Simple Storage Service Developer Guide.* The KmsKeyId can be any of the following formats: * Key ID: "1234abcd-12ab-34cd-56ef-1234567890ab" * Key ARN: "arn:aws:kms:us-west-2:111122223333:key /1234abcd-12ab-34cd-56ef-1234567890ab" * Alias name: "alias/ExampleAlias" * Alias name ARN: "arn:aws:kms:us- west-2:111122223333:alias/ExampleAlias" * **VpcConfig** *(dict) --* A VpcConfig object that specifies the VPC that you want your compilation job to connect to. Control access to your models by configuring the VPC. For more information, see Protect Compilation Jobs by Using an Amazon Virtual Private Cloud. * **SecurityGroupIds** *(list) --* The VPC security group IDs. IDs have the form of "sg- xxxxxxxx". Specify the security groups for the VPC that is specified in the "Subnets" field. * *(string) --* * **Subnets** *(list) --* The ID of the subnets in the VPC that you want to connect the compilation job to for accessing the model in Amazon S3. * *(string) --* * **DerivedInformation** *(dict) --* Information that SageMaker Neo automatically derived about the model. * **DerivedDataInputConfig** *(string) --* The data input configuration that SageMaker Neo automatically derived for the model. When SageMaker Neo derives this information, you don't need to specify the data input configuration when you create a compilation job. **Exceptions** * "SageMaker.Client.exceptions.ResourceNotFound" SageMaker / Client / create_flow_definition create_flow_definition ********************** SageMaker.Client.create_flow_definition(**kwargs) Creates a flow definition. See also: AWS API Documentation **Request Syntax** response = client.create_flow_definition( FlowDefinitionName='string', HumanLoopRequestSource={ 'AwsManagedHumanLoopRequestSource': 'AWS/Rekognition/DetectModerationLabels/Image/V3'|'AWS/Textract/AnalyzeDocument/Forms/V1' }, HumanLoopActivationConfig={ 'HumanLoopActivationConditionsConfig': { 'HumanLoopActivationConditions': 'string' } }, HumanLoopConfig={ 'WorkteamArn': 'string', 'HumanTaskUiArn': 'string', 'TaskTitle': 'string', 'TaskDescription': 'string', 'TaskCount': 123, 'TaskAvailabilityLifetimeInSeconds': 123, 'TaskTimeLimitInSeconds': 123, 'TaskKeywords': [ 'string', ], 'PublicWorkforceTaskPrice': { 'AmountInUsd': { 'Dollars': 123, 'Cents': 123, 'TenthFractionsOfACent': 123 } } }, OutputConfig={ 'S3OutputPath': 'string', 'KmsKeyId': 'string' }, RoleArn='string', Tags=[ { 'Key': 'string', 'Value': 'string' }, ] ) Parameters: * **FlowDefinitionName** (*string*) -- **[REQUIRED]** The name of your flow definition. * **HumanLoopRequestSource** (*dict*) -- Container for configuring the source of human task requests. Use to specify if Amazon Rekognition or Amazon Textract is used as an integration source. * **AwsManagedHumanLoopRequestSource** *(string) --* **[REQUIRED]** Specifies whether Amazon Rekognition or Amazon Textract are used as the integration source. The default field settings and JSON parsing rules are different based on the integration source. Valid values: * **HumanLoopActivationConfig** (*dict*) -- An object containing information about the events that trigger a human workflow. * **HumanLoopActivationConditionsConfig** *(dict) --* **[REQUIRED]** Container structure for defining under what conditions SageMaker creates a human loop. * **HumanLoopActivationConditions** *(string) --* **[REQUIRED]** JSON expressing use-case specific conditions declaratively. If any condition is matched, atomic tasks are created against the configured work team. The set of conditions is different for Rekognition and Textract. For more information about how to structure the JSON, see JSON Schema for Human Loop Activation Conditions in Amazon Augmented AI in the *Amazon SageMaker Developer Guide*. * **HumanLoopConfig** (*dict*) -- An object containing information about the tasks the human reviewers will perform. * **WorkteamArn** *(string) --* **[REQUIRED]** Amazon Resource Name (ARN) of a team of workers. To learn more about the types of workforces and work teams you can create and use with Amazon A2I, see Create and Manage Workforces. * **HumanTaskUiArn** *(string) --* **[REQUIRED]** The Amazon Resource Name (ARN) of the human task user interface. You can use standard HTML and Crowd HTML Elements to create a custom worker task template. You use this template to create a human task UI. To learn how to create a custom HTML template, see Create Custom Worker Task Template. To learn how to create a human task UI, which is a worker task template that can be used in a flow definition, see Create and Delete a Worker Task Templates. * **TaskTitle** *(string) --* **[REQUIRED]** A title for the human worker task. * **TaskDescription** *(string) --* **[REQUIRED]** A description for the human worker task. * **TaskCount** *(integer) --* **[REQUIRED]** The number of distinct workers who will perform the same task on each object. For example, if "TaskCount" is set to "3" for an image classification labeling job, three workers will classify each input image. Increasing "TaskCount" can improve label accuracy. * **TaskAvailabilityLifetimeInSeconds** *(integer) --* The length of time that a task remains available for review by human workers. * **TaskTimeLimitInSeconds** *(integer) --* The amount of time that a worker has to complete a task. The default value is 3,600 seconds (1 hour). * **TaskKeywords** *(list) --* Keywords used to describe the task so that workers can discover the task. * *(string) --* * **PublicWorkforceTaskPrice** *(dict) --* Defines the amount of money paid to an Amazon Mechanical Turk worker for each task performed. Use one of the following prices for bounding box tasks. Prices are in US dollars and should be based on the complexity of the task; the longer it takes in your initial testing, the more you should offer. * 0.036 * 0.048 * 0.060 * 0.072 * 0.120 * 0.240 * 0.360 * 0.480 * 0.600 * 0.720 * 0.840 * 0.960 * 1.080 * 1.200 Use one of the following prices for image classification, text classification, and custom tasks. Prices are in US dollars. * 0.012 * 0.024 * 0.036 * 0.048 * 0.060 * 0.072 * 0.120 * 0.240 * 0.360 * 0.480 * 0.600 * 0.720 * 0.840 * 0.960 * 1.080 * 1.200 Use one of the following prices for semantic segmentation tasks. Prices are in US dollars. * 0.840 * 0.960 * 1.080 * 1.200 Use one of the following prices for Textract AnalyzeDocument Important Form Key Amazon Augmented AI review tasks. Prices are in US dollars. * 2.400 * 2.280 * 2.160 * 2.040 * 1.920 * 1.800 * 1.680 * 1.560 * 1.440 * 1.320 * 1.200 * 1.080 * 0.960 * 0.840 * 0.720 * 0.600 * 0.480 * 0.360 * 0.240 * 0.120 * 0.072 * 0.060 * 0.048 * 0.036 * 0.024 * 0.012 Use one of the following prices for Rekognition DetectModerationLabels Amazon Augmented AI review tasks. Prices are in US dollars. * 1.200 * 1.080 * 0.960 * 0.840 * 0.720 * 0.600 * 0.480 * 0.360 * 0.240 * 0.120 * 0.072 * 0.060 * 0.048 * 0.036 * 0.024 * 0.012 Use one of the following prices for Amazon Augmented AI custom human review tasks. Prices are in US dollars. * 1.200 * 1.080 * 0.960 * 0.840 * 0.720 * 0.600 * 0.480 * 0.360 * 0.240 * 0.120 * 0.072 * 0.060 * 0.048 * 0.036 * 0.024 * 0.012 * **AmountInUsd** *(dict) --* Defines the amount of money paid to an Amazon Mechanical Turk worker in United States dollars. * **Dollars** *(integer) --* The whole number of dollars in the amount. * **Cents** *(integer) --* The fractional portion, in cents, of the amount. * **TenthFractionsOfACent** *(integer) --* Fractions of a cent, in tenths. * **OutputConfig** (*dict*) -- **[REQUIRED]** An object containing information about where the human review results will be uploaded. * **S3OutputPath** *(string) --* **[REQUIRED]** The Amazon S3 path where the object containing human output will be made available. To learn more about the format of Amazon A2I output data, see Amazon A2I Output Data. * **KmsKeyId** *(string) --* The Amazon Key Management Service (KMS) key ID for server- side encryption. * **RoleArn** (*string*) -- **[REQUIRED]** The Amazon Resource Name (ARN) of the role needed to call other services on your behalf. For example, "arn:aws:iam::1234567890:role/service-role/AmazonSageMaker- ExecutionRole-20180111T151298". * **Tags** (*list*) -- An array of key-value pairs that contain metadata to help you categorize and organize a flow definition. Each tag consists of a key and a value, both of which you define. * *(dict) --* A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources. You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags. For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy. * **Key** *(string) --* **[REQUIRED]** The tag key. Tag keys must be unique per resource. * **Value** *(string) --* **[REQUIRED]** The tag value. Return type: dict Returns: **Response Syntax** { 'FlowDefinitionArn': 'string' } **Response Structure** * *(dict) --* * **FlowDefinitionArn** *(string) --* The Amazon Resource Name (ARN) of the flow definition you create. **Exceptions** * "SageMaker.Client.exceptions.ResourceInUse" * "SageMaker.Client.exceptions.ResourceLimitExceeded" SageMaker / Client / list_artifacts list_artifacts ************** SageMaker.Client.list_artifacts(**kwargs) Lists the artifacts in your account and their properties. See also: AWS API Documentation **Request Syntax** response = client.list_artifacts( SourceUri='string', ArtifactType='string', CreatedAfter=datetime(2015, 1, 1), CreatedBefore=datetime(2015, 1, 1), SortBy='CreationTime', SortOrder='Ascending'|'Descending', NextToken='string', MaxResults=123 ) Parameters: * **SourceUri** (*string*) -- A filter that returns only artifacts with the specified source URI. * **ArtifactType** (*string*) -- A filter that returns only artifacts of the specified type. * **CreatedAfter** (*datetime*) -- A filter that returns only artifacts created on or after the specified time. * **CreatedBefore** (*datetime*) -- A filter that returns only artifacts created on or before the specified time. * **SortBy** (*string*) -- The property used to sort results. The default value is "CreationTime". * **SortOrder** (*string*) -- The sort order. The default value is "Descending". * **NextToken** (*string*) -- If the previous call to "ListArtifacts" didn't return the full set of artifacts, the call returns a token for getting the next set of artifacts. * **MaxResults** (*integer*) -- The maximum number of artifacts to return in the response. The default value is 10. Return type: dict Returns: **Response Syntax** { 'ArtifactSummaries': [ { 'ArtifactArn': 'string', 'ArtifactName': 'string', 'Source': { 'SourceUri': 'string', 'SourceTypes': [ { 'SourceIdType': 'MD5Hash'|'S3ETag'|'S3Version'|'Custom', 'Value': 'string' }, ] }, 'ArtifactType': 'string', 'CreationTime': datetime(2015, 1, 1), 'LastModifiedTime': datetime(2015, 1, 1) }, ], 'NextToken': 'string' } **Response Structure** * *(dict) --* * **ArtifactSummaries** *(list) --* A list of artifacts and their properties. * *(dict) --* Lists a summary of the properties of an artifact. An artifact represents a URI addressable object or data. Some examples are a dataset and a model. * **ArtifactArn** *(string) --* The Amazon Resource Name (ARN) of the artifact. * **ArtifactName** *(string) --* The name of the artifact. * **Source** *(dict) --* The source of the artifact. * **SourceUri** *(string) --* The URI of the source. * **SourceTypes** *(list) --* A list of source types. * *(dict) --* The ID and ID type of an artifact source. * **SourceIdType** *(string) --* The type of ID. * **Value** *(string) --* The ID. * **ArtifactType** *(string) --* The type of the artifact. * **CreationTime** *(datetime) --* When the artifact was created. * **LastModifiedTime** *(datetime) --* When the artifact was last modified. * **NextToken** *(string) --* A token for getting the next set of artifacts, if there are any. **Exceptions** * "SageMaker.Client.exceptions.ResourceNotFound" SageMaker / Client / update_devices update_devices ************** SageMaker.Client.update_devices(**kwargs) Updates one or more devices in a fleet. See also: AWS API Documentation **Request Syntax** response = client.update_devices( DeviceFleetName='string', Devices=[ { 'DeviceName': 'string', 'Description': 'string', 'IotThingName': 'string' }, ] ) Parameters: * **DeviceFleetName** (*string*) -- **[REQUIRED]** The name of the fleet the devices belong to. * **Devices** (*list*) -- **[REQUIRED]** List of devices to register with Edge Manager agent. * *(dict) --* Information of a particular device. * **DeviceName** *(string) --* **[REQUIRED]** The name of the device. * **Description** *(string) --* Description of the device. * **IotThingName** *(string) --* Amazon Web Services Internet of Things (IoT) object name. Returns: None SageMaker / Client / delete_model_bias_job_definition delete_model_bias_job_definition ******************************** SageMaker.Client.delete_model_bias_job_definition(**kwargs) Deletes an Amazon SageMaker AI model bias job definition. See also: AWS API Documentation **Request Syntax** response = client.delete_model_bias_job_definition( JobDefinitionName='string' ) Parameters: **JobDefinitionName** (*string*) -- **[REQUIRED]** The name of the model bias job definition to delete. Returns: None **Exceptions** * "SageMaker.Client.exceptions.ResourceNotFound" SageMaker / Client / list_model_cards list_model_cards **************** SageMaker.Client.list_model_cards(**kwargs) List existing model cards. See also: AWS API Documentation **Request Syntax** response = client.list_model_cards( CreationTimeAfter=datetime(2015, 1, 1), CreationTimeBefore=datetime(2015, 1, 1), MaxResults=123, NameContains='string', ModelCardStatus='Draft'|'PendingReview'|'Approved'|'Archived', NextToken='string', SortBy='Name'|'CreationTime', SortOrder='Ascending'|'Descending' ) Parameters: * **CreationTimeAfter** (*datetime*) -- Only list model cards that were created after the time specified. * **CreationTimeBefore** (*datetime*) -- Only list model cards that were created before the time specified. * **MaxResults** (*integer*) -- The maximum number of model cards to list. * **NameContains** (*string*) -- Only list model cards with names that contain the specified string. * **ModelCardStatus** (*string*) -- Only list model cards with the specified approval status. * **NextToken** (*string*) -- If the response to a previous "ListModelCards" request was truncated, the response includes a "NextToken". To retrieve the next set of model cards, use the token in the next request. * **SortBy** (*string*) -- Sort model cards by either name or creation time. Sorts by creation time by default. * **SortOrder** (*string*) -- Sort model cards by ascending or descending order. Return type: dict Returns: **Response Syntax** { 'ModelCardSummaries': [ { 'ModelCardName': 'string', 'ModelCardArn': 'string', 'ModelCardStatus': 'Draft'|'PendingReview'|'Approved'|'Archived', 'CreationTime': datetime(2015, 1, 1), 'LastModifiedTime': datetime(2015, 1, 1) }, ], 'NextToken': 'string' } **Response Structure** * *(dict) --* * **ModelCardSummaries** *(list) --* The summaries of the listed model cards. * *(dict) --* A summary of the model card. * **ModelCardName** *(string) --* The name of the model card. * **ModelCardArn** *(string) --* The Amazon Resource Name (ARN) of the model card. * **ModelCardStatus** *(string) --* The approval status of the model card within your organization. Different organizations might have different criteria for model card review and approval. * "Draft": The model card is a work in progress. * "PendingReview": The model card is pending review. * "Approved": The model card is approved. * "Archived": The model card is archived. No more updates should be made to the model card, but it can still be exported. * **CreationTime** *(datetime) --* The date and time that the model card was created. * **LastModifiedTime** *(datetime) --* The date and time that the model card was last modified. * **NextToken** *(string) --* If the response is truncated, SageMaker returns this token. To retrieve the next set of model cards, use it in the subsequent request. SageMaker / Client / create_hyper_parameter_tuning_job create_hyper_parameter_tuning_job ********************************* SageMaker.Client.create_hyper_parameter_tuning_job(**kwargs) Starts a hyperparameter tuning job. A hyperparameter tuning job finds the best version of a model by running many training jobs on your dataset using the algorithm you choose and values for hyperparameters within ranges that you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured by an objective metric that you choose. A hyperparameter tuning job automatically creates Amazon SageMaker experiments, trials, and trial components for each training job that it runs. You can view these entities in Amazon SageMaker Studio. For more information, see View Experiments, Trials, and Trial Components. Warning: Do not include any security-sensitive information including account access IDs, secrets, or tokens in any hyperparameter fields. As part of the shared responsibility model, you are responsible for any potential exposure, unauthorized access, or compromise of your sensitive data if caused by any security- sensitive information included in the request hyperparameter variable or plain text fields.. See also: AWS API Documentation **Request Syntax** response = client.create_hyper_parameter_tuning_job( HyperParameterTuningJobName='string', HyperParameterTuningJobConfig={ 'Strategy': 'Bayesian'|'Random'|'Hyperband'|'Grid', 'StrategyConfig': { 'HyperbandStrategyConfig': { 'MinResource': 123, 'MaxResource': 123 } }, 'HyperParameterTuningJobObjective': { 'Type': 'Maximize'|'Minimize', 'MetricName': 'string' }, 'ResourceLimits': { 'MaxNumberOfTrainingJobs': 123, 'MaxParallelTrainingJobs': 123, 'MaxRuntimeInSeconds': 123 }, 'ParameterRanges': { 'IntegerParameterRanges': [ { 'Name': 'string', 'MinValue': 'string', 'MaxValue': 'string', 'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic' }, ], 'ContinuousParameterRanges': [ { 'Name': 'string', 'MinValue': 'string', 'MaxValue': 'string', 'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic' }, ], 'CategoricalParameterRanges': [ { 'Name': 'string', 'Values': [ 'string', ] }, ], 'AutoParameters': [ { 'Name': 'string', 'ValueHint': 'string' }, ] }, 'TrainingJobEarlyStoppingType': 'Off'|'Auto', 'TuningJobCompletionCriteria': { 'TargetObjectiveMetricValue': ..., 'BestObjectiveNotImproving': { 'MaxNumberOfTrainingJobsNotImproving': 123 }, 'ConvergenceDetected': { 'CompleteOnConvergence': 'Disabled'|'Enabled' } }, 'RandomSeed': 123 }, TrainingJobDefinition={ 'DefinitionName': 'string', 'TuningObjective': { 'Type': 'Maximize'|'Minimize', 'MetricName': 'string' }, 'HyperParameterRanges': { 'IntegerParameterRanges': [ { 'Name': 'string', 'MinValue': 'string', 'MaxValue': 'string', 'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic' }, ], 'ContinuousParameterRanges': [ { 'Name': 'string', 'MinValue': 'string', 'MaxValue': 'string', 'ScalingType': 'Auto'|'Linear'|'Logarithmic'|'ReverseLogarithmic' }, ], 'CategoricalParameterRanges': [ { 'Name': 'string', 'Values': [ 'string', ] }, ], 'AutoParameters': [ { 'Name': 'string', 'ValueHint': 'string' }, ] }, 'StaticHyperParameters': { 'string': 'string' }, 'AlgorithmSpecification': { 'TrainingImage': 'string', 'TrainingInputMode': 'Pipe'|'File'|'FastFile', 'AlgorithmName': 'string', 'MetricDefinitions': [ { 'Name': 'string', 'Regex': 'string' }, ] }, 'RoleArn': 'string', 'InputDataConfig': [ { 'ChannelName': 'string', 'DataSource': { 'S3DataSource': { 'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile'|'Converse', 'S3Uri': 'string', 'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key', 'AttributeNames': [ 'string', ], 'InstanceGroupNames': [ 'string', ], 'ModelAccessConfig': { 'AcceptEula': True|False }, 'HubAccessConfig': { 'HubContentArn': 'string' } }, 'FileSystemDataSource': { 'FileSystemId': 'string', 'FileSystemAccessMode': 'rw'|'ro', 'FileSystemType': 'EFS'|'FSxLustre', 'DirectoryPath': 'string' } }, 'ContentType': 'string', 'CompressionType': 'None'|'Gzip', 'RecordWrapperType': 'None'|'RecordIO', 'InputMode': 'Pipe'|'File'|'FastFile', 'ShuffleConfig': { 'Seed': 123 } }, ], 'VpcConfig': { 'SecurityGroupIds': [ 'string', ], 'Subnets': [ 'string', ] }, 'OutputDataConfig': { 'KmsKeyId': 'string', 'S3OutputPath': 'string', 'CompressionType': 'GZIP'|'NONE' }, 'ResourceConfig': { 'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.p5e.48xlarge'|'ml.p5en.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.16xlarge'|'ml.g6.12xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.16xlarge'|'ml.g6e.12xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.trn2.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.8xlarge'|'ml.r5d.12xlarge'|'ml.r5d.16xlarge'|'ml.r5d.24xlarge'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.p6-b200.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.p6e-gb200.36xlarge', 'InstanceCount': 123, 'VolumeSizeInGB': 123, 'VolumeKmsKeyId': 'string', 'KeepAlivePeriodInSeconds': 123, 'InstanceGroups': [ { 'InstanceType': 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'RecordWrapperType': 'None'|'RecordIO', 'InputMode': 'Pipe'|'File'|'FastFile', 'ShuffleConfig': { 'Seed': 123 } }, ], 'VpcConfig': { 'SecurityGroupIds': [ 'string', ], 'Subnets': [ 'string', ] }, 'OutputDataConfig': { 'KmsKeyId': 'string', 'S3OutputPath': 'string', 'CompressionType': 'GZIP'|'NONE' }, 'ResourceConfig': { 'InstanceType': 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'InstanceCount': 123, 'VolumeSizeInGB': 123, 'VolumeKmsKeyId': 'string', 'KeepAlivePeriodInSeconds': 123, 'InstanceGroups': [ { 'InstanceType': 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'InstanceCount': 123, 'InstanceGroupName': 'string' }, ], 'TrainingPlanArn': 'string', 'InstancePlacementConfig': { 'EnableMultipleJobs': True|False, 'PlacementSpecifications': [ { 'UltraServerId': 'string', 'InstanceCount': 123 }, ] } }, 'HyperParameterTuningResourceConfig': { 'InstanceType': 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'InstanceCount': 123, 'VolumeSizeInGB': 123, 'VolumeKmsKeyId': 'string', 'AllocationStrategy': 'Prioritized', 'InstanceConfigs': [ { 'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.p3dn.24xlarge'|'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.p5e.48xlarge'|'ml.p5en.48xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5n.xlarge'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.16xlarge'|'ml.g6.12xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.16xlarge'|'ml.g6e.12xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.trn2.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.8xlarge'|'ml.c6i.4xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.8xlarge'|'ml.r5d.12xlarge'|'ml.r5d.16xlarge'|'ml.r5d.24xlarge'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.p6-b200.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.p6e-gb200.36xlarge', 'InstanceCount': 123, 'VolumeSizeInGB': 123 }, ] }, 'StoppingCondition': { 'MaxRuntimeInSeconds': 123, 'MaxWaitTimeInSeconds': 123, 'MaxPendingTimeInSeconds': 123 }, 'EnableNetworkIsolation': True|False, 'EnableInterContainerTrafficEncryption': True|False, 'EnableManagedSpotTraining': True|False, 'CheckpointConfig': { 'S3Uri': 'string', 'LocalPath': 'string' }, 'RetryStrategy': { 'MaximumRetryAttempts': 123 }, 'Environment': { 'string': 'string' } }, ], WarmStartConfig={ 'ParentHyperParameterTuningJobs': [ { 'HyperParameterTuningJobName': 'string' }, ], 'WarmStartType': 'IdenticalDataAndAlgorithm'|'TransferLearning' }, Tags=[ { 'Key': 'string', 'Value': 'string' }, ], Autotune={ 'Mode': 'Enabled' } ) Parameters: * **HyperParameterTuningJobName** (*string*) -- **[REQUIRED]** The name of the tuning job. This name is the prefix for the names of all training jobs that this tuning job launches. The name must be unique within the same Amazon Web Services account and Amazon Web Services Region. The name must have 1 to 32 characters. Valid characters are a-z, A-Z, 0-9, and : + = @ _ % - (hyphen). The name is not case sensitive. * **HyperParameterTuningJobConfig** (*dict*) -- **[REQUIRED]** The HyperParameterTuningJobConfig object that describes the tuning job, including the search strategy, the objective metric used to evaluate training jobs, ranges of parameters to search, and resource limits for the tuning job. For more information, see How Hyperparameter Tuning Works. * **Strategy** *(string) --* **[REQUIRED]** Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training job it launches. For information about search strategies, see How Hyperparameter Tuning Works. * **StrategyConfig** *(dict) --* The configuration for the "Hyperband" optimization strategy. This parameter should be provided only if "Hyperband" is selected as the strategy for "HyperParameterTuningJobConfig". * **HyperbandStrategyConfig** *(dict) --* The configuration for the object that specifies the "Hyperband" strategy. This parameter is only supported for the "Hyperband" selection for "Strategy" within the "HyperParameterTuningJobConfig" API. * **MinResource** *(integer) --* The minimum number of resources (such as epochs) that can be used by a training job launched by a hyperparameter tuning job. If the value for "MinResource" has not been reached, the training job is not stopped by "Hyperband". * **MaxResource** *(integer) --* The maximum number of resources (such as epochs) that can be used by a training job launched by a hyperparameter tuning job. Once a job reaches the "MaxResource" value, it is stopped. If a value for "MaxResource" is not provided, and "Hyperband" is selected as the hyperparameter tuning strategy, "HyperbandTraining" attempts to infer "MaxResource" from the following keys (if present) in StaticsHyperParameters: * "epochs" * "numepochs" * "n-epochs" * "n_epochs" * "num_epochs" If "HyperbandStrategyConfig" is unable to infer a value for "MaxResource", it generates a validation error. The maximum value is 20,000 epochs. All metrics that correspond to an objective metric are used to derive early stopping decisions. For distributed training jobs, ensure that duplicate metrics are not printed in the logs across the individual nodes in a training job. If multiple nodes are publishing duplicate or incorrect metrics, training jobs may make an incorrect stopping decision and stop the job prematurely. * **HyperParameterTuningJobObjective** *(dict) --* The HyperParameterTuningJobObjective specifies the objective metric used to evaluate the performance of training jobs launched by this tuning job. * **Type** *(string) --* **[REQUIRED]** Whether to minimize or maximize the objective metric. * **MetricName** *(string) --* **[REQUIRED]** The name of the metric to use for the objective metric. * **ResourceLimits** *(dict) --* **[REQUIRED]** The ResourceLimits object that specifies the maximum number of training and parallel training jobs that can be used for this hyperparameter tuning job. * **MaxNumberOfTrainingJobs** *(integer) --* The maximum number of training jobs that a hyperparameter tuning job can launch. * **MaxParallelTrainingJobs** *(integer) --* **[REQUIRED]** The maximum number of concurrent training jobs that a hyperparameter tuning job can launch. * **MaxRuntimeInSeconds** *(integer) --* The maximum time in seconds that a hyperparameter tuning job can run. * **ParameterRanges** *(dict) --* The ParameterRanges object that specifies the ranges of hyperparameters that this tuning job searches over to find the optimal configuration for the highest model performance against your chosen objective metric. * **IntegerParameterRanges** *(list) --* The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches. * *(dict) --* For a hyperparameter of the integer type, specifies the range that a hyperparameter tuning job searches. * **Name** *(string) --* **[REQUIRED]** The name of the hyperparameter to search. * **MinValue** *(string) --* **[REQUIRED]** The minimum value of the hyperparameter to search. * **MaxValue** *(string) --* **[REQUIRED]** The maximum value of the hyperparameter to search. * **ScalingType** *(string) --* The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values: Auto SageMaker hyperparameter tuning chooses the best scale for the hyperparameter. Linear Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale. Logarithmic Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale. Logarithmic scaling works only for ranges that have only values greater than 0. * **ContinuousParameterRanges** *(list) --* The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches. * *(dict) --* A list of continuous hyperparameters to tune. * **Name** *(string) --* **[REQUIRED]** The name of the continuous hyperparameter to tune. * **MinValue** *(string) --* **[REQUIRED]** The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and >>``<>``<>``<::key/"" * // KMS Key Alias ""alias/ExampleAlias"" * // Amazon Resource Name (ARN) of a KMS Key Alias ""arn:aws:kms:::alias/"" For more information about key identifiers, see Key identifiers (KeyID) in the Amazon Web Services Key Management Service (Amazon Web Services KMS) documentation. * **ContainerConfig** *(dict) --* Specifies mandatory fields for running an Inference Recommender job. The fields specified in "ContainerConfig" override the corresponding fields in the model package. * **Domain** *(string) --* The machine learning domain of the model and its components. Valid Values: "COMPUTER_VISION | NATURAL_LANGUAGE_PROCESSING | MACHINE_LEARNING" * **Task** *(string) --* The machine learning task that the model accomplishes. Valid Values: "IMAGE_CLASSIFICATION | OBJECT_DETECTION | TEXT_GENERATION | IMAGE_SEGMENTATION | FILL_MASK | CLASSIFICATION | REGRESSION | OTHER" * **Framework** *(string) --* The machine learning framework of the container image. Valid Values: "TENSORFLOW | PYTORCH | XGBOOST | SAGEMAKER- SCIKIT-LEARN" * **FrameworkVersion** *(string) --* The framework version of the container image. * **PayloadConfig** *(dict) --* Specifies the "SamplePayloadUrl" and all other sample payload-related fields. * **SamplePayloadUrl** *(string) --* The Amazon Simple Storage Service (Amazon S3) path where the sample payload is stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). * **SupportedContentTypes** *(list) --* The supported MIME types for the input data. * *(string) --* * **NearestModelName** *(string) --* The name of a pre-trained machine learning model benchmarked by Amazon SageMaker Inference Recommender that matches your model. Valid Values: "efficientnetb7 | unet | xgboost | faster- rcnn-resnet101 | nasnetlarge | vgg16 | inception-v3 | mask-rcnn | sagemaker-scikit-learn | densenet201-gluon | resnet18v2-gluon | xception | densenet201 | yolov4 | resnet152 | bert-base-cased | xceptionV1-keras | resnet50 | retinanet" * **SupportedInstanceTypes** *(list) --* A list of the instance types that are used to generate inferences in real-time. * *(string) --* * **SupportedEndpointType** *(string) --* The endpoint type to receive recommendations for. By default this is null, and the results of the inference recommendation job return a combined list of both real- time and serverless benchmarks. By specifying a value for this field, you can receive a longer list of benchmarks for the desired endpoint type. * **DataInputConfig** *(string) --* Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. This field is used for optimizing your model using SageMaker Neo. For more information, see DataInputConfig. * **SupportedResponseMIMETypes** *(list) --* The supported MIME types for the output data. * *(string) --* * **Endpoints** *(list) --* Existing customer endpoints on which to run an Inference Recommender job. * *(dict) --* Details about a customer endpoint that was compared in an Inference Recommender job. * **EndpointName** *(string) --* The name of a customer's endpoint. * **VpcConfig** *(dict) --* Inference Recommender provisions SageMaker endpoints with access to VPC in the inference recommendation job. * **SecurityGroupIds** *(list) --* **[REQUIRED]** The VPC security group IDs. IDs have the form of "sg- xxxxxxxx". Specify the security groups for the VPC that is specified in the "Subnets" field. * *(string) --* * **Subnets** *(list) --* **[REQUIRED]** The ID of the subnets in the VPC to which you want to connect your model. * *(string) --* * **JobDescription** (*string*) -- Description of the recommendation job. * **StoppingConditions** (*dict*) -- A set of conditions for stopping a recommendation job. If any of the conditions are met, the job is automatically stopped. * **MaxInvocations** *(integer) --* The maximum number of requests per minute expected for the endpoint. * **ModelLatencyThresholds** *(list) --* The interval of time taken by a model to respond as viewed from SageMaker. The interval includes the local communication time taken to send the request and to fetch the response from the container of a model and the time taken to complete the inference in the container. * *(dict) --* The model latency threshold. * **Percentile** *(string) --* The model latency percentile threshold. Acceptable values are "P95" and "P99". For custom load tests, specify the value as "P95". * **ValueInMilliseconds** *(integer) --* The model latency percentile value in milliseconds. * **FlatInvocations** *(string) --* Stops a load test when the number of invocations (TPS) peaks and flattens, which means that the instance has reached capacity. The default value is "Stop". If you want the load test to continue after invocations have flattened, set the value to "Continue". * **OutputConfig** (*dict*) -- Provides information about the output artifacts and the KMS key to use for Amazon S3 server-side encryption. * **KmsKeyId** *(string) --* The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt your output artifacts with Amazon S3 server-side encryption. The SageMaker execution role must have "kms:GenerateDataKey" permission. The "KmsKeyId" can be any of the following formats: * // KMS Key ID ""1234abcd-12ab-34cd-56ef-1234567890ab"" * // Amazon Resource Name (ARN) of a KMS Key ""arn:aws:kms:::key/"" * // KMS Key Alias ""alias/ExampleAlias"" * // Amazon Resource Name (ARN) of a KMS Key Alias ""arn:aws:kms:::alias/"" For more information about key identifiers, see Key identifiers (KeyID) in the Amazon Web Services Key Management Service (Amazon Web Services KMS) documentation. * **CompiledOutputConfig** *(dict) --* Provides information about the output configuration for the compiled model. * **S3OutputUri** *(string) --* Identifies the Amazon S3 bucket where you want SageMaker to store the compiled model artifacts. * **Tags** (*list*) -- The metadata that you apply to Amazon Web Services resources to help you categorize and organize them. Each tag consists of a key and a value, both of which you define. For more information, see Tagging Amazon Web Services Resources in the Amazon Web Services General Reference. * *(dict) --* A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources. You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags. For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy. * **Key** *(string) --* **[REQUIRED]** The tag key. Tag keys must be unique per resource. * **Value** *(string) --* **[REQUIRED]** The tag value. Return type: dict Returns: **Response Syntax** { 'JobArn': 'string' } **Response Structure** * *(dict) --* * **JobArn** *(string) --* The Amazon Resource Name (ARN) of the recommendation job. **Exceptions** * "SageMaker.Client.exceptions.ResourceInUse" * "SageMaker.Client.exceptions.ResourceLimitExceeded" SageMaker / Client / describe_trial describe_trial ************** SageMaker.Client.describe_trial(**kwargs) Provides a list of a trial's properties. See also: AWS API Documentation **Request Syntax** response = client.describe_trial( TrialName='string' ) Parameters: **TrialName** (*string*) -- **[REQUIRED]** The name of the trial to describe. Return type: dict Returns: **Response Syntax** { 'TrialName': 'string', 'TrialArn': 'string', 'DisplayName': 'string', 'ExperimentName': 'string', 'Source': { 'SourceArn': 'string', 'SourceType': 'string' }, 'CreationTime': datetime(2015, 1, 1), 'CreatedBy': { 'UserProfileArn': 'string', 'UserProfileName': 'string', 'DomainId': 'string', 'IamIdentity': { 'Arn': 'string', 'PrincipalId': 'string', 'SourceIdentity': 'string' } }, 'LastModifiedTime': datetime(2015, 1, 1), 'LastModifiedBy': { 'UserProfileArn': 'string', 'UserProfileName': 'string', 'DomainId': 'string', 'IamIdentity': { 'Arn': 'string', 'PrincipalId': 'string', 'SourceIdentity': 'string' } }, 'MetadataProperties': { 'CommitId': 'string', 'Repository': 'string', 'GeneratedBy': 'string', 'ProjectId': 'string' } } **Response Structure** * *(dict) --* * **TrialName** *(string) --* The name of the trial. * **TrialArn** *(string) --* The Amazon Resource Name (ARN) of the trial. * **DisplayName** *(string) --* The name of the trial as displayed. If "DisplayName" isn't specified, "TrialName" is displayed. * **ExperimentName** *(string) --* The name of the experiment the trial is part of. * **Source** *(dict) --* The Amazon Resource Name (ARN) of the source and, optionally, the job type. * **SourceArn** *(string) --* The Amazon Resource Name (ARN) of the source. * **SourceType** *(string) --* The source job type. * **CreationTime** *(datetime) --* When the trial was created. * **CreatedBy** *(dict) --* Who created the trial. * **UserProfileArn** *(string) --* The Amazon Resource Name (ARN) of the user's profile. * **UserProfileName** *(string) --* The name of the user's profile. * **DomainId** *(string) --* The domain associated with the user. * **IamIdentity** *(dict) --* The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only. * **Arn** *(string) --* The Amazon Resource Name (ARN) of the IAM identity. * **PrincipalId** *(string) --* The ID of the principal that assumes the IAM identity. * **SourceIdentity** *(string) --* The person or application which assumes the IAM identity. * **LastModifiedTime** *(datetime) --* When the trial was last modified. * **LastModifiedBy** *(dict) --* Who last modified the trial. * **UserProfileArn** *(string) --* The Amazon Resource Name (ARN) of the user's profile. * **UserProfileName** *(string) --* The name of the user's profile. * **DomainId** *(string) --* The domain associated with the user. * **IamIdentity** *(dict) --* The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only. * **Arn** *(string) --* The Amazon Resource Name (ARN) of the IAM identity. * **PrincipalId** *(string) --* The ID of the principal that assumes the IAM identity. * **SourceIdentity** *(string) --* The person or application which assumes the IAM identity. * **MetadataProperties** *(dict) --* Metadata properties of the tracking entity, trial, or trial component. * **CommitId** *(string) --* The commit ID. * **Repository** *(string) --* The repository. * **GeneratedBy** *(string) --* The entity this entity was generated by. * **ProjectId** *(string) --* The project ID. **Exceptions** * "SageMaker.Client.exceptions.ResourceNotFound" SageMaker / Client / describe_cluster_scheduler_config describe_cluster_scheduler_config ********************************* SageMaker.Client.describe_cluster_scheduler_config(**kwargs) Description of the cluster policy. This policy is used for task prioritization and fair-share allocation. This helps prioritize critical workloads and distributes idle compute across entities. See also: AWS API Documentation **Request Syntax** response = client.describe_cluster_scheduler_config( ClusterSchedulerConfigId='string', ClusterSchedulerConfigVersion=123 ) Parameters: * **ClusterSchedulerConfigId** (*string*) -- **[REQUIRED]** ID of the cluster policy. * **ClusterSchedulerConfigVersion** (*integer*) -- Version of the cluster policy. Return type: dict Returns: **Response Syntax** { 'ClusterSchedulerConfigArn': 'string', 'ClusterSchedulerConfigId': 'string', 'Name': 'string', 'ClusterSchedulerConfigVersion': 123, 'Status': 'Creating'|'CreateFailed'|'CreateRollbackFailed'|'Created'|'Updating'|'UpdateFailed'|'UpdateRollbackFailed'|'Updated'|'Deleting'|'DeleteFailed'|'DeleteRollbackFailed'|'Deleted', 'FailureReason': 'string', 'ClusterArn': 'string', 'SchedulerConfig': { 'PriorityClasses': [ { 'Name': 'string', 'Weight': 123 }, ], 'FairShare': 'Enabled'|'Disabled' }, 'Description': 'string', 'CreationTime': datetime(2015, 1, 1), 'CreatedBy': { 'UserProfileArn': 'string', 'UserProfileName': 'string', 'DomainId': 'string', 'IamIdentity': { 'Arn': 'string', 'PrincipalId': 'string', 'SourceIdentity': 'string' } }, 'LastModifiedTime': datetime(2015, 1, 1), 'LastModifiedBy': { 'UserProfileArn': 'string', 'UserProfileName': 'string', 'DomainId': 'string', 'IamIdentity': { 'Arn': 'string', 'PrincipalId': 'string', 'SourceIdentity': 'string' } } } **Response Structure** * *(dict) --* * **ClusterSchedulerConfigArn** *(string) --* ARN of the cluster policy. * **ClusterSchedulerConfigId** *(string) --* ID of the cluster policy. * **Name** *(string) --* Name of the cluster policy. * **ClusterSchedulerConfigVersion** *(integer) --* Version of the cluster policy. * **Status** *(string) --* Status of the cluster policy. * **FailureReason** *(string) --* Failure reason of the cluster policy. * **ClusterArn** *(string) --* ARN of the cluster where the cluster policy is applied. * **SchedulerConfig** *(dict) --* Cluster policy configuration. This policy is used for task prioritization and fair-share allocation. This helps prioritize critical workloads and distributes idle compute across entities. * **PriorityClasses** *(list) --* List of the priority classes, "PriorityClass", of the cluster policy. When specified, these class configurations define how tasks are queued. * *(dict) --* Priority class configuration. When included in "PriorityClasses", these class configurations define how tasks are queued. * **Name** *(string) --* Name of the priority class. * **Weight** *(integer) --* Weight of the priority class. The value is within a range from 0 to 100, where 0 is the default. A weight of 0 is the lowest priority and 100 is the highest. Weight 0 is the default. * **FairShare** *(string) --* When enabled, entities borrow idle compute based on their assigned "FairShareWeight". When disabled, entities borrow idle compute based on a first-come first-serve basis. Default is "Enabled". * **Description** *(string) --* Description of the cluster policy. * **CreationTime** *(datetime) --* Creation time of the cluster policy. * **CreatedBy** *(dict) --* Information about the user who created or modified a SageMaker resource. * **UserProfileArn** *(string) --* The Amazon Resource Name (ARN) of the user's profile. * **UserProfileName** *(string) --* The name of the user's profile. * **DomainId** *(string) --* The domain associated with the user. * **IamIdentity** *(dict) --* The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only. * **Arn** *(string) --* The Amazon Resource Name (ARN) of the IAM identity. * **PrincipalId** *(string) --* The ID of the principal that assumes the IAM identity. * **SourceIdentity** *(string) --* The person or application which assumes the IAM identity. * **LastModifiedTime** *(datetime) --* Last modified time of the cluster policy. * **LastModifiedBy** *(dict) --* Information about the user who created or modified a SageMaker resource. * **UserProfileArn** *(string) --* The Amazon Resource Name (ARN) of the user's profile. * **UserProfileName** *(string) --* The name of the user's profile. * **DomainId** *(string) --* The domain associated with the user. * **IamIdentity** *(dict) --* The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only. * **Arn** *(string) --* The Amazon Resource Name (ARN) of the IAM identity. * **PrincipalId** *(string) --* The ID of the principal that assumes the IAM identity. * **SourceIdentity** *(string) --* The person or application which assumes the IAM identity. **Exceptions** * "SageMaker.Client.exceptions.ResourceNotFound" SageMaker / Client / search search ****** SageMaker.Client.search(**kwargs) Finds SageMaker resources that match a search query. Matching resources are returned as a list of "SearchRecord" objects in the response. You can sort the search results by any resource property in a ascending or descending order. You can query against the following value types: numeric, text, Boolean, and timestamp. Note: The Search API may provide access to otherwise restricted data. See Amazon SageMaker API Permissions: Actions, Permissions, and Resources Reference for more information. See also: AWS API Documentation **Request Syntax** response = client.search( Resource='TrainingJob'|'Experiment'|'ExperimentTrial'|'ExperimentTrialComponent'|'Endpoint'|'Model'|'ModelPackage'|'ModelPackageGroup'|'Pipeline'|'PipelineExecution'|'FeatureGroup'|'FeatureMetadata'|'Image'|'ImageVersion'|'Project'|'HyperParameterTuningJob'|'ModelCard'|'PipelineVersion', SearchExpression={ 'Filters': [ { 'Name': 'string', 'Operator': 'Equals'|'NotEquals'|'GreaterThan'|'GreaterThanOrEqualTo'|'LessThan'|'LessThanOrEqualTo'|'Contains'|'Exists'|'NotExists'|'In', 'Value': 'string' }, ], 'NestedFilters': [ { 'NestedPropertyName': 'string', 'Filters': [ { 'Name': 'string', 'Operator': 'Equals'|'NotEquals'|'GreaterThan'|'GreaterThanOrEqualTo'|'LessThan'|'LessThanOrEqualTo'|'Contains'|'Exists'|'NotExists'|'In', 'Value': 'string' }, ] }, ], 'SubExpressions': [ {'... recursive ...'}, ], 'Operator': 'And'|'Or' }, SortBy='string', SortOrder='Ascending'|'Descending', NextToken='string', MaxResults=123, CrossAccountFilterOption='SameAccount'|'CrossAccount', VisibilityConditions=[ { 'Key': 'string', 'Value': 'string' }, ] ) Parameters: * **Resource** (*string*) -- **[REQUIRED]** The name of the SageMaker resource to search for. * **SearchExpression** (*dict*) -- A Boolean conditional statement. Resources must satisfy this condition to be included in search results. You must provide at least one subexpression, filter, or nested filter. The maximum number of recursive "SubExpressions", "NestedFilters", and "Filters" that can be included in a "SearchExpression" object is 50. * **Filters** *(list) --* A list of filter objects. * *(dict) --* A conditional statement for a search expression that includes a resource property, a Boolean operator, and a value. Resources that match the statement are returned in the results from the Search API. If you specify a "Value", but not an "Operator", SageMaker uses the equals operator. In search, there are several property types: Metrics To define a metric filter, enter a value using the form ""Metrics."", where "" is a metric name. For example, the following filter searches for training jobs with an ""accuracy"" metric greater than ""0.9"": "{" ""Name": "Metrics.accuracy"," ""Operator": "GreaterThan"," ""Value": "0.9"" "}" HyperParameters To define a hyperparameter filter, enter a value with the form ""HyperParameters."". Decimal hyperparameter values are treated as a decimal in a comparison if the specified "Value" is also a decimal value. If the specified "Value" is an integer, the decimal hyperparameter values are treated as integers. For example, the following filter is satisfied by training jobs with a ""learning_rate"" hyperparameter that is less than ""0.5"": "{" ""Name": "HyperParameters.learning_rate"," ""Operator": "LessThan"," ""Value": "0.5"" "}" Tags To define a tag filter, enter a value with the form "Tags.". * **Name** *(string) --* **[REQUIRED]** A resource property name. For example, "TrainingJobName". For valid property names, see SearchRecord. You must specify a valid property for the resource. * **Operator** *(string) --* A Boolean binary operator that is used to evaluate the filter. The operator field contains one of the following values: Equals The value of "Name" equals "Value". NotEquals The value of "Name" doesn't equal "Value". Exists The "Name" property exists. NotExists The "Name" property does not exist. GreaterThan The value of "Name" is greater than "Value". Not supported for text properties. GreaterThanOrEqualTo The value of "Name" is greater than or equal to "Value". Not supported for text properties. LessThan The value of "Name" is less than "Value". Not supported for text properties. LessThanOrEqualTo The value of "Name" is less than or equal to "Value". Not supported for text properties. In The value of "Name" is one of the comma delimited strings in "Value". Only supported for text properties. Contains The value of "Name" contains the string "Value". Only supported for text properties. A "SearchExpression" can include the "Contains" operator multiple times when the value of "Name" is one of the following: * "Experiment.DisplayName" * "Experiment.ExperimentName" * "Experiment.Tags" * "Trial.DisplayName" * "Trial.TrialName" * "Trial.Tags" * "TrialComponent.DisplayName" * "TrialComponent.TrialComponentName" * "TrialComponent.Tags" * "TrialComponent.InputArtifacts" * "TrialComponent.OutputArtifacts" A "SearchExpression" can include only one "Contains" operator for all other values of "Name". In these cases, if you include multiple "Contains" operators in the "SearchExpression", the result is the following error message: " "'CONTAINS' operator usage limit of 1 exceeded."" * **Value** *(string) --* A value used with "Name" and "Operator" to determine which resources satisfy the filter's condition. For numerical properties, "Value" must be an integer or floating-point decimal. For timestamp properties, "Value" must be an ISO 8601 date-time string of the following format: "YYYY-mm-dd'T'HH:MM:SS". * **NestedFilters** *(list) --* A list of nested filter objects. * *(dict) --* A list of nested Filter objects. A resource must satisfy the conditions of all filters to be included in the results returned from the Search API. For example, to filter on a training job's "InputDataConfig" property with a specific channel name and "S3Uri" prefix, define the following filters: * "'{Name:"InputDataConfig.ChannelName", "Operator":"Equals", "Value":"train"}'," * "'{Name:"InputDataConfig.DataSource.S3DataSource.S3Uri", "Operator":"Contains", "Value":"mybucket/catdata"}'" * **NestedPropertyName** *(string) --* **[REQUIRED]** The name of the property to use in the nested filters. The value must match a listed property name, such as "InputDataConfig". * **Filters** *(list) --* **[REQUIRED]** A list of filters. Each filter acts on a property. Filters must contain at least one "Filters" value. For example, a "NestedFilters" call might include a filter on the "PropertyName" parameter of the "InputDataConfig" property: "InputDataConfig.DataSource.S3DataSource.S3Uri". * *(dict) --* A conditional statement for a search expression that includes a resource property, a Boolean operator, and a value. Resources that match the statement are returned in the results from the Search API. If you specify a "Value", but not an "Operator", SageMaker uses the equals operator. In search, there are several property types: Metrics To define a metric filter, enter a value using the form ""Metrics."", where "" is a metric name. For example, the following filter searches for training jobs with an ""accuracy"" metric greater than ""0.9"": "{" ""Name": "Metrics.accuracy"," ""Operator": "GreaterThan"," ""Value": "0.9"" "}" HyperParameters To define a hyperparameter filter, enter a value with the form ""HyperParameters."". Decimal hyperparameter values are treated as a decimal in a comparison if the specified "Value" is also a decimal value. If the specified "Value" is an integer, the decimal hyperparameter values are treated as integers. For example, the following filter is satisfied by training jobs with a ""learning_rate"" hyperparameter that is less than ""0.5"": "{" ""Name": "HyperParameters.learning_rate"," ""Operator": "LessThan"," ""Value": "0.5"" "}" Tags To define a tag filter, enter a value with the form "Tags.". * **Name** *(string) --* **[REQUIRED]** A resource property name. For example, "TrainingJobName". For valid property names, see SearchRecord. You must specify a valid property for the resource. * **Operator** *(string) --* A Boolean binary operator that is used to evaluate the filter. The operator field contains one of the following values: Equals The value of "Name" equals "Value". NotEquals The value of "Name" doesn't equal "Value". Exists The "Name" property exists. NotExists The "Name" property does not exist. GreaterThan The value of "Name" is greater than "Value". Not supported for text properties. GreaterThanOrEqualTo The value of "Name" is greater than or equal to "Value". Not supported for text properties. LessThan The value of "Name" is less than "Value". Not supported for text properties. LessThanOrEqualTo The value of "Name" is less than or equal to "Value". Not supported for text properties. In The value of "Name" is one of the comma delimited strings in "Value". Only supported for text properties. Contains The value of "Name" contains the string "Value". Only supported for text properties. A "SearchExpression" can include the "Contains" operator multiple times when the value of "Name" is one of the following: * "Experiment.DisplayName" * "Experiment.ExperimentName" * "Experiment.Tags" * "Trial.DisplayName" * "Trial.TrialName" * "Trial.Tags" * "TrialComponent.DisplayName" * "TrialComponent.TrialComponentName" * "TrialComponent.Tags" * "TrialComponent.InputArtifacts" * "TrialComponent.OutputArtifacts" A "SearchExpression" can include only one "Contains" operator for all other values of "Name". In these cases, if you include multiple "Contains" operators in the "SearchExpression", the result is the following error message: " "'CONTAINS' operator usage limit of 1 exceeded."" * **Value** *(string) --* A value used with "Name" and "Operator" to determine which resources satisfy the filter's condition. For numerical properties, "Value" must be an integer or floating-point decimal. For timestamp properties, "Value" must be an ISO 8601 date-time string of the following format: "YYYY-mm-dd'T'HH:MM:SS". * **SubExpressions** *(list) --* A list of search expression objects. * *(dict) --* A multi-expression that searches for the specified resource or resources in a search. All resource objects that satisfy the expression's condition are included in the search results. You must specify at least one subexpression, filter, or nested filter. A "SearchExpression" can contain up to twenty elements. A "SearchExpression" contains the following components: * A list of "Filter" objects. Each filter defines a simple Boolean expression comprised of a resource property name, Boolean operator, and value. * A list of "NestedFilter" objects. Each nested filter defines a list of Boolean expressions using a list of resource properties. A nested filter is satisfied if a single object in the list satisfies all Boolean expressions. * A list of "SearchExpression" objects. A search expression object can be nested in a list of search expression objects. * A Boolean operator: "And" or "Or". * **Operator** *(string) --* A Boolean operator used to evaluate the search expression. If you want every conditional statement in all lists to be satisfied for the entire search expression to be true, specify "And". If only a single conditional statement needs to be true for the entire search expression to be true, specify "Or". The default value is "And". * **SortBy** (*string*) -- The name of the resource property used to sort the "SearchResults". The default is "LastModifiedTime". * **SortOrder** (*string*) -- How "SearchResults" are ordered. Valid values are "Ascending" or "Descending". The default is "Descending". * **NextToken** (*string*) -- If more than "MaxResults" resources match the specified "SearchExpression", the response includes a "NextToken". The "NextToken" can be passed to the next "SearchRequest" to continue retrieving results. * **MaxResults** (*integer*) -- The maximum number of results to return. * **CrossAccountFilterOption** (*string*) -- A cross account filter option. When the value is ""CrossAccount"" the search results will only include resources made discoverable to you from other accounts. When the value is ""SameAccount"" or "null" the search results will only include resources from your account. Default is "null". For more information on searching for resources made discoverable to your account, see Search discoverable resources in the SageMaker Developer Guide. The maximum number of >>``<". * **Value** *(string) --* The value for the tag that you're using to filter the search results. Return type: dict Returns: **Response Syntax** # This section is too large to render. # Please see the AWS API Documentation linked below. AWS API Documentation **Response Structure** # This section is too large to render. # Please see the AWS API Documentation linked below. AWS API Documentation SageMaker / Client / describe_hub_content describe_hub_content ******************** SageMaker.Client.describe_hub_content(**kwargs) Describe the content of a hub. See also: AWS API Documentation **Request Syntax** response = client.describe_hub_content( HubName='string', HubContentType='Model'|'Notebook'|'ModelReference', HubContentName='string', HubContentVersion='string' ) Parameters: * **HubName** (*string*) -- **[REQUIRED]** The name of the hub that contains the content to describe. * **HubContentType** (*string*) -- **[REQUIRED]** The type of content in the hub. * **HubContentName** (*string*) -- **[REQUIRED]** The name of the content to describe. * **HubContentVersion** (*string*) -- The version of the content to describe. Return type: dict Returns: **Response Syntax** { 'HubContentName': 'string', 'HubContentArn': 'string', 'HubContentVersion': 'string', 'HubContentType': 'Model'|'Notebook'|'ModelReference', 'DocumentSchemaVersion': 'string', 'HubName': 'string', 'HubArn': 'string', 'HubContentDisplayName': 'string', 'HubContentDescription': 'string', 'HubContentMarkdown': 'string', 'HubContentDocument': 'string', 'SageMakerPublicHubContentArn': 'string', 'ReferenceMinVersion': 'string', 'SupportStatus': 'Supported'|'Deprecated'|'Restricted', 'HubContentSearchKeywords': [ 'string', ], 'HubContentDependencies': [ { 'DependencyOriginPath': 'string', 'DependencyCopyPath': 'string' }, ], 'HubContentStatus': 'Available'|'Importing'|'Deleting'|'ImportFailed'|'DeleteFailed', 'FailureReason': 'string', 'CreationTime': datetime(2015, 1, 1), 'LastModifiedTime': datetime(2015, 1, 1) } **Response Structure** * *(dict) --* * **HubContentName** *(string) --* The name of the hub content. * **HubContentArn** *(string) --* The Amazon Resource Name (ARN) of the hub content. * **HubContentVersion** *(string) --* The version of the hub content. * **HubContentType** *(string) --* The type of hub content. * **DocumentSchemaVersion** *(string) --* The document schema version for the hub content. * **HubName** *(string) --* The name of the hub that contains the content. * **HubArn** *(string) --* The Amazon Resource Name (ARN) of the hub that contains the content. * **HubContentDisplayName** *(string) --* The display name of the hub content. * **HubContentDescription** *(string) --* A description of the hub content. * **HubContentMarkdown** *(string) --* A string that provides a description of the hub content. This string can include links, tables, and standard markdown formating. * **HubContentDocument** *(string) --* The hub content document that describes information about the hub content such as type, associated containers, scripts, and more. * **SageMakerPublicHubContentArn** *(string) --* The ARN of the public hub content. * **ReferenceMinVersion** *(string) --* The minimum version of the hub content. * **SupportStatus** *(string) --* The support status of the hub content. * **HubContentSearchKeywords** *(list) --* The searchable keywords for the hub content. * *(string) --* * **HubContentDependencies** *(list) --* The location of any dependencies that the hub content has, such as scripts, model artifacts, datasets, or notebooks. * *(dict) --* Any dependencies related to hub content, such as scripts, model artifacts, datasets, or notebooks. * **DependencyOriginPath** *(string) --* The hub content dependency origin path. * **DependencyCopyPath** *(string) --* The hub content dependency copy path. * **HubContentStatus** *(string) --* The status of the hub content. * **FailureReason** *(string) --* The failure reason if importing hub content failed. * **CreationTime** *(datetime) --* The date and time that hub content was created. * **LastModifiedTime** *(datetime) --* The last modified time of the hub content. **Exceptions** * "SageMaker.Client.exceptions.ResourceNotFound" SageMaker / Client / list_labeling_jobs list_labeling_jobs ****************** SageMaker.Client.list_labeling_jobs(**kwargs) Gets a list of labeling jobs. See also: AWS API Documentation **Request Syntax** response = client.list_labeling_jobs( CreationTimeAfter=datetime(2015, 1, 1), CreationTimeBefore=datetime(2015, 1, 1), LastModifiedTimeAfter=datetime(2015, 1, 1), LastModifiedTimeBefore=datetime(2015, 1, 1), MaxResults=123, NextToken='string', NameContains='string', SortBy='Name'|'CreationTime'|'Status', SortOrder='Ascending'|'Descending', StatusEquals='Initializing'|'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped' ) Parameters: * **CreationTimeAfter** (*datetime*) -- A filter that returns only labeling jobs created after the specified time (timestamp). * **CreationTimeBefore** (*datetime*) -- A filter that returns only labeling jobs created before the specified time (timestamp). * **LastModifiedTimeAfter** (*datetime*) -- A filter that returns only labeling jobs modified after the specified time (timestamp). * **LastModifiedTimeBefore** (*datetime*) -- A filter that returns only labeling jobs modified before the specified time (timestamp). * **MaxResults** (*integer*) -- The maximum number of labeling jobs to return in each page of the response. * **NextToken** (*string*) -- If the result of the previous "ListLabelingJobs" request was truncated, the response includes a "NextToken". To retrieve the next set of labeling jobs, use the token in the next request. * **NameContains** (*string*) -- A string in the labeling job name. This filter returns only labeling jobs whose name contains the specified string. * **SortBy** (*string*) -- The field to sort results by. The default is "CreationTime". * **SortOrder** (*string*) -- The sort order for results. The default is "Ascending". * **StatusEquals** (*string*) -- A filter that retrieves only labeling jobs with a specific status. Return type: dict Returns: **Response Syntax** { 'LabelingJobSummaryList': [ { 'LabelingJobName': 'string', 'LabelingJobArn': 'string', 'CreationTime': datetime(2015, 1, 1), 'LastModifiedTime': datetime(2015, 1, 1), 'LabelingJobStatus': 'Initializing'|'InProgress'|'Completed'|'Failed'|'Stopping'|'Stopped', 'LabelCounters': { 'TotalLabeled': 123, 'HumanLabeled': 123, 'MachineLabeled': 123, 'FailedNonRetryableError': 123, 'Unlabeled': 123 }, 'WorkteamArn': 'string', 'PreHumanTaskLambdaArn': 'string', 'AnnotationConsolidationLambdaArn': 'string', 'FailureReason': 'string', 'LabelingJobOutput': { 'OutputDatasetS3Uri': 'string', 'FinalActiveLearningModelArn': 'string' }, 'InputConfig': { 'DataSource': { 'S3DataSource': { 'ManifestS3Uri': 'string' }, 'SnsDataSource': { 'SnsTopicArn': 'string' } }, 'DataAttributes': { 'ContentClassifiers': [ 'FreeOfPersonallyIdentifiableInformation'|'FreeOfAdultContent', ] } } }, ], 'NextToken': 'string' } **Response Structure** * *(dict) --* * **LabelingJobSummaryList** *(list) --* An array of "LabelingJobSummary" objects, each describing a labeling job. * *(dict) --* Provides summary information about a labeling job. * **LabelingJobName** *(string) --* The name of the labeling job. * **LabelingJobArn** *(string) --* The Amazon Resource Name (ARN) assigned to the labeling job when it was created. * **CreationTime** *(datetime) --* The date and time that the job was created (timestamp). * **LastModifiedTime** *(datetime) --* The date and time that the job was last modified (timestamp). * **LabelingJobStatus** *(string) --* The current status of the labeling job. * **LabelCounters** *(dict) --* Counts showing the progress of the labeling job. * **TotalLabeled** *(integer) --* The total number of objects labeled. * **HumanLabeled** *(integer) --* The total number of objects labeled by a human worker. * **MachineLabeled** *(integer) --* The total number of objects labeled by automated data labeling. * **FailedNonRetryableError** *(integer) --* The total number of objects that could not be labeled due to an error. * **Unlabeled** *(integer) --* The total number of objects not yet labeled. * **WorkteamArn** *(string) --* The Amazon Resource Name (ARN) of the work team assigned to the job. * **PreHumanTaskLambdaArn** *(string) --* The Amazon Resource Name (ARN) of a Lambda function. The function is run before each data object is sent to a worker. * **AnnotationConsolidationLambdaArn** *(string) --* The Amazon Resource Name (ARN) of the Lambda function used to consolidate the annotations from individual workers into a label for a data object. For more information, see Annotation Consolidation. * **FailureReason** *(string) --* If the "LabelingJobStatus" field is "Failed", this field contains a description of the error. * **LabelingJobOutput** *(dict) --* The location of the output produced by the labeling job. * **OutputDatasetS3Uri** *(string) --* The Amazon S3 bucket location of the manifest file for labeled data. * **FinalActiveLearningModelArn** *(string) --* The Amazon Resource Name (ARN) for the most recent SageMaker model trained as part of automated data labeling. * **InputConfig** *(dict) --* Input configuration for the labeling job. * **DataSource** *(dict) --* The location of the input data. * **S3DataSource** *(dict) --* The Amazon S3 location of the input data objects. * **ManifestS3Uri** *(string) --* The Amazon S3 location of the manifest file that describes the input data objects. The input manifest file referenced in "ManifestS3Uri" must contain one of the following keys: "source-ref" or "source". The value of the keys are interpreted as follows: * "source-ref": The source of the object is the Amazon S3 object specified in the value. Use this value when the object is a binary object, such as an image. * "source": The source of the object is the value. Use this value when the object is a text value. If you are a new user of Ground Truth, it is recommended you review Use an Input Manifest File in the Amazon SageMaker Developer Guide to learn how to create an input manifest file. * **SnsDataSource** *(dict) --* An Amazon SNS data source used for streaming labeling jobs. To learn more, see Send Data to a Streaming Labeling Job. * **SnsTopicArn** *(string) --* The Amazon SNS input topic Amazon Resource Name (ARN). Specify the ARN of the input topic you will use to send new data objects to a streaming labeling job. * **DataAttributes** *(dict) --* Attributes of the data specified by the customer. * **ContentClassifiers** *(list) --* Declares that your content is free of personally identifiable information or adult content. SageMaker may restrict the Amazon Mechanical Turk workers that can view your task based on this information. * *(string) --* * **NextToken** *(string) --* If the response is truncated, SageMaker returns this token. To retrieve the next set of labeling jobs, use it in the subsequent request. SageMaker / Client / delete_workforce delete_workforce **************** SageMaker.Client.delete_workforce(**kwargs) Use this operation to delete a workforce. If you want to create a new workforce in an Amazon Web Services Region where a workforce already exists, use this operation to delete the existing workforce and then use CreateWorkforce to create a new workforce. Warning: If a private workforce contains one or more work teams, you must use the DeleteWorkteam operation to delete all work teams before you delete the workforce. If you try to delete a workforce that contains one or more work teams, you will receive a "ResourceInUse" error. See also: AWS API Documentation **Request Syntax** response = client.delete_workforce( WorkforceName='string' ) Parameters: **WorkforceName** (*string*) -- **[REQUIRED]** The name of the workforce. Return type: dict Returns: **Response Syntax** {} **Response Structure** * *(dict) --* SageMaker / Client / list_candidates_for_auto_ml_job list_candidates_for_auto_ml_job ******************************* SageMaker.Client.list_candidates_for_auto_ml_job(**kwargs) List the candidates created for the job. See also: AWS API Documentation **Request Syntax** response = client.list_candidates_for_auto_ml_job( AutoMLJobName='string', StatusEquals='Completed'|'InProgress'|'Failed'|'Stopped'|'Stopping', CandidateNameEquals='string', SortOrder='Ascending'|'Descending', SortBy='CreationTime'|'Status'|'FinalObjectiveMetricValue', MaxResults=123, NextToken='string' ) Parameters: * **AutoMLJobName** (*string*) -- **[REQUIRED]** List the candidates created for the job by providing the job's name. * **StatusEquals** (*string*) -- List the candidates for the job and filter by status. * **CandidateNameEquals** (*string*) -- List the candidates for the job and filter by candidate name. * **SortOrder** (*string*) -- The sort order for the results. The default is "Ascending". * **SortBy** (*string*) -- The parameter by which to sort the results. The default is "Descending". * **MaxResults** (*integer*) -- List the job's candidates up to a specified limit. * **NextToken** (*string*) -- If the previous response was truncated, you receive this token. Use it in your next request to receive the next set of results. Return type: dict Returns: **Response Syntax** { 'Candidates': [ { 'CandidateName': 'string', 'FinalAutoMLJobObjectiveMetric': { 'Type': 'Maximize'|'Minimize', 'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'BalancedAccuracy'|'R2'|'Recall'|'RecallMacro'|'Precision'|'PrecisionMacro'|'MAE'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss', 'Value': ..., 'StandardMetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'BalancedAccuracy'|'R2'|'Recall'|'RecallMacro'|'Precision'|'PrecisionMacro'|'MAE'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss' }, 'ObjectiveStatus': 'Succeeded'|'Pending'|'Failed', 'CandidateSteps': [ { 'CandidateStepType': 'AWS::SageMaker::TrainingJob'|'AWS::SageMaker::TransformJob'|'AWS::SageMaker::ProcessingJob', 'CandidateStepArn': 'string', 'CandidateStepName': 'string' }, ], 'CandidateStatus': 'Completed'|'InProgress'|'Failed'|'Stopped'|'Stopping', 'InferenceContainers': [ { 'Image': 'string', 'ModelDataUrl': 'string', 'Environment': { 'string': 'string' } }, ], 'CreationTime': datetime(2015, 1, 1), 'EndTime': datetime(2015, 1, 1), 'LastModifiedTime': datetime(2015, 1, 1), 'FailureReason': 'string', 'CandidateProperties': { 'CandidateArtifactLocations': { 'Explainability': 'string', 'ModelInsights': 'string', 'BacktestResults': 'string' }, 'CandidateMetrics': [ { 'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'BalancedAccuracy'|'R2'|'Recall'|'RecallMacro'|'Precision'|'PrecisionMacro'|'MAE'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss', 'StandardMetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'MAE'|'R2'|'BalancedAccuracy'|'Precision'|'PrecisionMacro'|'Recall'|'RecallMacro'|'LogLoss'|'InferenceLatency'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss'|'Rouge1'|'Rouge2'|'RougeL'|'RougeLSum'|'Perplexity'|'ValidationLoss'|'TrainingLoss', 'Value': ..., 'Set': 'Train'|'Validation'|'Test' }, ] }, 'InferenceContainerDefinitions': { 'string': [ { 'Image': 'string', 'ModelDataUrl': 'string', 'Environment': { 'string': 'string' } }, ] } }, ], 'NextToken': 'string' } **Response Structure** * *(dict) --* * **Candidates** *(list) --* Summaries about the "AutoMLCandidates". * *(dict) --* Information about a candidate produced by an AutoML training job, including its status, steps, and other properties. * **CandidateName** *(string) --* The name of the candidate. * **FinalAutoMLJobObjectiveMetric** *(dict) --* The best candidate result from an AutoML training job. * **Type** *(string) --* The type of metric with the best result. * **MetricName** *(string) --* The name of the metric with the best result. For a description of the possible objective metrics, see AutoMLJobObjective$MetricName. * **Value** *(float) --* The value of the metric with the best result. * **StandardMetricName** *(string) --* The name of the standard metric. For a description of the standard metrics, see Autopilot candidate metrics. * **ObjectiveStatus** *(string) --* The objective's status. * **CandidateSteps** *(list) --* Information about the candidate's steps. * *(dict) --* Information about the steps for a candidate and what step it is working on. * **CandidateStepType** *(string) --* Whether the candidate is at the transform, training, or processing step. * **CandidateStepArn** *(string) --* The ARN for the candidate's step. * **CandidateStepName** *(string) --* The name for the candidate's step. * **CandidateStatus** *(string) --* The candidate's status. * **InferenceContainers** *(list) --* Information about the recommended inference container definitions. * *(dict) --* A list of container definitions that describe the different containers that make up an AutoML candidate. For more information, see ContainerDefinition. * **Image** *(string) --* The Amazon Elastic Container Registry (Amazon ECR) path of the container. For more information, see ContainerDefinition. * **ModelDataUrl** *(string) --* The location of the model artifacts. For more information, see ContainerDefinition. * **Environment** *(dict) --* The environment variables to set in the container. For more information, see ContainerDefinition. * *(string) --* * *(string) --* * **CreationTime** *(datetime) --* The creation time. * **EndTime** *(datetime) --* The end time. * **LastModifiedTime** *(datetime) --* The last modified time. * **FailureReason** *(string) --* The failure reason. * **CandidateProperties** *(dict) --* The properties of an AutoML candidate job. * **CandidateArtifactLocations** *(dict) --* The Amazon S3 prefix to the artifacts generated for an AutoML candidate. * **Explainability** *(string) --* The Amazon S3 prefix to the explainability artifacts generated for the AutoML candidate. * **ModelInsights** *(string) --* The Amazon S3 prefix to the model insight artifacts generated for the AutoML candidate. * **BacktestResults** *(string) --* The Amazon S3 prefix to the accuracy metrics and the inference results observed over the testing window. Available only for the time-series forecasting problem type. * **CandidateMetrics** *(list) --* Information about the candidate metrics for an AutoML job. * *(dict) --* Information about the metric for a candidate produced by an AutoML job. * **MetricName** *(string) --* The name of the metric. * **StandardMetricName** *(string) --* The name of the standard metric. Note: For definitions of the standard metrics, see Autopilot candidate metrics. * **Value** *(float) --* The value of the metric. * **Set** *(string) --* The dataset split from which the AutoML job produced the metric. * **InferenceContainerDefinitions** *(dict) --* The mapping of all supported processing unit (CPU, GPU, etc...) to inference container definitions for the candidate. This field is populated for the AutoML jobs V2 (for example, for jobs created by calling "CreateAutoMLJobV2") related to image or text classification problem types only. * *(string) --* Processing unit for an inference container. Currently Autopilot only supports "CPU" or "GPU". * *(list) --* Information about the recommended inference container definitions. * *(dict) --* A list of container definitions that describe the different containers that make up an AutoML candidate. For more information, see ContainerDefinition. * **Image** *(string) --* The Amazon Elastic Container Registry (Amazon ECR) path of the container. For more information, see ContainerDefinition. * **ModelDataUrl** *(string) --* The location of the model artifacts. For more information, see ContainerDefinition. * **Environment** *(dict) --* The environment variables to set in the container. For more information, see ContainerDefinition. * *(string) --* * *(string) --* * **NextToken** *(string) --* If the previous response was truncated, you receive this token. Use it in your next request to receive the next set of results. **Exceptions** * "SageMaker.Client.exceptions.ResourceNotFound" SageMaker / Client / describe_partner_app describe_partner_app ******************** SageMaker.Client.describe_partner_app(**kwargs) Gets information about a SageMaker Partner AI App. See also: AWS API Documentation **Request Syntax** response = client.describe_partner_app( Arn='string' ) Parameters: **Arn** (*string*) -- **[REQUIRED]** The ARN of the SageMaker Partner AI App to describe. Return type: dict Returns: **Response Syntax** { 'Arn': 'string', 'Name': 'string', 'Type': 'lakera-guard'|'comet'|'deepchecks-llm-evaluation'|'fiddler', 'Status': 'Creating'|'Updating'|'Deleting'|'Available'|'Failed'|'UpdateFailed'|'Deleted', 'CreationTime': datetime(2015, 1, 1), 'LastModifiedTime': datetime(2015, 1, 1), 'ExecutionRoleArn': 'string', 'KmsKeyId': 'string', 'BaseUrl': 'string', 'MaintenanceConfig': { 'MaintenanceWindowStart': 'string' }, 'Tier': 'string', 'Version': 'string', 'ApplicationConfig': { 'AdminUsers': [ 'string', ], 'Arguments': { 'string': 'string' } }, 'AuthType': 'IAM', 'EnableIamSessionBasedIdentity': True|False, 'Error': { 'Code': 'string', 'Reason': 'string' } } **Response Structure** * *(dict) --* * **Arn** *(string) --* The ARN of the SageMaker Partner AI App that was described. * **Name** *(string) --* The name of the SageMaker Partner AI App. * **Type** *(string) --* The type of SageMaker Partner AI App. Must be one of the following: "lakera-guard", "comet", "deepchecks-llm- evaluation", or "fiddler". * **Status** *(string) --* The status of the SageMaker Partner AI App. * **CreationTime** *(datetime) --* The time that the SageMaker Partner AI App was created. * **LastModifiedTime** *(datetime) --* The time that the SageMaker Partner AI App was last modified. * **ExecutionRoleArn** *(string) --* The ARN of the IAM role associated with the SageMaker Partner AI App. * **KmsKeyId** *(string) --* The Amazon Web Services KMS customer managed key used to encrypt the data at rest associated with SageMaker Partner AI Apps. * **BaseUrl** *(string) --* The URL of the SageMaker Partner AI App that the Application SDK uses to support in-app calls for the user. * **MaintenanceConfig** *(dict) --* Maintenance configuration settings for the SageMaker Partner AI App. * **MaintenanceWindowStart** *(string) --* The day and time of the week in Coordinated Universal Time (UTC) 24-hour standard time that weekly maintenance updates are scheduled. This value must take the following format: "3-letter-day:24-h-hour:minute". For example: "TUE:03:30". * **Tier** *(string) --* The instance type and size of the cluster attached to the SageMaker Partner AI App. * **Version** *(string) --* The version of the SageMaker Partner AI App. * **ApplicationConfig** *(dict) --* Configuration settings for the SageMaker Partner AI App. * **AdminUsers** *(list) --* The list of users that are given admin access to the SageMaker Partner AI App. * *(string) --* * **Arguments** *(dict) --* This is a map of required inputs for a SageMaker Partner AI App. Based on the application type, the map is populated with a key and value pair that is specific to the user and application. * *(string) --* * *(string) --* * **AuthType** *(string) --* The authorization type that users use to access the SageMaker Partner AI App. * **EnableIamSessionBasedIdentity** *(boolean) --* When set to "TRUE", the SageMaker Partner AI App sets the Amazon Web Services IAM session name or the authenticated IAM user as the identity of the SageMaker Partner AI App user. * **Error** *(dict) --* This is an error field object that contains the error code and the reason for an operation failure. * **Code** *(string) --* The error code for an invalid or failed operation. * **Reason** *(string) --* The failure reason for the operation. **Exceptions** * "SageMaker.Client.exceptions.ResourceNotFound" SageMaker / Client / update_experiment update_experiment ***************** SageMaker.Client.update_experiment(**kwargs) Adds, updates, or removes the description of an experiment. Updates the display name of an experiment. See also: AWS API Documentation **Request Syntax** response = client.update_experiment( ExperimentName='string', DisplayName='string', Description='string' ) Parameters: * **ExperimentName** (*string*) -- **[REQUIRED]** The name of the experiment to update. * **DisplayName** (*string*) -- The name of the experiment as displayed. The name doesn't need to be unique. If "DisplayName" isn't specified, "ExperimentName" is displayed. * **Description** (*string*) -- The description of the experiment. Return type: dict Returns: **Response Syntax** { 'ExperimentArn': 'string' } **Response Structure** * *(dict) --* * **ExperimentArn** *(string) --* The Amazon Resource Name (ARN) of the experiment. **Exceptions** * "SageMaker.Client.exceptions.ConflictException" * "SageMaker.Client.exceptions.ResourceNotFound" SageMaker / Client / update_cluster_scheduler_config update_cluster_scheduler_config ******************************* SageMaker.Client.update_cluster_scheduler_config(**kwargs) Update the cluster policy configuration. See also: AWS API Documentation **Request Syntax** response = client.update_cluster_scheduler_config( ClusterSchedulerConfigId='string', TargetVersion=123, SchedulerConfig={ 'PriorityClasses': [ { 'Name': 'string', 'Weight': 123 }, ], 'FairShare': 'Enabled'|'Disabled' }, Description='string' ) Parameters: * **ClusterSchedulerConfigId** (*string*) -- **[REQUIRED]** ID of the cluster policy. * **TargetVersion** (*integer*) -- **[REQUIRED]** Target version. * **SchedulerConfig** (*dict*) -- Cluster policy configuration. * **PriorityClasses** *(list) --* List of the priority classes, "PriorityClass", of the cluster policy. When specified, these class configurations define how tasks are queued. * *(dict) --* Priority class configuration. When included in "PriorityClasses", these class configurations define how tasks are queued. * **Name** *(string) --* **[REQUIRED]** Name of the priority class. * **Weight** *(integer) --* **[REQUIRED]** Weight of the priority class. The value is within a range from 0 to 100, where 0 is the default. A weight of 0 is the lowest priority and 100 is the highest. Weight 0 is the default. * **FairShare** *(string) --* When enabled, entities borrow idle compute based on their assigned "FairShareWeight". When disabled, entities borrow idle compute based on a first-come first-serve basis. Default is "Enabled". * **Description** (*string*) -- Description of the cluster policy. Return type: dict Returns: **Response Syntax** { 'ClusterSchedulerConfigArn': 'string', 'ClusterSchedulerConfigVersion': 123 } **Response Structure** * *(dict) --* * **ClusterSchedulerConfigArn** *(string) --* ARN of the cluster policy. * **ClusterSchedulerConfigVersion** *(integer) --* Version of the cluster policy. **Exceptions** * "SageMaker.Client.exceptions.ConflictException" * "SageMaker.Client.exceptions.ResourceNotFound" * "SageMaker.Client.exceptions.ResourceLimitExceeded" SageMaker / Client / enable_sagemaker_servicecatalog_portfolio enable_sagemaker_servicecatalog_portfolio ***************************************** SageMaker.Client.enable_sagemaker_servicecatalog_portfolio() Enables using Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects. See also: AWS API Documentation **Request Syntax** response = client.enable_sagemaker_servicecatalog_portfolio() Return type: dict Returns: **Response Syntax** {} **Response Structure** * *(dict) --* SageMaker / Client / create_studio_lifecycle_config create_studio_lifecycle_config ****************************** SageMaker.Client.create_studio_lifecycle_config(**kwargs) Creates a new Amazon SageMaker AI Studio Lifecycle Configuration. See also: AWS API Documentation **Request Syntax** response = client.create_studio_lifecycle_config( StudioLifecycleConfigName='string', StudioLifecycleConfigContent='string', StudioLifecycleConfigAppType='JupyterServer'|'KernelGateway'|'CodeEditor'|'JupyterLab', Tags=[ { 'Key': 'string', 'Value': 'string' }, ] ) Parameters: * **StudioLifecycleConfigName** (*string*) -- **[REQUIRED]** The name of the Amazon SageMaker AI Studio Lifecycle Configuration to create. * **StudioLifecycleConfigContent** (*string*) -- **[REQUIRED]** The content of your Amazon SageMaker AI Studio Lifecycle Configuration script. This content must be base64 encoded. * **StudioLifecycleConfigAppType** (*string*) -- **[REQUIRED]** The App type that the Lifecycle Configuration is attached to. * **Tags** (*list*) -- Tags to be associated with the Lifecycle Configuration. Each tag consists of a key and an optional value. Tag keys must be unique per resource. Tags are searchable using the Search API. * *(dict) --* A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources. You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags. For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy. * **Key** *(string) --* **[REQUIRED]** The tag key. Tag keys must be unique per resource. * **Value** *(string) --* **[REQUIRED]** The tag value. Return type: dict Returns: **Response Syntax** { 'StudioLifecycleConfigArn': 'string' } **Response Structure** * *(dict) --* * **StudioLifecycleConfigArn** *(string) --* The ARN of your created Lifecycle Configuration. **Exceptions** * "SageMaker.Client.exceptions.ResourceInUse" SageMaker / Client / delete_artifact delete_artifact *************** SageMaker.Client.delete_artifact(**kwargs) Deletes an artifact. Either "ArtifactArn" or "Source" must be specified. See also: AWS API Documentation **Request Syntax** response = client.delete_artifact( ArtifactArn='string', Source={ 'SourceUri': 'string', 'SourceTypes': [ { 'SourceIdType': 'MD5Hash'|'S3ETag'|'S3Version'|'Custom', 'Value': 'string' }, ] } ) Parameters: * **ArtifactArn** (*string*) -- The Amazon Resource Name (ARN) of the artifact to delete. * **Source** (*dict*) -- The URI of the source. * **SourceUri** *(string) --* **[REQUIRED]** The URI of the source. * **SourceTypes** *(list) --* A list of source types. * *(dict) --* The ID and ID type of an artifact source. * **SourceIdType** *(string) --* **[REQUIRED]** The type of ID. * **Value** *(string) --* **[REQUIRED]** The ID. Return type: dict Returns: **Response Syntax** { 'ArtifactArn': 'string' } **Response Structure** * *(dict) --* * **ArtifactArn** *(string) --* The Amazon Resource Name (ARN) of the artifact. **Exceptions** * "SageMaker.Client.exceptions.ResourceNotFound" SageMaker / Client / attach_cluster_node_volume attach_cluster_node_volume ************************** SageMaker.Client.attach_cluster_node_volume(**kwargs) Attaches your Amazon Elastic Block Store (Amazon EBS) volume to a node in your EKS orchestrated HyperPod cluster. This API works with the Amazon Elastic Block Store (Amazon EBS) Container Storage Interface (CSI) driver to manage the lifecycle of persistent storage in your HyperPod EKS clusters. See also: AWS API Documentation **Request Syntax** response = client.attach_cluster_node_volume( ClusterArn='string', NodeId='string', VolumeId='string' ) Parameters: * **ClusterArn** (*string*) -- **[REQUIRED]** The Amazon Resource Name (ARN) of your SageMaker HyperPod cluster containing the target node. Your cluster must use EKS as the orchestration and be in the "InService" state. * **NodeId** (*string*) -- **[REQUIRED]** The unique identifier of the cluster node to which you want to attach the volume. The node must belong to your specified HyperPod cluster and cannot be part of a Restricted Instance Group (RIG). * **VolumeId** (*string*) -- **[REQUIRED]** The unique identifier of your EBS volume to attach. The volume must be in the "available" state. Return type: dict Returns: **Response Syntax** { 'ClusterArn': 'string', 'NodeId': 'string', 'VolumeId': 'string', 'AttachTime': datetime(2015, 1, 1), 'Status': 'attaching'|'attached'|'detaching'|'detached'|'busy', 'DeviceName': 'string' } **Response Structure** * *(dict) --* * **ClusterArn** *(string) --* The Amazon Resource Name (ARN) of your SageMaker HyperPod cluster where the volume attachment operation was performed. * **NodeId** *(string) --* The unique identifier of the cluster node where your volume was attached. * **VolumeId** *(string) --* The unique identifier of your EBS volume that was attached. * **AttachTime** *(datetime) --* The timestamp when the volume attachment operation was initiated by the SageMaker HyperPod service. * **Status** *(string) --* The current status of your volume attachment operation. * **DeviceName** *(string) --* The device name assigned to your attached volume on the target instance. **Exceptions** * "SageMaker.Client.exceptions.ResourceNotFound" SageMaker / Client / list_monitoring_alerts list_monitoring_alerts ********************** SageMaker.Client.list_monitoring_alerts(**kwargs) Gets the alerts for a single monitoring schedule. See also: AWS API Documentation **Request Syntax** response = client.list_monitoring_alerts( MonitoringScheduleName='string', NextToken='string', MaxResults=123 ) Parameters: * **MonitoringScheduleName** (*string*) -- **[REQUIRED]** The name of a monitoring schedule. * **NextToken** (*string*) -- If the result of the previous "ListMonitoringAlerts" request was truncated, the response includes a "NextToken". To retrieve the next set of alerts in the history, use the token in the next request. * **MaxResults** (*integer*) -- The maximum number of results to display. The default is 100. Return type: dict Returns: **Response Syntax** { 'MonitoringAlertSummaries': [ { 'MonitoringAlertName': 'string', 'CreationTime': datetime(2015, 1, 1), 'LastModifiedTime': datetime(2015, 1, 1), 'AlertStatus': 'InAlert'|'OK', 'DatapointsToAlert': 123, 'EvaluationPeriod': 123, 'Actions': { 'ModelDashboardIndicator': { 'Enabled': True|False } } }, ], 'NextToken': 'string' } **Response Structure** * *(dict) --* * **MonitoringAlertSummaries** *(list) --* A JSON array where each element is a summary for a monitoring alert. * *(dict) --* Provides summary information about a monitor alert. * **MonitoringAlertName** *(string) --* The name of a monitoring alert. * **CreationTime** *(datetime) --* A timestamp that indicates when a monitor alert was created. * **LastModifiedTime** *(datetime) --* A timestamp that indicates when a monitor alert was last updated. * **AlertStatus** *(string) --* The current status of an alert. * **DatapointsToAlert** *(integer) --* Within "EvaluationPeriod", how many execution failures will raise an alert. * **EvaluationPeriod** *(integer) --* The number of most recent monitoring executions to consider when evaluating alert status. * **Actions** *(dict) --* A list of alert actions taken in response to an alert going into "InAlert" status. * **ModelDashboardIndicator** *(dict) --* An alert action taken to light up an icon on the Model Dashboard when an alert goes into "InAlert" status. * **Enabled** *(boolean) --* Indicates whether the alert action is turned on. * **NextToken** *(string) --* If the response is truncated, SageMaker returns this token. To retrieve the next set of alerts, use it in the subsequent request. **Exceptions** * "SageMaker.Client.exceptions.ResourceNotFound" SageMaker / Client / create_workteam create_workteam *************** SageMaker.Client.create_workteam(**kwargs) Creates a new work team for labeling your data. A work team is defined by one or more Amazon Cognito user pools. You must first create the user pools before you can create a work team. You cannot create more than 25 work teams in an account and region. See also: AWS API Documentation **Request Syntax** response = client.create_workteam( WorkteamName='string', WorkforceName='string', MemberDefinitions=[ { 'CognitoMemberDefinition': { 'UserPool': 'string', 'UserGroup': 'string', 'ClientId': 'string' }, 'OidcMemberDefinition': { 'Groups': [ 'string', ] } }, ], Description='string', NotificationConfiguration={ 'NotificationTopicArn': 'string' }, WorkerAccessConfiguration={ 'S3Presign': { 'IamPolicyConstraints': { 'SourceIp': 'Enabled'|'Disabled', 'VpcSourceIp': 'Enabled'|'Disabled' } } }, Tags=[ { 'Key': 'string', 'Value': 'string' }, ] ) Parameters: * **WorkteamName** (*string*) -- **[REQUIRED]** The name of the work team. Use this name to identify the work team. * **WorkforceName** (*string*) -- The name of the workforce. * **MemberDefinitions** (*list*) -- **[REQUIRED]** A list of "MemberDefinition" objects that contains objects that identify the workers that make up the work team. Workforces can be created using Amazon Cognito or your own OIDC Identity Provider (IdP). For private workforces created using Amazon Cognito use "CognitoMemberDefinition". For workforces created using your own OIDC identity provider (IdP) use "OidcMemberDefinition". Do not provide input for both of these parameters in a single request. For workforces created using Amazon Cognito, private work teams correspond to Amazon Cognito *user groups* within the user pool used to create a workforce. All of the "CognitoMemberDefinition" objects that make up the member definition must have the same "ClientId" and "UserPool" values. To add a Amazon Cognito user group to an existing worker pool, see Adding groups to a User Pool. For more information about user pools, see `Amazon Cognito User Pools. For workforces created using your own OIDC IdP, specify the user groups that you want to include in your private work team in "OidcMemberDefinition" by listing those groups in "Groups". * *(dict) --* Defines an Amazon Cognito or your own OIDC IdP user group that is part of a work team. * **CognitoMemberDefinition** *(dict) --* The Amazon Cognito user group that is part of the work team. * **UserPool** *(string) --* **[REQUIRED]** An identifier for a user pool. The user pool must be in the same region as the service that you are calling. * **UserGroup** *(string) --* **[REQUIRED]** An identifier for a user group. * **ClientId** *(string) --* **[REQUIRED]** An identifier for an application client. You must create the app client ID using Amazon Cognito. * **OidcMemberDefinition** *(dict) --* A list user groups that exist in your OIDC Identity Provider (IdP). One to ten groups can be used to create a single private work team. When you add a user group to the list of "Groups", you can add that user group to one or more private work teams. If you add a user group to a private work team, all workers in that user group are added to the work team. * **Groups** *(list) --* A list of comma seperated strings that identifies user groups in your OIDC IdP. Each user group is made up of a group of private workers. * *(string) --* * **Description** (*string*) -- **[REQUIRED]** A description of the work team. * **NotificationConfiguration** (*dict*) -- Configures notification of workers regarding available or expiring work items. * **NotificationTopicArn** *(string) --* The ARN for the Amazon SNS topic to which notifications should be published. * **WorkerAccessConfiguration** (*dict*) -- Use this optional parameter to constrain access to an Amazon S3 resource based on the IP address using supported IAM global condition keys. The Amazon S3 resource is accessed in the worker portal using a Amazon S3 presigned URL. * **S3Presign** *(dict) --* Defines any Amazon S3 resource constraints. * **IamPolicyConstraints** *(dict) --* Use this parameter to specify the allowed request source. Possible sources are either "SourceIp" or "VpcSourceIp". * **SourceIp** *(string) --* When "SourceIp" is "Enabled" the worker's IP address when a task is rendered in the worker portal is added to the IAM policy as a "Condition" used to generate the Amazon S3 presigned URL. This IP address is checked by Amazon S3 and must match in order for the Amazon S3 resource to be rendered in the worker portal. * **VpcSourceIp** *(string) --* When "VpcSourceIp" is "Enabled" the worker's IP address when a task is rendered in private worker portal inside the VPC is added to the IAM policy as a "Condition" used to generate the Amazon S3 presigned URL. To render the task successfully Amazon S3 checks that the presigned URL is being accessed over an Amazon S3 VPC Endpoint, and that the worker's IP address matches the IP address in the IAM policy. To learn more about configuring private worker portal, see Use Amazon VPC mode from a private worker portal. * **Tags** (*list*) -- An array of key-value pairs. For more information, see Resource Tag and Using Cost Allocation Tags in the *Amazon Web Services Billing and Cost Management User Guide*. * *(dict) --* A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources. You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags. For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy. * **Key** *(string) --* **[REQUIRED]** The tag key. Tag keys must be unique per resource. * **Value** *(string) --* **[REQUIRED]** The tag value. Return type: dict Returns: **Response Syntax** { 'WorkteamArn': 'string' } **Response Structure** * *(dict) --* * **WorkteamArn** *(string) --* The Amazon Resource Name (ARN) of the work team. You can use this ARN to identify the work team. **Exceptions** * "SageMaker.Client.exceptions.ResourceInUse" * "SageMaker.Client.exceptions.ResourceLimitExceeded" SageMaker / Client / create_workforce create_workforce **************** SageMaker.Client.create_workforce(**kwargs) Use this operation to create a workforce. This operation will return an error if a workforce already exists in the Amazon Web Services Region that you specify. You can only create one workforce in each Amazon Web Services Region per Amazon Web Services account. If you want to create a new workforce in an Amazon Web Services Region where a workforce already exists, use the DeleteWorkforce API operation to delete the existing workforce and then use "CreateWorkforce" to create a new workforce. To create a private workforce using Amazon Cognito, you must specify a Cognito user pool in "CognitoConfig". You can also create an Amazon Cognito workforce using the Amazon SageMaker console. For more information, see Create a Private Workforce (Amazon Cognito). To create a private workforce using your own OIDC Identity Provider (IdP), specify your IdP configuration in "OidcConfig". Your OIDC IdP must support *groups* because groups are used by Ground Truth and Amazon A2I to create work teams. For more information, see Create a Private Workforce (OIDC IdP). See also: AWS API Documentation **Request Syntax** response = client.create_workforce( CognitoConfig={ 'UserPool': 'string', 'ClientId': 'string' }, OidcConfig={ 'ClientId': 'string', 'ClientSecret': 'string', 'Issuer': 'string', 'AuthorizationEndpoint': 'string', 'TokenEndpoint': 'string', 'UserInfoEndpoint': 'string', 'LogoutEndpoint': 'string', 'JwksUri': 'string', 'Scope': 'string', 'AuthenticationRequestExtraParams': { 'string': 'string' } }, SourceIpConfig={ 'Cidrs': [ 'string', ] }, WorkforceName='string', Tags=[ { 'Key': 'string', 'Value': 'string' }, ], WorkforceVpcConfig={ 'VpcId': 'string', 'SecurityGroupIds': [ 'string', ], 'Subnets': [ 'string', ] }, IpAddressType='ipv4'|'dualstack' ) Parameters: * **CognitoConfig** (*dict*) -- Use this parameter to configure an Amazon Cognito private workforce. A single Cognito workforce is created using and corresponds to a single Amazon Cognito user pool. Do not use "OidcConfig" if you specify values for "CognitoConfig". * **UserPool** *(string) --* **[REQUIRED]** A user pool is a user directory in Amazon Cognito. With a user pool, your users can sign in to your web or mobile app through Amazon Cognito. Your users can also sign in through social identity providers like Google, Facebook, Amazon, or Apple, and through SAML identity providers. * **ClientId** *(string) --* **[REQUIRED]** The client ID for your Amazon Cognito user pool. * **OidcConfig** (*dict*) -- Use this parameter to configure a private workforce using your own OIDC Identity Provider. Do not use "CognitoConfig" if you specify values for "OidcConfig". * **ClientId** *(string) --* **[REQUIRED]** The OIDC IdP client ID used to configure your private workforce. * **ClientSecret** *(string) --* **[REQUIRED]** The OIDC IdP client secret used to configure your private workforce. * **Issuer** *(string) --* **[REQUIRED]** The OIDC IdP issuer used to configure your private workforce. * **AuthorizationEndpoint** *(string) --* **[REQUIRED]** The OIDC IdP authorization endpoint used to configure your private workforce. * **TokenEndpoint** *(string) --* **[REQUIRED]** The OIDC IdP token endpoint used to configure your private workforce. * **UserInfoEndpoint** *(string) --* **[REQUIRED]** The OIDC IdP user information endpoint used to configure your private workforce. * **LogoutEndpoint** *(string) --* **[REQUIRED]** The OIDC IdP logout endpoint used to configure your private workforce. * **JwksUri** *(string) --* **[REQUIRED]** The OIDC IdP JSON Web Key Set (Jwks) URI used to configure your private workforce. * **Scope** *(string) --* An array of string identifiers used to refer to the specific pieces of user data or claims that the client application wants to access. * **AuthenticationRequestExtraParams** *(dict) --* A string to string map of identifiers specific to the custom identity provider (IdP) being used. * *(string) --* * *(string) --* * **SourceIpConfig** (*dict*) -- A list of IP address ranges ( CIDRs). Used to create an allow list of IP addresses for a private workforce. Workers will only be able to log in to their worker portal from an IP address within this range. By default, a workforce isn't restricted to specific IP addresses. * **Cidrs** *(list) --* **[REQUIRED]** A list of one to ten Classless Inter-Domain Routing (CIDR) values. Maximum: Ten CIDR values Note: The following Length Constraints apply to individual CIDR values in the CIDR value list. * *(string) --* * **WorkforceName** (*string*) -- **[REQUIRED]** The name of the private workforce. * **Tags** (*list*) -- An array of key-value pairs that contain metadata to help you categorize and organize our workforce. Each tag consists of a key and a value, both of which you define. * *(dict) --* A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources. You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags. For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy. * **Key** *(string) --* **[REQUIRED]** The tag key. Tag keys must be unique per resource. * **Value** *(string) --* **[REQUIRED]** The tag value. * **WorkforceVpcConfig** (*dict*) -- Use this parameter to configure a workforce using VPC. * **VpcId** *(string) --* The ID of the VPC that the workforce uses for communication. * **SecurityGroupIds** *(list) --* The VPC security group IDs, in the form "sg-xxxxxxxx". The security groups must be for the same VPC as specified in the subnet. * *(string) --* * **Subnets** *(list) --* The ID of the subnets in the VPC that you want to connect. * *(string) --* * **IpAddressType** (*string*) -- Use this parameter to specify whether you want "IPv4" only or "dualstack" ( "IPv4" and "IPv6") to support your labeling workforce. Return type: dict Returns: **Response Syntax** { 'WorkforceArn': 'string' } **Response Structure** * *(dict) --* * **WorkforceArn** *(string) --* The Amazon Resource Name (ARN) of the workforce. SageMaker / Client / list_algorithms list_algorithms *************** SageMaker.Client.list_algorithms(**kwargs) Lists the machine learning algorithms that have been created. See also: AWS API Documentation **Request Syntax** response = client.list_algorithms( CreationTimeAfter=datetime(2015, 1, 1), CreationTimeBefore=datetime(2015, 1, 1), MaxResults=123, NameContains='string', NextToken='string', SortBy='Name'|'CreationTime', SortOrder='Ascending'|'Descending' ) Parameters: * **CreationTimeAfter** (*datetime*) -- A filter that returns only algorithms created after the specified time (timestamp). * **CreationTimeBefore** (*datetime*) -- A filter that returns only algorithms created before the specified time (timestamp). * **MaxResults** (*integer*) -- The maximum number of algorithms to return in the response. * **NameContains** (*string*) -- A string in the algorithm name. This filter returns only algorithms whose name contains the specified string. * **NextToken** (*string*) -- If the response to a previous "ListAlgorithms" request was truncated, the response includes a "NextToken". To retrieve the next set of algorithms, use the token in the next request. * **SortBy** (*string*) -- The parameter by which to sort the results. The default is "CreationTime". * **SortOrder** (*string*) -- The sort order for the results. The default is "Ascending". Return type: dict Returns: **Response Syntax** { 'AlgorithmSummaryList': [ { 'AlgorithmName': 'string', 'AlgorithmArn': 'string', 'AlgorithmDescription': 'string', 'CreationTime': datetime(2015, 1, 1), 'AlgorithmStatus': 'Pending'|'InProgress'|'Completed'|'Failed'|'Deleting' }, ], 'NextToken': 'string' } **Response Structure** * *(dict) --* * **AlgorithmSummaryList** *(list) --* >An array of "AlgorithmSummary" objects, each of which lists an algorithm. * *(dict) --* Provides summary information about an algorithm. * **AlgorithmName** *(string) --* The name of the algorithm that is described by the summary. * **AlgorithmArn** *(string) --* The Amazon Resource Name (ARN) of the algorithm. * **AlgorithmDescription** *(string) --* A brief description of the algorithm. * **CreationTime** *(datetime) --* A timestamp that shows when the algorithm was created. * **AlgorithmStatus** *(string) --* The overall status of the algorithm. * **NextToken** *(string) --* If the response is truncated, SageMaker returns this token. To retrieve the next set of algorithms, use it in the subsequent request. SageMaker / Client / create_model_explainability_job_definition create_model_explainability_job_definition ****************************************** SageMaker.Client.create_model_explainability_job_definition(**kwargs) Creates the definition for a model explainability job. See also: AWS API Documentation **Request Syntax** response = client.create_model_explainability_job_definition( JobDefinitionName='string', ModelExplainabilityBaselineConfig={ 'BaseliningJobName': 'string', 'ConstraintsResource': { 'S3Uri': 'string' } }, ModelExplainabilityAppSpecification={ 'ImageUri': 'string', 'ConfigUri': 'string', 'Environment': { 'string': 'string' } }, ModelExplainabilityJobInput={ 'EndpointInput': { 'EndpointName': 'string', 'LocalPath': 'string', 'S3InputMode': 'Pipe'|'File', 'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key', 'FeaturesAttribute': 'string', 'InferenceAttribute': 'string', 'ProbabilityAttribute': 'string', 'ProbabilityThresholdAttribute': 123.0, 'StartTimeOffset': 'string', 'EndTimeOffset': 'string', 'ExcludeFeaturesAttribute': 'string' }, 'BatchTransformInput': { 'DataCapturedDestinationS3Uri': 'string', 'DatasetFormat': { 'Csv': { 'Header': True|False }, 'Json': { 'Line': True|False }, 'Parquet': {} }, 'LocalPath': 'string', 'S3InputMode': 'Pipe'|'File', 'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key', 'FeaturesAttribute': 'string', 'InferenceAttribute': 'string', 'ProbabilityAttribute': 'string', 'ProbabilityThresholdAttribute': 123.0, 'StartTimeOffset': 'string', 'EndTimeOffset': 'string', 'ExcludeFeaturesAttribute': 'string' } }, ModelExplainabilityJobOutputConfig={ 'MonitoringOutputs': [ { 'S3Output': { 'S3Uri': 'string', 'LocalPath': 'string', 'S3UploadMode': 'Continuous'|'EndOfJob' } }, ], 'KmsKeyId': 'string' }, JobResources={ 'ClusterConfig': { 'InstanceCount': 123, 'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.8xlarge'|'ml.r5d.12xlarge'|'ml.r5d.16xlarge'|'ml.r5d.24xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge', 'VolumeSizeInGB': 123, 'VolumeKmsKeyId': 'string' } }, NetworkConfig={ 'EnableInterContainerTrafficEncryption': True|False, 'EnableNetworkIsolation': True|False, 'VpcConfig': { 'SecurityGroupIds': [ 'string', ], 'Subnets': [ 'string', ] } }, RoleArn='string', StoppingCondition={ 'MaxRuntimeInSeconds': 123 }, Tags=[ { 'Key': 'string', 'Value': 'string' }, ] ) Parameters: * **JobDefinitionName** (*string*) -- **[REQUIRED]** The name of the model explainability job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account. * **ModelExplainabilityBaselineConfig** (*dict*) -- The baseline configuration for a model explainability job. * **BaseliningJobName** *(string) --* The name of the baseline model explainability job. * **ConstraintsResource** *(dict) --* The constraints resource for a monitoring job. * **S3Uri** *(string) --* The Amazon S3 URI for the constraints resource. * **ModelExplainabilityAppSpecification** (*dict*) -- **[REQUIRED]** Configures the model explainability job to run a specified Docker container image. * **ImageUri** *(string) --* **[REQUIRED]** The container image to be run by the model explainability job. * **ConfigUri** *(string) --* **[REQUIRED]** JSON formatted Amazon S3 file that defines explainability parameters. For more information on this JSON configuration file, see Configure model explainability parameters. * **Environment** *(dict) --* Sets the environment variables in the Docker container. * *(string) --* * *(string) --* * **ModelExplainabilityJobInput** (*dict*) -- **[REQUIRED]** Inputs for the model explainability job. * **EndpointInput** *(dict) --* Input object for the endpoint * **EndpointName** *(string) --* **[REQUIRED]** An endpoint in customer's account which has enabled "DataCaptureConfig" enabled. * **LocalPath** *(string) --* **[REQUIRED]** Path to the filesystem where the endpoint data is available to the container. * **S3InputMode** *(string) --* Whether the "Pipe" or "File" is used as the input mode for transferring data for the monitoring job. "Pipe" mode is recommended for large datasets. "File" mode is useful for small files that fit in memory. Defaults to "File". * **S3DataDistributionType** *(string) --* Whether input data distributed in Amazon S3 is fully replicated or sharded by an Amazon S3 key. Defaults to "FullyReplicated" * **FeaturesAttribute** *(string) --* The attributes of the input data that are the input features. * **InferenceAttribute** *(string) --* The attribute of the input data that represents the ground truth label. * **ProbabilityAttribute** *(string) --* In a classification problem, the attribute that represents the class probability. * **ProbabilityThresholdAttribute** *(float) --* The threshold for the class probability to be evaluated as a positive result. * **StartTimeOffset** *(string) --* If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs. * **EndTimeOffset** *(string) --* If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs. * **ExcludeFeaturesAttribute** *(string) --* The attributes of the input data to exclude from the analysis. * **BatchTransformInput** *(dict) --* Input object for the batch transform job. * **DataCapturedDestinationS3Uri** *(string) --* **[REQUIRED]** The Amazon S3 location being used to capture the data. * **DatasetFormat** *(dict) --* **[REQUIRED]** The dataset format for your batch transform job. * **Csv** *(dict) --* The CSV dataset used in the monitoring job. * **Header** *(boolean) --* Indicates if the CSV data has a header. * **Json** *(dict) --* The JSON dataset used in the monitoring job * **Line** *(boolean) --* Indicates if the file should be read as a JSON object per line. * **Parquet** *(dict) --* The Parquet dataset used in the monitoring job * **LocalPath** *(string) --* **[REQUIRED]** Path to the filesystem where the batch transform data is available to the container. * **S3InputMode** *(string) --* Whether the "Pipe" or "File" is used as the input mode for transferring data for the monitoring job. "Pipe" mode is recommended for large datasets. "File" mode is useful for small files that fit in memory. Defaults to "File". * **S3DataDistributionType** *(string) --* Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to "FullyReplicated" * **FeaturesAttribute** *(string) --* The attributes of the input data that are the input features. * **InferenceAttribute** *(string) --* The attribute of the input data that represents the ground truth label. * **ProbabilityAttribute** *(string) --* In a classification problem, the attribute that represents the class probability. * **ProbabilityThresholdAttribute** *(float) --* The threshold for the class probability to be evaluated as a positive result. * **StartTimeOffset** *(string) --* If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs. * **EndTimeOffset** *(string) --* If specified, monitoring jobs subtract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs. * **ExcludeFeaturesAttribute** *(string) --* The attributes of the input data to exclude from the analysis. * **ModelExplainabilityJobOutputConfig** (*dict*) -- **[REQUIRED]** The output configuration for monitoring jobs. * **MonitoringOutputs** *(list) --* **[REQUIRED]** Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded. * *(dict) --* The output object for a monitoring job. * **S3Output** *(dict) --* **[REQUIRED]** The Amazon S3 storage location where the results of a monitoring job are saved. * **S3Uri** *(string) --* **[REQUIRED]** A URI that identifies the Amazon S3 storage location where Amazon SageMaker AI saves the results of a monitoring job. * **LocalPath** *(string) --* **[REQUIRED]** The local path to the Amazon S3 storage location where Amazon SageMaker AI saves the results of a monitoring job. LocalPath is an absolute path for the output data. * **S3UploadMode** *(string) --* Whether to upload the results of the monitoring job continuously or after the job completes. * **KmsKeyId** *(string) --* The Key Management Service (KMS) key that Amazon SageMaker AI uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. * **JobResources** (*dict*) -- **[REQUIRED]** Identifies the resources to deploy for a monitoring job. * **ClusterConfig** *(dict) --* **[REQUIRED]** The configuration for the cluster resources used to run the processing job. * **InstanceCount** *(integer) --* **[REQUIRED]** The number of ML compute instances to use in the model monitoring job. For distributed processing jobs, specify a value greater than 1. The default value is 1. * **InstanceType** *(string) --* **[REQUIRED]** The ML compute instance type for the processing job. * **VolumeSizeInGB** *(integer) --* **[REQUIRED]** The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario. * **VolumeKmsKeyId** *(string) --* The Key Management Service (KMS) key that Amazon SageMaker AI uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the model monitoring job. * **NetworkConfig** (*dict*) -- Networking options for a model explainability job. * **EnableInterContainerTrafficEncryption** *(boolean) --* Whether to encrypt all communications between the instances used for the monitoring jobs. Choose "True" to encrypt communications. Encryption provides greater security for distributed jobs, but the processing might take longer. * **EnableNetworkIsolation** *(boolean) --* Whether to allow inbound and outbound network calls to and from the containers used for the monitoring job. * **VpcConfig** *(dict) --* Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC. * **SecurityGroupIds** *(list) --* **[REQUIRED]** The VPC security group IDs, in the form "sg-xxxxxxxx". Specify the security groups for the VPC that is specified in the "Subnets" field. * *(string) --* * **Subnets** *(list) --* **[REQUIRED]** The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones. * *(string) --* * **RoleArn** (*string*) -- **[REQUIRED]** The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker AI can assume to perform tasks on your behalf. * **StoppingCondition** (*dict*) -- A time limit for how long the monitoring job is allowed to run before stopping. * **MaxRuntimeInSeconds** *(integer) --* **[REQUIRED]** The maximum runtime allowed in seconds. Note: The "MaxRuntimeInSeconds" cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds. * **Tags** (*list*) -- (Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the *Amazon Web Services Billing and Cost Management User Guide*. * *(dict) --* A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources. You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags. For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy. * **Key** *(string) --* **[REQUIRED]** The tag key. Tag keys must be unique per resource. * **Value** *(string) --* **[REQUIRED]** The tag value. Return type: dict Returns: **Response Syntax** { 'JobDefinitionArn': 'string' } **Response Structure** * *(dict) --* * **JobDefinitionArn** *(string) --* The Amazon Resource Name (ARN) of the model explainability job. **Exceptions** * "SageMaker.Client.exceptions.ResourceInUse" * "SageMaker.Client.exceptions.ResourceLimitExceeded" SageMaker / Client / list_edge_packaging_jobs list_edge_packaging_jobs ************************ SageMaker.Client.list_edge_packaging_jobs(**kwargs) Returns a list of edge packaging jobs. See also: AWS API Documentation **Request Syntax** response = client.list_edge_packaging_jobs( NextToken='string', MaxResults=123, CreationTimeAfter=datetime(2015, 1, 1), CreationTimeBefore=datetime(2015, 1, 1), LastModifiedTimeAfter=datetime(2015, 1, 1), LastModifiedTimeBefore=datetime(2015, 1, 1), NameContains='string', ModelNameContains='string', StatusEquals='STARTING'|'INPROGRESS'|'COMPLETED'|'FAILED'|'STOPPING'|'STOPPED', SortBy='NAME'|'MODEL_NAME'|'CREATION_TIME'|'LAST_MODIFIED_TIME'|'STATUS', SortOrder='Ascending'|'Descending' ) Parameters: * **NextToken** (*string*) -- The response from the last list when returning a list large enough to need tokening. * **MaxResults** (*integer*) -- Maximum number of results to select. * **CreationTimeAfter** (*datetime*) -- Select jobs where the job was created after specified time. * **CreationTimeBefore** (*datetime*) -- Select jobs where the job was created before specified time. * **LastModifiedTimeAfter** (*datetime*) -- Select jobs where the job was updated after specified time. * **LastModifiedTimeBefore** (*datetime*) -- Select jobs where the job was updated before specified time. * **NameContains** (*string*) -- Filter for jobs containing this name in their packaging job name. * **ModelNameContains** (*string*) -- Filter for jobs where the model name contains this string. * **StatusEquals** (*string*) -- The job status to filter for. * **SortBy** (*string*) -- Use to specify what column to sort by. * **SortOrder** (*string*) -- What direction to sort by. Return type: dict Returns: **Response Syntax** { 'EdgePackagingJobSummaries': [ { 'EdgePackagingJobArn': 'string', 'EdgePackagingJobName': 'string', 'EdgePackagingJobStatus': 'STARTING'|'INPROGRESS'|'COMPLETED'|'FAILED'|'STOPPING'|'STOPPED', 'CompilationJobName': 'string', 'ModelName': 'string', 'ModelVersion': 'string', 'CreationTime': datetime(2015, 1, 1), 'LastModifiedTime': datetime(2015, 1, 1) }, ], 'NextToken': 'string' } **Response Structure** * *(dict) --* * **EdgePackagingJobSummaries** *(list) --* Summaries of edge packaging jobs. * *(dict) --* Summary of edge packaging job. * **EdgePackagingJobArn** *(string) --* The Amazon Resource Name (ARN) of the edge packaging job. * **EdgePackagingJobName** *(string) --* The name of the edge packaging job. * **EdgePackagingJobStatus** *(string) --* The status of the edge packaging job. * **CompilationJobName** *(string) --* The name of the SageMaker Neo compilation job. * **ModelName** *(string) --* The name of the model. * **ModelVersion** *(string) --* The version of the model. * **CreationTime** *(datetime) --* The timestamp of when the job was created. * **LastModifiedTime** *(datetime) --* The timestamp of when the edge packaging job was last updated. * **NextToken** *(string) --* Token to use when calling the next page of results. SageMaker / Client / describe_auto_ml_job describe_auto_ml_job ******************** SageMaker.Client.describe_auto_ml_job(**kwargs) Returns information about an AutoML job created by calling CreateAutoMLJob. Note: AutoML jobs created by calling CreateAutoMLJobV2 cannot be described by "DescribeAutoMLJob". See also: AWS API Documentation **Request Syntax** response = client.describe_auto_ml_job( AutoMLJobName='string' ) Parameters: **AutoMLJobName** (*string*) -- **[REQUIRED]** Requests information about an AutoML job using its unique name. Return type: dict Returns: **Response Syntax** { 'AutoMLJobName': 'string', 'AutoMLJobArn': 'string', 'InputDataConfig': [ { 'DataSource': { 'S3DataSource': { 'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile', 'S3Uri': 'string' } }, 'CompressionType': 'None'|'Gzip', 'TargetAttributeName': 'string', 'ContentType': 'string', 'ChannelType': 'training'|'validation', 'SampleWeightAttributeName': 'string' }, ], 'OutputDataConfig': { 'KmsKeyId': 'string', 'S3OutputPath': 'string' }, 'RoleArn': 'string', 'AutoMLJobObjective': { 'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'BalancedAccuracy'|'R2'|'Recall'|'RecallMacro'|'Precision'|'PrecisionMacro'|'MAE'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss' }, 'ProblemType': 'BinaryClassification'|'MulticlassClassification'|'Regression', 'AutoMLJobConfig': { 'CompletionCriteria': { 'MaxCandidates': 123, 'MaxRuntimePerTrainingJobInSeconds': 123, 'MaxAutoMLJobRuntimeInSeconds': 123 }, 'SecurityConfig': { 'VolumeKmsKeyId': 'string', 'EnableInterContainerTrafficEncryption': True|False, 'VpcConfig': { 'SecurityGroupIds': [ 'string', ], 'Subnets': [ 'string', ] } }, 'CandidateGenerationConfig': { 'FeatureSpecificationS3Uri': 'string', 'AlgorithmsConfig': [ { 'AutoMLAlgorithms': [ 'xgboost'|'linear-learner'|'mlp'|'lightgbm'|'catboost'|'randomforest'|'extra-trees'|'nn-torch'|'fastai'|'cnn-qr'|'deepar'|'prophet'|'npts'|'arima'|'ets', ] }, ] }, 'DataSplitConfig': { 'ValidationFraction': ... }, 'Mode': 'AUTO'|'ENSEMBLING'|'HYPERPARAMETER_TUNING' }, 'CreationTime': datetime(2015, 1, 1), 'EndTime': datetime(2015, 1, 1), 'LastModifiedTime': datetime(2015, 1, 1), 'FailureReason': 'string', 'PartialFailureReasons': [ { 'PartialFailureMessage': 'string' }, ], 'BestCandidate': { 'CandidateName': 'string', 'FinalAutoMLJobObjectiveMetric': { 'Type': 'Maximize'|'Minimize', 'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'BalancedAccuracy'|'R2'|'Recall'|'RecallMacro'|'Precision'|'PrecisionMacro'|'MAE'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss', 'Value': ..., 'StandardMetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'BalancedAccuracy'|'R2'|'Recall'|'RecallMacro'|'Precision'|'PrecisionMacro'|'MAE'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss' }, 'ObjectiveStatus': 'Succeeded'|'Pending'|'Failed', 'CandidateSteps': [ { 'CandidateStepType': 'AWS::SageMaker::TrainingJob'|'AWS::SageMaker::TransformJob'|'AWS::SageMaker::ProcessingJob', 'CandidateStepArn': 'string', 'CandidateStepName': 'string' }, ], 'CandidateStatus': 'Completed'|'InProgress'|'Failed'|'Stopped'|'Stopping', 'InferenceContainers': [ { 'Image': 'string', 'ModelDataUrl': 'string', 'Environment': { 'string': 'string' } }, ], 'CreationTime': datetime(2015, 1, 1), 'EndTime': datetime(2015, 1, 1), 'LastModifiedTime': datetime(2015, 1, 1), 'FailureReason': 'string', 'CandidateProperties': { 'CandidateArtifactLocations': { 'Explainability': 'string', 'ModelInsights': 'string', 'BacktestResults': 'string' }, 'CandidateMetrics': [ { 'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'BalancedAccuracy'|'R2'|'Recall'|'RecallMacro'|'Precision'|'PrecisionMacro'|'MAE'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss', 'StandardMetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'MAE'|'R2'|'BalancedAccuracy'|'Precision'|'PrecisionMacro'|'Recall'|'RecallMacro'|'LogLoss'|'InferenceLatency'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss'|'Rouge1'|'Rouge2'|'RougeL'|'RougeLSum'|'Perplexity'|'ValidationLoss'|'TrainingLoss', 'Value': ..., 'Set': 'Train'|'Validation'|'Test' }, ] }, 'InferenceContainerDefinitions': { 'string': [ { 'Image': 'string', 'ModelDataUrl': 'string', 'Environment': { 'string': 'string' } }, ] } }, 'AutoMLJobStatus': 'Completed'|'InProgress'|'Failed'|'Stopped'|'Stopping', 'AutoMLJobSecondaryStatus': 'Starting'|'MaxCandidatesReached'|'Failed'|'Stopped'|'MaxAutoMLJobRuntimeReached'|'Stopping'|'CandidateDefinitionsGenerated'|'Completed'|'ExplainabilityError'|'DeployingModel'|'ModelDeploymentError'|'GeneratingModelInsightsReport'|'ModelInsightsError'|'AnalyzingData'|'FeatureEngineering'|'ModelTuning'|'GeneratingExplainabilityReport'|'TrainingModels'|'PreTraining', 'GenerateCandidateDefinitionsOnly': True|False, 'AutoMLJobArtifacts': { 'CandidateDefinitionNotebookLocation': 'string', 'DataExplorationNotebookLocation': 'string' }, 'ResolvedAttributes': { 'AutoMLJobObjective': { 'MetricName': 'Accuracy'|'MSE'|'F1'|'F1macro'|'AUC'|'RMSE'|'BalancedAccuracy'|'R2'|'Recall'|'RecallMacro'|'Precision'|'PrecisionMacro'|'MAE'|'MAPE'|'MASE'|'WAPE'|'AverageWeightedQuantileLoss' }, 'ProblemType': 'BinaryClassification'|'MulticlassClassification'|'Regression', 'CompletionCriteria': { 'MaxCandidates': 123, 'MaxRuntimePerTrainingJobInSeconds': 123, 'MaxAutoMLJobRuntimeInSeconds': 123 } }, 'ModelDeployConfig': { 'AutoGenerateEndpointName': True|False, 'EndpointName': 'string' }, 'ModelDeployResult': { 'EndpointName': 'string' } } **Response Structure** * *(dict) --* * **AutoMLJobName** *(string) --* Returns the name of the AutoML job. * **AutoMLJobArn** *(string) --* Returns the ARN of the AutoML job. * **InputDataConfig** *(list) --* Returns the input data configuration for the AutoML job. * *(dict) --* A channel is a named input source that training algorithms can consume. The validation dataset size is limited to less than 2 GB. The training dataset size must be less than 100 GB. For more information, see Channel. Note: A validation dataset must contain the same headers as the training dataset. * **DataSource** *(dict) --* The data source for an AutoML channel. * **S3DataSource** *(dict) --* The Amazon S3 location of the input data. * **S3DataType** *(string) --* The data type. * If you choose "S3Prefix", "S3Uri" identifies a key name prefix. SageMaker AI uses all objects that match the specified key name prefix for model training. The "S3Prefix" should have the following format: "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE- FOLDER-OR-FILE" * If you choose "ManifestFile", "S3Uri" identifies an object that is a manifest file containing a list of object keys that you want SageMaker AI to use for model training. A "ManifestFile" should have the format shown below: "[ {"prefix": "s3 ://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/DOC- EXAMPLE-PREFIX/"}," ""DOC-EXAMPLE-RELATIVE-PATH /DOC-EXAMPLE-FOLDER/DATA-1"," ""DOC-EXAMPLE- RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-2"," "... "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE- FOLDER/DATA-N" ]" * If you choose "AugmentedManifestFile", "S3Uri" identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. "AugmentedManifestFile" is available for V2 API jobs only (for example, for jobs created by calling "CreateAutoMLJobV2"). Here is a minimal, single-record example of an "AugmentedManifestFile": "{"source-ref": "s3 ://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE- FOLDER/cats/cat.jpg"," ""label-metadata": {"class- name": "cat"" } For more information on "AugmentedManifestFile", see Provide Dataset Metadata to Training Jobs with an Augmented Manifest File. * **S3Uri** *(string) --* The URL to the Amazon S3 data source. The Uri refers to the Amazon S3 prefix or ManifestFile depending on the data type. * **CompressionType** *(string) --* You can use "Gzip" or "None". The default value is "None". * **TargetAttributeName** *(string) --* The name of the target variable in supervised learning, usually represented by 'y'. * **ContentType** *(string) --* The content type of the data from the input source. You can use "text/csv;header=present" or "x-application/vnd.amazon+parquet". The default value is "text/csv;header=present". * **ChannelType** *(string) --* The channel type (optional) is an "enum" string. The default value is "training". Channels for training and validation must share the same "ContentType" and "TargetAttributeName". For information on specifying training and validation channel types, see How to specify training and validation datasets. * **SampleWeightAttributeName** *(string) --* If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model. This column is not considered as a predictive feature. For more information on Autopilot metrics, see Metrics and validation. Sample weights should be numeric, non-negative, with larger values indicating which rows are more important than others. Data points that have invalid or no weight value are excluded. Support for sample weights is available in Ensembling mode only. * **OutputDataConfig** *(dict) --* Returns the job's output data config. * **KmsKeyId** *(string) --* The Key Management Service encryption key ID. * **S3OutputPath** *(string) --* The Amazon S3 output path. Must be 512 characters or less. * **RoleArn** *(string) --* The ARN of the IAM role that has read permission to the input data location and write permission to the output data location in Amazon S3. * **AutoMLJobObjective** *(dict) --* Returns the job's objective. * **MetricName** *(string) --* The name of the objective metric used to measure the predictive quality of a machine learning system. During training, the model's parameters are updated iteratively to optimize its performance based on the feedback provided by the objective metric when evaluating the model on the validation dataset. The list of available metrics supported by Autopilot and the default metric applied when you do not specify a metric name explicitly depend on the problem type. * For tabular problem types: * List of available metrics: * Regression: "MAE", "MSE", "R2", "RMSE" * Binary classification: "Accuracy", "AUC", "BalancedAccuracy", "F1", "Precision", "Recall" * Multiclass classification: "Accuracy", "BalancedAccuracy", "F1macro", "PrecisionMacro", "RecallMacro" For a description of each metric, see Autopilot metrics for classification and regression. * Default objective metrics: * Regression: "MSE". * Binary classification: "F1". * Multiclass classification: "Accuracy". * For image or text classification problem types: * List of available metrics: "Accuracy" For a description of each metric, see Autopilot metrics for text and image classification. * Default objective metrics: "Accuracy" * For time-series forecasting problem types: * List of available metrics: "RMSE", "wQL", "Average wQL", "MASE", "MAPE", "WAPE" For a description of each metric, see Autopilot metrics for time-series forecasting. * Default objective metrics: "AverageWeightedQuantileLoss" * For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the "AutoMLJobObjective" field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross- entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot. * **ProblemType** *(string) --* Returns the job's problem type. * **AutoMLJobConfig** *(dict) --* Returns the configuration for the AutoML job. * **CompletionCriteria** *(dict) --* How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate. * **MaxCandidates** *(integer) --* The maximum number of times a training job is allowed to run. For text and image classification, time-series forecasting, as well as text generation (LLMs fine- tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750. * **MaxRuntimePerTrainingJobInSeconds** *(integer) --* The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action. For job V2s (jobs created by calling "CreateAutoMLJobV2"), this field controls the runtime of the job candidate. For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds). * **MaxAutoMLJobRuntimeInSeconds** *(integer) --* The maximum runtime, in seconds, an AutoML job has to complete. If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best- performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed. * **SecurityConfig** *(dict) --* The security configuration for traffic encryption or Amazon VPC settings. * **VolumeKmsKeyId** *(string) --* The key used to encrypt stored data. * **EnableInterContainerTrafficEncryption** *(boolean) --* Whether to use traffic encryption between the container layers. * **VpcConfig** *(dict) --* The VPC configuration. * **SecurityGroupIds** *(list) --* The VPC security group IDs, in the form "sg-xxxxxxxx". Specify the security groups for the VPC that is specified in the "Subnets" field. * *(string) --* * **Subnets** *(list) --* The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones. * *(string) --* * **CandidateGenerationConfig** *(dict) --* The configuration for generating a candidate for an AutoML job (optional). * **FeatureSpecificationS3Uri** *(string) --* A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job. You can input "FeatureAttributeNames" (optional) in JSON format as shown below: "{ "FeatureAttributeNames":["col1", "col2", ...] }". You can also specify the data type of the feature (optional) in the format shown below: "{ "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }" Note: These column keys may not include the target column. In ensembling mode, Autopilot only supports the following data types: "numeric", "categorical", "text", and "datetime". In HPO mode, Autopilot can support "numeric", "categorical", "text", "datetime", and "sequence". If only "FeatureDataTypes" is provided, the column keys ( "col1", "col2",..) should be a subset of the column names in the input data. If both "FeatureDataTypes" and "FeatureAttributeNames" are provided, then the column keys should be a subset of the column names provided in "FeatureAttributeNames". The key name "FeatureAttributeNames" is fixed. The values listed in "["col1", "col2", ...]" are case sensitive and should be a list of strings containing unique values that are a subset of the column names in the input data. The list of columns provided must not include the target column. * **AlgorithmsConfig** *(list) --* Stores the configuration information for the selection of algorithms trained on tabular data. The list of available algorithms to choose from depends on the training mode set in TabularJobConfig.Mode. * "AlgorithmsConfig" should not be set if the training mode is set on "AUTO". * When "AlgorithmsConfig" is provided, one "AutoMLAlgorithms" attribute must be set and one only. If the list of algorithms provided as values for "AutoMLAlgorithms" is empty, "CandidateGenerationConfig" uses the full set of algorithms for the given training mode. * When "AlgorithmsConfig" is not provided, "CandidateGenerationConfig" uses the full set of algorithms for the given training mode. For the list of all algorithms per problem type and training mode, see AutoMLAlgorithmConfig. For more information on each algorithm, see the Algorithm support section in Autopilot developer guide. * *(dict) --* The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job. * **AutoMLAlgorithms** *(list) --* The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job. * For the tabular problem type "TabularJobConfig": Note: Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode ( "ENSEMBLING" or "HYPERPARAMETER_TUNING"). Choose a minimum of 1 algorithm. * In "ENSEMBLING" mode: * "catboost" * "extra-trees" * "fastai" * "lightgbm" * "linear-learner" * "nn-torch" * "randomforest" * "xgboost" * In "HYPERPARAMETER_TUNING" mode: * "linear-learner" * "mlp" * "xgboost" * For the time-series forecasting problem type "TimeSeriesForecastingJobConfig": * Choose your algorithms from this list. * "cnn-qr" * "deepar" * "prophet" * "arima" * "npts" * "ets" * *(string) --* * **DataSplitConfig** *(dict) --* The configuration for splitting the input training dataset. Type: AutoMLDataSplitConfig * **ValidationFraction** *(float) --* The validation fraction (optional) is a float that specifies the portion of the training dataset to be used for validation. The default value is 0.2, and values must be greater than 0 and less than 1. We recommend setting this value to be less than 0.5. * **Mode** *(string) --* The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting "AUTO". In "AUTO" mode, Autopilot chooses "ENSEMBLING" for datasets smaller than 100 MB, and "HYPERPARAMETER_TUNING" for larger ones. The "ENSEMBLING" mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by "ENSEMBLING" mode. The "HYPERPARAMETER_TUNING" (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by "HYPERPARAMETER_TUNING" mode. * **CreationTime** *(datetime) --* Returns the creation time of the AutoML job. * **EndTime** *(datetime) --* Returns the end time of the AutoML job. * **LastModifiedTime** *(datetime) --* Returns the job's last modified time. * **FailureReason** *(string) --* Returns the failure reason for an AutoML job, when applicable. * **PartialFailureReasons** *(list) --* Returns a list of reasons for partial failures within an AutoML job. * *(dict) --* The reason for a partial failure of an AutoML job. * **PartialFailureMessage** *(string) --* The message containing the reason for a partial failure of an AutoML job. * **BestCandidate** *(dict) --* The best model candidate selected by SageMaker AI Autopilot using both the best objective metric and lowest InferenceLatency for an experiment. * **CandidateName** *(string) --* The name of the candidate. * **FinalAutoMLJobObjectiveMetric** *(dict) --* The best candidate result from an AutoML training job. * **Type** *(string) --* The type of metric with the best result. * **MetricName** *(string) --* The name of the metric with the best result. For a description of the possible objective metrics, see AutoMLJobObjective$MetricName. * **Value** *(float) --* The value of the metric with the best result. * **StandardMetricName** *(string) --* The name of the standard metric. For a description of the standard metrics, see Autopilot candidate metrics. * **ObjectiveStatus** *(string) --* The objective's status. * **CandidateSteps** *(list) --* Information about the candidate's steps. * *(dict) --* Information about the steps for a candidate and what step it is working on. * **CandidateStepType** *(string) --* Whether the candidate is at the transform, training, or processing step. * **CandidateStepArn** *(string) --* The ARN for the candidate's step. * **CandidateStepName** *(string) --* The name for the candidate's step. * **CandidateStatus** *(string) --* The candidate's status. * **InferenceContainers** *(list) --* Information about the recommended inference container definitions. * *(dict) --* A list of container definitions that describe the different containers that make up an AutoML candidate. For more information, see ContainerDefinition. * **Image** *(string) --* The Amazon Elastic Container Registry (Amazon ECR) path of the container. For more information, see ContainerDefinition. * **ModelDataUrl** *(string) --* The location of the model artifacts. For more information, see ContainerDefinition. * **Environment** *(dict) --* The environment variables to set in the container. For more information, see ContainerDefinition. * *(string) --* * *(string) --* * **CreationTime** *(datetime) --* The creation time. * **EndTime** *(datetime) --* The end time. * **LastModifiedTime** *(datetime) --* The last modified time. * **FailureReason** *(string) --* The failure reason. * **CandidateProperties** *(dict) --* The properties of an AutoML candidate job. * **CandidateArtifactLocations** *(dict) --* The Amazon S3 prefix to the artifacts generated for an AutoML candidate. * **Explainability** *(string) --* The Amazon S3 prefix to the explainability artifacts generated for the AutoML candidate. * **ModelInsights** *(string) --* The Amazon S3 prefix to the model insight artifacts generated for the AutoML candidate. * **BacktestResults** *(string) --* The Amazon S3 prefix to the accuracy metrics and the inference results observed over the testing window. Available only for the time-series forecasting problem type. * **CandidateMetrics** *(list) --* Information about the candidate metrics for an AutoML job. * *(dict) --* Information about the metric for a candidate produced by an AutoML job. * **MetricName** *(string) --* The name of the metric. * **StandardMetricName** *(string) --* The name of the standard metric. Note: For definitions of the standard metrics, see Autopilot candidate metrics. * **Value** *(float) --* The value of the metric. * **Set** *(string) --* The dataset split from which the AutoML job produced the metric. * **InferenceContainerDefinitions** *(dict) --* The mapping of all supported processing unit (CPU, GPU, etc...) to inference container definitions for the candidate. This field is populated for the AutoML jobs V2 (for example, for jobs created by calling "CreateAutoMLJobV2") related to image or text classification problem types only. * *(string) --* Processing unit for an inference container. Currently Autopilot only supports "CPU" or "GPU". * *(list) --* Information about the recommended inference container definitions. * *(dict) --* A list of container definitions that describe the different containers that make up an AutoML candidate. For more information, see ContainerDefinition. * **Image** *(string) --* The Amazon Elastic Container Registry (Amazon ECR) path of the container. For more information, see ContainerDefinition. * **ModelDataUrl** *(string) --* The location of the model artifacts. For more information, see ContainerDefinition. * **Environment** *(dict) --* The environment variables to set in the container. For more information, see ContainerDefinition. * *(string) --* * *(string) --* * **AutoMLJobStatus** *(string) --* Returns the status of the AutoML job. * **AutoMLJobSecondaryStatus** *(string) --* Returns the secondary status of the AutoML job. * **GenerateCandidateDefinitionsOnly** *(boolean) --* Indicates whether the output for an AutoML job generates candidate definitions only. * **AutoMLJobArtifacts** *(dict) --* Returns information on the job's artifacts found in "AutoMLJobArtifacts". * **CandidateDefinitionNotebookLocation** *(string) --* The URL of the notebook location. * **DataExplorationNotebookLocation** *(string) --* The URL of the notebook location. * **ResolvedAttributes** *(dict) --* Contains "ProblemType", "AutoMLJobObjective", and "CompletionCriteria". If you do not provide these values, they are inferred. * **AutoMLJobObjective** *(dict) --* Specifies a metric to minimize or maximize as the objective of an AutoML job. * **MetricName** *(string) --* The name of the objective metric used to measure the predictive quality of a machine learning system. During training, the model's parameters are updated iteratively to optimize its performance based on the feedback provided by the objective metric when evaluating the model on the validation dataset. The list of available metrics supported by Autopilot and the default metric applied when you do not specify a metric name explicitly depend on the problem type. * For tabular problem types: * List of available metrics: * Regression: "MAE", "MSE", "R2", "RMSE" * Binary classification: "Accuracy", "AUC", "BalancedAccuracy", "F1", "Precision", "Recall" * Multiclass classification: "Accuracy", "BalancedAccuracy", "F1macro", "PrecisionMacro", "RecallMacro" For a description of each metric, see Autopilot metrics for classification and regression. * Default objective metrics: * Regression: "MSE". * Binary classification: "F1". * Multiclass classification: "Accuracy". * For image or text classification problem types: * List of available metrics: "Accuracy" For a description of each metric, see Autopilot metrics for text and image classification. * Default objective metrics: "Accuracy" * For time-series forecasting problem types: * List of available metrics: "RMSE", "wQL", "Average wQL", "MASE", "MAPE", "WAPE" For a description of each metric, see Autopilot metrics for time-series forecasting. * Default objective metrics: "AverageWeightedQuantileLoss" * For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the "AutoMLJobObjective" field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot. * **ProblemType** *(string) --* The problem type. * **CompletionCriteria** *(dict) --* How long a job is allowed to run, or how many candidates a job is allowed to generate. * **MaxCandidates** *(integer) --* The maximum number of times a training job is allowed to run. For text and image classification, time-series forecasting, as well as text generation (LLMs fine- tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750. * **MaxRuntimePerTrainingJobInSeconds** *(integer) --* The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action. For job V2s (jobs created by calling "CreateAutoMLJobV2"), this field controls the runtime of the job candidate. For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds). * **MaxAutoMLJobRuntimeInSeconds** *(integer) --* The maximum runtime, in seconds, an AutoML job has to complete. If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best- performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed. * **ModelDeployConfig** *(dict) --* Indicates whether the model was deployed automatically to an endpoint and the name of that endpoint if deployed automatically. * **AutoGenerateEndpointName** *(boolean) --* Set to "True" to automatically generate an endpoint name for a one-click Autopilot model deployment; set to "False" otherwise. The default value is "False". Note: If you set "AutoGenerateEndpointName" to "True", do not specify the "EndpointName"; otherwise a 400 error is thrown. * **EndpointName** *(string) --* Specifies the endpoint name to use for a one-click Autopilot model deployment if the endpoint name is not generated automatically. Note: Specify the "EndpointName" if and only if you set "AutoGenerateEndpointName" to "False"; otherwise a 400 error is thrown. * **ModelDeployResult** *(dict) --* Provides information about endpoint for the model deployment. * **EndpointName** *(string) --* The name of the endpoint to which the model has been deployed. Note: If model deployment fails, this field is omitted from the response. **Exceptions** * "SageMaker.Client.exceptions.ResourceNotFound" SageMaker / Client / delete_cluster_scheduler_config delete_cluster_scheduler_config ******************************* SageMaker.Client.delete_cluster_scheduler_config(**kwargs) Deletes the cluster policy of the cluster. See also: AWS API Documentation **Request Syntax** response = client.delete_cluster_scheduler_config( ClusterSchedulerConfigId='string' ) Parameters: **ClusterSchedulerConfigId** (*string*) -- **[REQUIRED]** ID of the cluster policy. Returns: None **Exceptions** * "SageMaker.Client.exceptions.ResourceNotFound" SageMaker / Client / delete_space delete_space ************ SageMaker.Client.delete_space(**kwargs) Used to delete a space. See also: AWS API Documentation **Request Syntax** response = client.delete_space( DomainId='string', SpaceName='string' ) Parameters: * **DomainId** (*string*) -- **[REQUIRED]** The ID of the associated domain. * **SpaceName** (*string*) -- **[REQUIRED]** The name of the space. Returns: None **Exceptions** * "SageMaker.Client.exceptions.ResourceNotFound" * "SageMaker.Client.exceptions.ResourceInUse" SageMaker / Client / describe_data_quality_job_definition describe_data_quality_job_definition ************************************ SageMaker.Client.describe_data_quality_job_definition(**kwargs) Gets the details of a data quality monitoring job definition. See also: AWS API Documentation **Request Syntax** response = client.describe_data_quality_job_definition( JobDefinitionName='string' ) Parameters: **JobDefinitionName** (*string*) -- **[REQUIRED]** The name of the data quality monitoring job definition to describe. Return type: dict Returns: **Response Syntax** { 'JobDefinitionArn': 'string', 'JobDefinitionName': 'string', 'CreationTime': datetime(2015, 1, 1), 'DataQualityBaselineConfig': { 'BaseliningJobName': 'string', 'ConstraintsResource': { 'S3Uri': 'string' }, 'StatisticsResource': { 'S3Uri': 'string' } }, 'DataQualityAppSpecification': { 'ImageUri': 'string', 'ContainerEntrypoint': [ 'string', ], 'ContainerArguments': [ 'string', ], 'RecordPreprocessorSourceUri': 'string', 'PostAnalyticsProcessorSourceUri': 'string', 'Environment': { 'string': 'string' } }, 'DataQualityJobInput': { 'EndpointInput': { 'EndpointName': 'string', 'LocalPath': 'string', 'S3InputMode': 'Pipe'|'File', 'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key', 'FeaturesAttribute': 'string', 'InferenceAttribute': 'string', 'ProbabilityAttribute': 'string', 'ProbabilityThresholdAttribute': 123.0, 'StartTimeOffset': 'string', 'EndTimeOffset': 'string', 'ExcludeFeaturesAttribute': 'string' }, 'BatchTransformInput': { 'DataCapturedDestinationS3Uri': 'string', 'DatasetFormat': { 'Csv': { 'Header': True|False }, 'Json': { 'Line': True|False }, 'Parquet': {} }, 'LocalPath': 'string', 'S3InputMode': 'Pipe'|'File', 'S3DataDistributionType': 'FullyReplicated'|'ShardedByS3Key', 'FeaturesAttribute': 'string', 'InferenceAttribute': 'string', 'ProbabilityAttribute': 'string', 'ProbabilityThresholdAttribute': 123.0, 'StartTimeOffset': 'string', 'EndTimeOffset': 'string', 'ExcludeFeaturesAttribute': 'string' } }, 'DataQualityJobOutputConfig': { 'MonitoringOutputs': [ { 'S3Output': { 'S3Uri': 'string', 'LocalPath': 'string', 'S3UploadMode': 'Continuous'|'EndOfJob' } }, ], 'KmsKeyId': 'string' }, 'JobResources': { 'ClusterConfig': { 'InstanceCount': 123, 'InstanceType': 'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.8xlarge'|'ml.r5.12xlarge'|'ml.r5.16xlarge'|'ml.r5.24xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.16xlarge'|'ml.g5.12xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.8xlarge'|'ml.r5d.12xlarge'|'ml.r5d.16xlarge'|'ml.r5d.24xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge', 'VolumeSizeInGB': 123, 'VolumeKmsKeyId': 'string' } }, 'NetworkConfig': { 'EnableInterContainerTrafficEncryption': True|False, 'EnableNetworkIsolation': True|False, 'VpcConfig': { 'SecurityGroupIds': [ 'string', ], 'Subnets': [ 'string', ] } }, 'RoleArn': 'string', 'StoppingCondition': { 'MaxRuntimeInSeconds': 123 } } **Response Structure** * *(dict) --* * **JobDefinitionArn** *(string) --* The Amazon Resource Name (ARN) of the data quality monitoring job definition. * **JobDefinitionName** *(string) --* The name of the data quality monitoring job definition. * **CreationTime** *(datetime) --* The time that the data quality monitoring job definition was created. * **DataQualityBaselineConfig** *(dict) --* The constraints and baselines for the data quality monitoring job definition. * **BaseliningJobName** *(string) --* The name of the job that performs baselining for the data quality monitoring job. * **ConstraintsResource** *(dict) --* The constraints resource for a monitoring job. * **S3Uri** *(string) --* The Amazon S3 URI for the constraints resource. * **StatisticsResource** *(dict) --* The statistics resource for a monitoring job. * **S3Uri** *(string) --* The Amazon S3 URI for the statistics resource. * **DataQualityAppSpecification** *(dict) --* Information about the container that runs the data quality monitoring job. * **ImageUri** *(string) --* The container image that the data quality monitoring job runs. * **ContainerEntrypoint** *(list) --* The entrypoint for a container used to run a monitoring job. * *(string) --* * **ContainerArguments** *(list) --* The arguments to send to the container that the monitoring job runs. * *(string) --* * **RecordPreprocessorSourceUri** *(string) --* An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flattened JSON so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers. * **PostAnalyticsProcessorSourceUri** *(string) --* An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers. * **Environment** *(dict) --* Sets the environment variables in the container that the monitoring job runs. * *(string) --* * *(string) --* * **DataQualityJobInput** *(dict) --* The list of inputs for the data quality monitoring job. Currently endpoints are supported. * **EndpointInput** *(dict) --* Input object for the endpoint * **EndpointName** *(string) --* An endpoint in customer's account which has enabled "DataCaptureConfig" enabled. * **LocalPath** *(string) --* Path to the filesystem where the endpoint data is available to the container. * **S3InputMode** *(string) --* Whether the "Pipe" or "File" is used as the input mode for transferring data for the monitoring job. "Pipe" mode is recommended for large datasets. "File" mode is useful for small files that fit in memory. Defaults to "File". * **S3DataDistributionType** *(string) --* Whether input data distributed in Amazon S3 is fully replicated or sharded by an Amazon S3 key. Defaults to "FullyReplicated" * **FeaturesAttribute** *(string) --* The attributes of the input data that are the input features. * **InferenceAttribute** *(string) --* The attribute of the input data that represents the ground truth label. * **ProbabilityAttribute** *(string) --* In a classification problem, the attribute that represents the class probability. * **ProbabilityThresholdAttribute** *(float) --* The threshold for the class probability to be evaluated as a positive result. * **StartTimeOffset** *(string) --* If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs. * **EndTimeOffset** *(string) --* If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs. * **ExcludeFeaturesAttribute** *(string) --* The attributes of the input data to exclude from the analysis. * **BatchTransformInput** *(dict) --* Input object for the batch transform job. * **DataCapturedDestinationS3Uri** *(string) --* The Amazon S3 location being used to capture the data. * **DatasetFormat** *(dict) --* The dataset format for your batch transform job. * **Csv** *(dict) --* The CSV dataset used in the monitoring job. * **Header** *(boolean) --* Indicates if the CSV data has a header. * **Json** *(dict) --* The JSON dataset used in the monitoring job * **Line** *(boolean) --* Indicates if the file should be read as a JSON object per line. * **Parquet** *(dict) --* The Parquet dataset used in the monitoring job * **LocalPath** *(string) --* Path to the filesystem where the batch transform data is available to the container. * **S3InputMode** *(string) --* Whether the "Pipe" or "File" is used as the input mode for transferring data for the monitoring job. "Pipe" mode is recommended for large datasets. "File" mode is useful for small files that fit in memory. Defaults to "File". * **S3DataDistributionType** *(string) --* Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to "FullyReplicated" * **FeaturesAttribute** *(string) --* The attributes of the input data that are the input features. * **InferenceAttribute** *(string) --* The attribute of the input data that represents the ground truth label. * **ProbabilityAttribute** *(string) --* In a classification problem, the attribute that represents the class probability. * **ProbabilityThresholdAttribute** *(float) --* The threshold for the class probability to be evaluated as a positive result. * **StartTimeOffset** *(string) --* If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs. * **EndTimeOffset** *(string) --* If specified, monitoring jobs subtract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs. * **ExcludeFeaturesAttribute** *(string) --* The attributes of the input data to exclude from the analysis. * **DataQualityJobOutputConfig** *(dict) --* The output configuration for monitoring jobs. * **MonitoringOutputs** *(list) --* Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded. * *(dict) --* The output object for a monitoring job. * **S3Output** *(dict) --* The Amazon S3 storage location where the results of a monitoring job are saved. * **S3Uri** *(string) --* A URI that identifies the Amazon S3 storage location where Amazon SageMaker AI saves the results of a monitoring job. * **LocalPath** *(string) --* The local path to the Amazon S3 storage location where Amazon SageMaker AI saves the results of a monitoring job. LocalPath is an absolute path for the output data. * **S3UploadMode** *(string) --* Whether to upload the results of the monitoring job continuously or after the job completes. * **KmsKeyId** *(string) --* The Key Management Service (KMS) key that Amazon SageMaker AI uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. * **JobResources** *(dict) --* Identifies the resources to deploy for a monitoring job. * **ClusterConfig** *(dict) --* The configuration for the cluster resources used to run the processing job. * **InstanceCount** *(integer) --* The number of ML compute instances to use in the model monitoring job. For distributed processing jobs, specify a value greater than 1. The default value is 1. * **InstanceType** *(string) --* The ML compute instance type for the processing job. * **VolumeSizeInGB** *(integer) --* The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario. * **VolumeKmsKeyId** *(string) --* The Key Management Service (KMS) key that Amazon SageMaker AI uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the model monitoring job. * **NetworkConfig** *(dict) --* The networking configuration for the data quality monitoring job. * **EnableInterContainerTrafficEncryption** *(boolean) --* Whether to encrypt all communications between the instances used for the monitoring jobs. Choose "True" to encrypt communications. Encryption provides greater security for distributed jobs, but the processing might take longer. * **EnableNetworkIsolation** *(boolean) --* Whether to allow inbound and outbound network calls to and from the containers used for the monitoring job. * **VpcConfig** *(dict) --* Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC. * **SecurityGroupIds** *(list) --* The VPC security group IDs, in the form "sg-xxxxxxxx". Specify the security groups for the VPC that is specified in the "Subnets" field. * *(string) --* * **Subnets** *(list) --* The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones. * *(string) --* * **RoleArn** *(string) --* The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker AI can assume to perform tasks on your behalf. * **StoppingCondition** *(dict) --* A time limit for how long the monitoring job is allowed to run before stopping. * **MaxRuntimeInSeconds** *(integer) --* The maximum runtime allowed in seconds. Note: The "MaxRuntimeInSeconds" cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds. **Exceptions** * "SageMaker.Client.exceptions.ResourceNotFound" SageMaker / Client / describe_image_version describe_image_version ********************** SageMaker.Client.describe_image_version(**kwargs) Describes a version of a SageMaker AI image. See also: AWS API Documentation **Request Syntax** response = client.describe_image_version( ImageName='string', Version=123, Alias='string' ) Parameters: * **ImageName** (*string*) -- **[REQUIRED]** The name of the image. * **Version** (*integer*) -- The version of the image. If not specified, the latest version is described. * **Alias** (*string*) -- The alias of the image version. Return type: dict Returns: **Response Syntax** { 'BaseImage': 'string', 'ContainerImage': 'string', 'CreationTime': datetime(2015, 1, 1), 'FailureReason': 'string', 'ImageArn': 'string', 'ImageVersionArn': 'string', 'ImageVersionStatus': 'CREATING'|'CREATED'|'CREATE_FAILED'|'DELETING'|'DELETE_FAILED', 'LastModifiedTime': datetime(2015, 1, 1), 'Version': 123, 'VendorGuidance': 'NOT_PROVIDED'|'STABLE'|'TO_BE_ARCHIVED'|'ARCHIVED', 'JobType': 'TRAINING'|'INFERENCE'|'NOTEBOOK_KERNEL', 'MLFramework': 'string', 'ProgrammingLang': 'string', 'Processor': 'CPU'|'GPU', 'Horovod': True|False, 'ReleaseNotes': 'string' } **Response Structure** * *(dict) --* * **BaseImage** *(string) --* The registry path of the container image on which this image version is based. * **ContainerImage** *(string) --* The registry path of the container image that contains this image version. * **CreationTime** *(datetime) --* When the version was created. * **FailureReason** *(string) --* When a create or delete operation fails, the reason for the failure. * **ImageArn** *(string) --* The ARN of the image the version is based on. * **ImageVersionArn** *(string) --* The ARN of the version. * **ImageVersionStatus** *(string) --* The status of the version. * **LastModifiedTime** *(datetime) --* When the version was last modified. * **Version** *(integer) --* The version number. * **VendorGuidance** *(string) --* The stability of the image version specified by the maintainer. * "NOT_PROVIDED": The maintainers did not provide a status for image version stability. * "STABLE": The image version is stable. * "TO_BE_ARCHIVED": The image version is set to be archived. Custom image versions that are set to be archived are automatically archived after three months. * "ARCHIVED": The image version is archived. Archived image versions are not searchable and are no longer actively supported. * **JobType** *(string) --* Indicates SageMaker AI job type compatibility. * "TRAINING": The image version is compatible with SageMaker AI training jobs. * "INFERENCE": The image version is compatible with SageMaker AI inference jobs. * "NOTEBOOK_KERNEL": The image version is compatible with SageMaker AI notebook kernels. * **MLFramework** *(string) --* The machine learning framework vended in the image version. * **ProgrammingLang** *(string) --* The supported programming language and its version. * **Processor** *(string) --* Indicates CPU or GPU compatibility. * "CPU": The image version is compatible with CPU. * "GPU": The image version is compatible with GPU. * **Horovod** *(boolean) --* Indicates Horovod compatibility. * **ReleaseNotes** *(string) --* The maintainer description of the image version. **Exceptions** * "SageMaker.Client.exceptions.ResourceNotFound" SageMaker / Client / delete_compute_quota delete_compute_quota ******************** SageMaker.Client.delete_compute_quota(**kwargs) Deletes the compute allocation from the cluster. See also: AWS API Documentation **Request Syntax** response = client.delete_compute_quota( ComputeQuotaId='string' ) Parameters: **ComputeQuotaId** (*string*) -- **[REQUIRED]** ID of the compute allocation definition. Returns: None **Exceptions** * "SageMaker.Client.exceptions.ResourceNotFound" SageMaker / Client / list_devices list_devices ************ SageMaker.Client.list_devices(**kwargs) A list of devices. See also: AWS API Documentation **Request Syntax** response = client.list_devices( NextToken='string', MaxResults=123, LatestHeartbeatAfter=datetime(2015, 1, 1), ModelName='string', DeviceFleetName='string' ) Parameters: * **NextToken** (*string*) -- The response from the last list when returning a list large enough to need tokening. * **MaxResults** (*integer*) -- Maximum number of results to select. * **LatestHeartbeatAfter** (*datetime*) -- Select fleets where the job was updated after X * **ModelName** (*string*) -- A filter that searches devices that contains this name in any of their models. * **DeviceFleetName** (*string*) -- Filter for fleets containing this name in their device fleet name. Return type: dict Returns: **Response Syntax** { 'DeviceSummaries': [ { 'DeviceName': 'string', 'DeviceArn': 'string', 'Description': 'string', 'DeviceFleetName': 'string', 'IotThingName': 'string', 'RegistrationTime': datetime(2015, 1, 1), 'LatestHeartbeat': datetime(2015, 1, 1), 'Models': [ { 'ModelName': 'string', 'ModelVersion': 'string' }, ], 'AgentVersion': 'string' }, ], 'NextToken': 'string' } **Response Structure** * *(dict) --* * **DeviceSummaries** *(list) --* Summary of devices. * *(dict) --* Summary of the device. * **DeviceName** *(string) --* The unique identifier of the device. * **DeviceArn** *(string) --* Amazon Resource Name (ARN) of the device. * **Description** *(string) --* A description of the device. * **DeviceFleetName** *(string) --* The name of the fleet the device belongs to. * **IotThingName** *(string) --* The Amazon Web Services Internet of Things (IoT) object thing name associated with the device.. * **RegistrationTime** *(datetime) --* The timestamp of the last registration or de- reregistration. * **LatestHeartbeat** *(datetime) --* The last heartbeat received from the device. * **Models** *(list) --* Models on the device. * *(dict) --* Summary of model on edge device. * **ModelName** *(string) --* The name of the model. * **ModelVersion** *(string) --* The version model. * **AgentVersion** *(string) --* Edge Manager agent version. * **NextToken** *(string) --* The response from the last list when returning a list large enough to need tokening. SageMaker / Client / create_model_package create_model_package ******************** SageMaker.Client.create_model_package(**kwargs) Creates a model package that you can use to create SageMaker models or list on Amazon Web Services Marketplace, or a versioned model that is part of a model group. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in SageMaker. To create a model package by specifying a Docker container that contains your inference code and the Amazon S3 location of your model artifacts, provide values for "InferenceSpecification". To create a model from an algorithm resource that you created or subscribed to in Amazon Web Services Marketplace, provide a value for "SourceAlgorithmSpecification". Note: There are two types of model packages: * Versioned - a model that is part of a model group in the model registry. * Unversioned - a model package that is not part of a model group. See also: AWS API Documentation **Request Syntax** response = client.create_model_package( ModelPackageName='string', ModelPackageGroupName='string', ModelPackageDescription='string', InferenceSpecification={ 'Containers': [ { 'ContainerHostname': 'string', 'Image': 'string', 'ImageDigest': 'string', 'ModelDataUrl': 'string', 'ModelDataSource': { 'S3DataSource': { 'S3Uri': 'string', 'S3DataType': 'S3Prefix'|'S3Object', 'CompressionType': 'None'|'Gzip', 'ModelAccessConfig': { 'AcceptEula': True|False }, 'HubAccessConfig': { 'HubContentArn': 'string' }, 'ManifestS3Uri': 'string', 'ETag': 'string', 'ManifestEtag': 'string' } }, 'ProductId': 'string', 'Environment': { 'string': 'string' }, 'ModelInput': { 'DataInputConfig': 'string' }, 'Framework': 'string', 'FrameworkVersion': 'string', 'NearestModelName': 'string', 'AdditionalS3DataSource': { 'S3DataType': 'S3Object'|'S3Prefix', 'S3Uri': 'string', 'CompressionType': 'None'|'Gzip', 'ETag': 'string' }, 'ModelDataETag': 'string' }, ], 'SupportedTransformInstanceTypes': [ 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge', ], 'SupportedRealtimeInferenceInstanceTypes': [ 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], 'SupportedContentTypes': [ 'string', ], 'SupportedResponseMIMETypes': [ 'string', ] }, ValidationSpecification={ 'ValidationRole': 'string', 'ValidationProfiles': [ { 'ProfileName': 'string', 'TransformJobDefinition': { 'MaxConcurrentTransforms': 123, 'MaxPayloadInMB': 123, 'BatchStrategy': 'MultiRecord'|'SingleRecord', 'Environment': { 'string': 'string' }, 'TransformInput': { 'DataSource': { 'S3DataSource': { 'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile'|'Converse', 'S3Uri': 'string' } }, 'ContentType': 'string', 'CompressionType': 'None'|'Gzip', 'SplitType': 'None'|'Line'|'RecordIO'|'TFRecord' }, 'TransformOutput': { 'S3OutputPath': 'string', 'Accept': 'string', 'AssembleWith': 'None'|'Line', 'KmsKeyId': 'string' }, 'TransformResources': { 'InstanceType': 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'InstanceCount': 123, 'VolumeKmsKeyId': 'string', 'TransformAmiVersion': 'string' } } }, ] }, SourceAlgorithmSpecification={ 'SourceAlgorithms': [ { 'ModelDataUrl': 'string', 'ModelDataSource': { 'S3DataSource': { 'S3Uri': 'string', 'S3DataType': 'S3Prefix'|'S3Object', 'CompressionType': 'None'|'Gzip', 'ModelAccessConfig': { 'AcceptEula': True|False }, 'HubAccessConfig': { 'HubContentArn': 'string' }, 'ManifestS3Uri': 'string', 'ETag': 'string', 'ManifestEtag': 'string' } }, 'ModelDataETag': 'string', 'AlgorithmName': 'string' }, ] }, CertifyForMarketplace=True|False, Tags=[ { 'Key': 'string', 'Value': 'string' }, ], ModelApprovalStatus='Approved'|'Rejected'|'PendingManualApproval', MetadataProperties={ 'CommitId': 'string', 'Repository': 'string', 'GeneratedBy': 'string', 'ProjectId': 'string' }, ModelMetrics={ 'ModelQuality': { 'Statistics': { 'ContentType': 'string', 'ContentDigest': 'string', 'S3Uri': 'string' }, 'Constraints': { 'ContentType': 'string', 'ContentDigest': 'string', 'S3Uri': 'string' } }, 'ModelDataQuality': { 'Statistics': { 'ContentType': 'string', 'ContentDigest': 'string', 'S3Uri': 'string' }, 'Constraints': { 'ContentType': 'string', 'ContentDigest': 'string', 'S3Uri': 'string' } }, 'Bias': { 'Report': { 'ContentType': 'string', 'ContentDigest': 'string', 'S3Uri': 'string' }, 'PreTrainingReport': { 'ContentType': 'string', 'ContentDigest': 'string', 'S3Uri': 'string' }, 'PostTrainingReport': { 'ContentType': 'string', 'ContentDigest': 'string', 'S3Uri': 'string' } }, 'Explainability': { 'Report': { 'ContentType': 'string', 'ContentDigest': 'string', 'S3Uri': 'string' } } }, ClientToken='string', Domain='string', Task='string', SamplePayloadUrl='string', CustomerMetadataProperties={ 'string': 'string' }, DriftCheckBaselines={ 'Bias': { 'ConfigFile': { 'ContentType': 'string', 'ContentDigest': 'string', 'S3Uri': 'string' }, 'PreTrainingConstraints': { 'ContentType': 'string', 'ContentDigest': 'string', 'S3Uri': 'string' }, 'PostTrainingConstraints': { 'ContentType': 'string', 'ContentDigest': 'string', 'S3Uri': 'string' } }, 'Explainability': { 'Constraints': { 'ContentType': 'string', 'ContentDigest': 'string', 'S3Uri': 'string' }, 'ConfigFile': { 'ContentType': 'string', 'ContentDigest': 'string', 'S3Uri': 'string' } }, 'ModelQuality': { 'Statistics': { 'ContentType': 'string', 'ContentDigest': 'string', 'S3Uri': 'string' }, 'Constraints': { 'ContentType': 'string', 'ContentDigest': 'string', 'S3Uri': 'string' } }, 'ModelDataQuality': { 'Statistics': { 'ContentType': 'string', 'ContentDigest': 'string', 'S3Uri': 'string' }, 'Constraints': { 'ContentType': 'string', 'ContentDigest': 'string', 'S3Uri': 'string' } } }, AdditionalInferenceSpecifications=[ { 'Name': 'string', 'Description': 'string', 'Containers': [ { 'ContainerHostname': 'string', 'Image': 'string', 'ImageDigest': 'string', 'ModelDataUrl': 'string', 'ModelDataSource': { 'S3DataSource': { 'S3Uri': 'string', 'S3DataType': 'S3Prefix'|'S3Object', 'CompressionType': 'None'|'Gzip', 'ModelAccessConfig': { 'AcceptEula': True|False }, 'HubAccessConfig': { 'HubContentArn': 'string' }, 'ManifestS3Uri': 'string', 'ETag': 'string', 'ManifestEtag': 'string' } }, 'ProductId': 'string', 'Environment': { 'string': 'string' }, 'ModelInput': { 'DataInputConfig': 'string' }, 'Framework': 'string', 'FrameworkVersion': 'string', 'NearestModelName': 'string', 'AdditionalS3DataSource': { 'S3DataType': 'S3Object'|'S3Prefix', 'S3Uri': 'string', 'CompressionType': 'None'|'Gzip', 'ETag': 'string' }, 'ModelDataETag': 'string' }, ], 'SupportedTransformInstanceTypes': [ 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], 'SupportedRealtimeInferenceInstanceTypes': [ 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], 'SupportedContentTypes': [ 'string', ], 'SupportedResponseMIMETypes': [ 'string', ] }, ], SkipModelValidation='All'|'None', SourceUri='string', SecurityConfig={ 'KmsKeyId': 'string' }, ModelCard={ 'ModelCardContent': 'string', 'ModelCardStatus': 'Draft'|'PendingReview'|'Approved'|'Archived' }, ModelLifeCycle={ 'Stage': 'string', 'StageStatus': 'string', 'StageDescription': 'string' } ) Parameters: * **ModelPackageName** (*string*) -- The name of the model package. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen). This parameter is required for unversioned models. It is not applicable to versioned models. * **ModelPackageGroupName** (*string*) -- The name or Amazon Resource Name (ARN) of the model package group that this model version belongs to. This parameter is required for versioned models, and does not apply to unversioned models. * **ModelPackageDescription** (*string*) -- A description of the model package. * **InferenceSpecification** (*dict*) -- Specifies details about inference jobs that you can run with models based on this model package, including the following information: * The Amazon ECR paths of containers that contain the inference code and model artifacts. * The instance types that the model package supports for transform jobs and real-time endpoints used for inference. * The input and output content formats that the model package supports for inference. * **Containers** *(list) --* **[REQUIRED]** The Amazon ECR registry path of the Docker image that contains the inference code. * *(dict) --* Describes the Docker container for the model package. * **ContainerHostname** *(string) --* The DNS host name for the Docker container. * **Image** *(string) --* **[REQUIRED]** The Amazon Elastic Container Registry (Amazon ECR) path where inference code is stored. If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both "registry/repository[:tag]" and "registry/repository[@digest]" image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker. * **ImageDigest** *(string) --* An MD5 hash of the training algorithm that identifies the Docker image used for training. * **ModelDataUrl** *(string) --* The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single "gzip" compressed tar archive ( ".tar.gz" suffix). Note: The model artifacts must be in an S3 bucket that is in the same region as the model package. * **ModelDataSource** *(dict) --* Specifies the location of ML model data to deploy during endpoint creation. * **S3DataSource** *(dict) --* Specifies the S3 location of ML model data to deploy. * **S3Uri** *(string) --* **[REQUIRED]** Specifies the S3 path of ML model data to deploy. * **S3DataType** *(string) --* **[REQUIRED]** Specifies the type of ML model data to deploy. If you choose "S3Prefix", "S3Uri" identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by "S3Uri" always ends with a forward slash (/). If you choose "S3Object", "S3Uri" identifies an object that is the ML model data to deploy. * **CompressionType** *(string) --* **[REQUIRED]** Specifies how the ML model data is prepared. If you choose "Gzip" and choose "S3Object" as the value of "S3DataType", "S3Uri" identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment. If you choose "None" and chooose "S3Object" as the value of "S3DataType", "S3Uri" identifies an object that represents an uncompressed ML model to deploy. If you choose None and choose "S3Prefix" as the value of "S3DataType", "S3Uri" identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy. If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code: * If you choose "S3Object" as the value of "S3DataType", then SageMaker will split the key of the S3 object referenced by "S3Uri" by slash (/), and use the last part as the filename of the file holding the content of the S3 object. * If you choose "S3Prefix" as the value of "S3DataType", then for each S3 object under the key name pefix referenced by "S3Uri", SageMaker will trim its key by the prefix, and use the remainder as the path (relative to "/opt/ml/model") of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object. * Do not use any of the following as file names or directory names: * An empty or blank string * A string which contains null bytes * A string longer than 255 bytes * A single dot ( ".") * A double dot ( "..") * Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects "s3://mybucket/model/weights" and "s3://mybucket/model/weights/part1" and you specify "s3://mybucket/model/" as the value of "S3Uri" and "S3Prefix" as the value of "S3DataType", then it will result in name clash between "/opt/ml/model/weights" (a regular file) and "/opt/ml/model/weights/" (a directory). * Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure. * **ModelAccessConfig** *(dict) --* Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the "ModelAccessConfig". You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model. * **AcceptEula** *(boolean) --* **[REQUIRED]** Specifies agreement to the model end-user license agreement (EULA). The "AcceptEula" value must be explicitly defined as "True" in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model. * **HubAccessConfig** *(dict) --* Configuration information for hub access. * **HubContentArn** *(string) --* **[REQUIRED]** The ARN of the hub content for which deployment access is allowed. * **ManifestS3Uri** *(string) --* The Amazon S3 URI of the manifest file. The manifest file is a CSV file that stores the artifact locations. * **ETag** *(string) --* The ETag associated with S3 URI. * **ManifestEtag** *(string) --* The ETag associated with Manifest S3 URI. * **ProductId** *(string) --* The Amazon Web Services Marketplace product ID of the model package. * **Environment** *(dict) --* The environment variables to set in the Docker container. Each key and value in the "Environment" string to string map can have length of up to 1024. We support up to 16 entries in the map. * *(string) --* * *(string) --* * **ModelInput** *(dict) --* A structure with Model Input details. * **DataInputConfig** *(string) --* **[REQUIRED]** The input configuration object for the model. * **Framework** *(string) --* The machine learning framework of the model package container image. * **FrameworkVersion** *(string) --* The framework version of the Model Package Container Image. * **NearestModelName** *(string) --* The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling "ListModelMetadata". * **AdditionalS3DataSource** *(dict) --* The additional data source that is used during inference in the Docker container for your model package. * **S3DataType** *(string) --* **[REQUIRED]** The data type of the additional data source that you specify for use in inference or training. * **S3Uri** *(string) --* **[REQUIRED]** The uniform resource identifier (URI) used to identify an additional data source used in inference or training. * **CompressionType** *(string) --* The type of compression used for an additional data source used in inference or training. Specify "None" if your additional data source is not compressed. * **ETag** *(string) --* The ETag associated with S3 URI. * **ModelDataETag** *(string) --* The ETag associated with Model Data URL. * **SupportedTransformInstanceTypes** *(list) --* A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed. This parameter is required for unversioned models, and optional for versioned models. * *(string) --* * **SupportedRealtimeInferenceInstanceTypes** *(list) --* A list of the instance types that are used to generate inferences in real-time. This parameter is required for unversioned models, and optional for versioned models. * *(string) --* * **SupportedContentTypes** *(list) --* The supported MIME types for the input data. * *(string) --* * **SupportedResponseMIMETypes** *(list) --* The supported MIME types for the output data. * *(string) --* * **ValidationSpecification** (*dict*) -- Specifies configurations for one or more transform jobs that SageMaker runs to test the model package. * **ValidationRole** *(string) --* **[REQUIRED]** The IAM roles to be used for the validation of the model package. * **ValidationProfiles** *(list) --* **[REQUIRED]** An array of "ModelPackageValidationProfile" objects, each of which specifies a batch transform job that SageMaker runs to validate your model package. * *(dict) --* Contains data, such as the inputs and targeted instance types that are used in the process of validating the model package. The data provided in the validation profile is made available to your buyers on Amazon Web Services Marketplace. * **ProfileName** *(string) --* **[REQUIRED]** The name of the profile for the model package. * **TransformJobDefinition** *(dict) --* **[REQUIRED]** The "TransformJobDefinition" object that describes the transform job used for the validation of the model package. * **MaxConcurrentTransforms** *(integer) --* The maximum number of parallel requests that can be sent to each instance in a transform job. The default value is 1. * **MaxPayloadInMB** *(integer) --* The maximum payload size allowed, in MB. A payload is the data portion of a record (without metadata). * **BatchStrategy** *(string) --* A string that determines the number of records included in a single mini-batch. "SingleRecord" means only one record is used per mini- batch. "MultiRecord" means a mini-batch is set to contain as many records that can fit within the "MaxPayloadInMB" limit. * **Environment** *(dict) --* The environment variables to set in the Docker container. We support up to 16 key and values entries in the map. * *(string) --* * *(string) --* * **TransformInput** *(dict) --* **[REQUIRED]** A description of the input source and the way the transform job consumes it. * **DataSource** *(dict) --* **[REQUIRED]** Describes the location of the channel data, which is, the S3 location of the input data that the model can consume. * **S3DataSource** *(dict) --* **[REQUIRED]** The S3 location of the data source that is associated with a channel. * **S3DataType** *(string) --* **[REQUIRED]** If you choose "S3Prefix", "S3Uri" identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform. If you choose "ManifestFile", "S3Uri" identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform. The following values are compatible: "ManifestFile", "S3Prefix" The following value is not compatible: "AugmentedManifestFile" * **S3Uri** *(string) --* **[REQUIRED]** Depending on the value specified for the "S3DataType", identifies either a key name prefix or a manifest. For example: * A key name prefix might look like this: "s3://bucketname/exampleprefix/". * A manifest might look like this: "s3://bucketname/example.manifest" The manifest is an S3 object which is a JSON file with the following format: "[ {"prefix": "s3://customer_bucket/some/prefix/"}," ""relative/path/to/custdata-1"," ""relative/path/custdata-2"," "..." ""relative/path/custdata-N"" "]" The preceding JSON matches the following "S3Uris": "s3://cu stomer_bucket/some/prefix/relative/path/to/cu stdata-1" "s3://customer_bucket/some/prefix/r elative/path/custdata-2" "..." "s3://customer _bucket/some/prefix/relative/path/custdata-N" The complete set of "S3Uris" in this manifest constitutes the input data for the channel for this datasource. The object that each "S3Uris" points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf. * **ContentType** *(string) --* The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job. * **CompressionType** *(string) --* If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is "None". * **SplitType** *(string) --* The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for "SplitType" is "None", which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to "Line" to split records on a newline character boundary. "SplitType" also supports a number of record-oriented binary data formats. Currently, the supported record formats are: * RecordIO * TFRecord When splitting is enabled, the size of a mini-batch depends on the values of the "BatchStrategy" and "MaxPayloadInMB" parameters. When the value of "BatchStrategy" is "MultiRecord", Amazon SageMaker sends the maximum number of records in each request, up to the "MaxPayloadInMB" limit. If the value of "BatchStrategy" is "SingleRecord", Amazon SageMaker sends individual records in each request. Note: Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of "BatchStrategy" is set to "SingleRecord". Padding is not removed if the value of "BatchStrategy" is set to "MultiRecord".For more information about "RecordIO", see Create a Dataset Using RecordIO in the MXNet documentation. For more information about "TFRecord", see Consuming TFRecord data in the TensorFlow documentation. * **TransformOutput** *(dict) --* **[REQUIRED]** Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job. * **S3OutputPath** *(string) --* **[REQUIRED]** The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example, "s3://bucket-name/key-name-prefix". For every S3 object used as input for the transform job, batch transform stores the transformed data with an . "out" suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored at "s3://bucket-name /input-name-prefix/dataset01/data.csv", batch transform stores the transformed data at "s3 ://bucket-name/output-name-prefix/input-name- prefix/data.csv.out". Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an . "out" file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation. * **Accept** *(string) --* The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job. * **AssembleWith** *(string) --* Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify "None". To add a newline character at the end of every transformed record, specify "Line". * **KmsKeyId** *(string) --* The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The "KmsKeyId" can be any of the following formats: * Key ID: "1234abcd-12ab-34cd-56ef-1234567890ab" * Key ARN: "arn:aws:kms:us-west-2:111122223333:key /1234abcd-12ab-34cd-56ef-1234567890ab" * Alias name: "alias/ExampleAlias" * Alias name ARN: "arn:aws:kms:us- west-2:111122223333:alias/ExampleAlias" If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS- Managed Encryption Keys in the *Amazon Simple Storage Service Developer Guide.* The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the *Amazon Web Services Key Management Service Developer Guide*. * **TransformResources** *(dict) --* **[REQUIRED]** Identifies the ML compute instances for the transform job. * **InstanceType** *(string) --* **[REQUIRED]** The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or >>``<//". * **RemoteAccess** *(string) --* A setting that enables or disables remote access for a SageMaker space. When enabled, this allows you to connect to the remote space from your local IDE. * **OwnershipSettings** *(dict) --* The collection of ownership settings for a space. * **OwnerUserProfileName** *(string) --* The user profile who is the owner of the space. * **SpaceSharingSettings** *(dict) --* The collection of space sharing settings for a space. * **SharingType** *(string) --* Specifies the sharing type of the space. * **SpaceDisplayName** *(string) --* The name of the space that appears in the Amazon SageMaker Studio UI. * **Url** *(string) --* Returns the URL of the space. If the space is created with Amazon Web Services IAM Identity Center (Successor to Amazon Web Services Single Sign-On) authentication, users can navigate to the URL after appending the respective redirect parameter for the application type to be federated through Amazon Web Services IAM Identity Center. The following application types are supported: * Studio Classic: "&redirect=JupyterServer" * JupyterLab: "&redirect=JupyterLab" * Code Editor, based on Code-OSS, Visual Studio Code - Open Source: "&redirect=CodeEditor" **Exceptions** * "SageMaker.Client.exceptions.ResourceNotFound" SageMaker / Client / describe_training_plan describe_training_plan ********************** SageMaker.Client.describe_training_plan(**kwargs) Retrieves detailed information about a specific training plan. See also: AWS API Documentation **Request Syntax** response = client.describe_training_plan( TrainingPlanName='string' ) Parameters: **TrainingPlanName** (*string*) -- **[REQUIRED]** The name of the training plan to describe. Return type: dict Returns: **Response Syntax** { 'TrainingPlanArn': 'string', 'TrainingPlanName': 'string', 'Status': 'Pending'|'Active'|'Scheduled'|'Expired'|'Failed', 'StatusMessage': 'string', 'DurationHours': 123, 'DurationMinutes': 123, 'StartTime': datetime(2015, 1, 1), 'EndTime': datetime(2015, 1, 1), 'UpfrontFee': 'string', 'CurrencyCode': 'string', 'TotalInstanceCount': 123, 'AvailableInstanceCount': 123, 'InUseInstanceCount': 123, 'UnhealthyInstanceCount': 123, 'AvailableSpareInstanceCount': 123, 'TotalUltraServerCount': 123, 'TargetResources': [ 'training-job'|'hyperpod-cluster', ], 'ReservedCapacitySummaries': [ { 'ReservedCapacityArn': 'string', 'ReservedCapacityType': 'UltraServer'|'Instance', 'UltraServerType': 'string', 'UltraServerCount': 123, 'InstanceType': 'ml.p4d.24xlarge'|'ml.p5.48xlarge'|'ml.p5e.48xlarge'|'ml.p5en.48xlarge'|'ml.trn1.32xlarge'|'ml.trn2.48xlarge'|'ml.p6-b200.48xlarge'|'ml.p4de.24xlarge'|'ml.p6e-gb200.36xlarge', 'TotalInstanceCount': 123, 'Status': 'Pending'|'Active'|'Scheduled'|'Expired'|'Failed', 'AvailabilityZone': 'string', 'DurationHours': 123, 'DurationMinutes': 123, 'StartTime': datetime(2015, 1, 1), 'EndTime': datetime(2015, 1, 1) }, ] } **Response Structure** * *(dict) --* * **TrainingPlanArn** *(string) --* The Amazon Resource Name (ARN); of the training plan. * **TrainingPlanName** *(string) --* The name of the training plan. * **Status** *(string) --* The current status of the training plan (e.g., Pending, Active, Expired). To see the complete list of status values available for a training plan, refer to the "Status" attribute within the "TrainingPlanSummary" object. * **StatusMessage** *(string) --* A message providing additional information about the current status of the training plan. * **DurationHours** *(integer) --* The number of whole hours in the total duration for this training plan. * **DurationMinutes** *(integer) --* The additional minutes beyond whole hours in the total duration for this training plan. * **StartTime** *(datetime) --* The start time of the training plan. * **EndTime** *(datetime) --* The end time of the training plan. * **UpfrontFee** *(string) --* The upfront fee for the training plan. * **CurrencyCode** *(string) --* The currency code for the upfront fee (e.g., USD). * **TotalInstanceCount** *(integer) --* The total number of instances reserved in this training plan. * **AvailableInstanceCount** *(integer) --* The number of instances currently available for use in this training plan. * **InUseInstanceCount** *(integer) --* The number of instances currently in use from this training plan. * **UnhealthyInstanceCount** *(integer) --* The number of instances in the training plan that are currently in an unhealthy state. * **AvailableSpareInstanceCount** *(integer) --* The number of available spare instances in the training plan. * **TotalUltraServerCount** *(integer) --* The total number of UltraServers reserved to this training plan. * **TargetResources** *(list) --* The target resources (e.g., SageMaker Training Jobs, SageMaker HyperPod) that can use this training plan. Training plans are specific to their target resource. * A training plan designed for SageMaker training jobs can only be used to schedule and run training jobs. * A training plan for HyperPod clusters can be used exclusively to provide compute resources to a cluster's instance group. * *(string) --* * **ReservedCapacitySummaries** *(list) --* The list of Reserved Capacity providing the underlying compute resources of the plan. * *(dict) --* Details of a reserved capacity for the training plan. For more information about how to reserve GPU capacity for your SageMaker HyperPod clusters using Amazon SageMaker Training Plan, see >>``<>``<<. * **ReservedCapacityArn** *(string) --* The Amazon Resource Name (ARN); of the reserved capacity. * **ReservedCapacityType** *(string) --* The type of reserved capacity. * **UltraServerType** *(string) --* The type of UltraServer included in this reserved capacity, such as ml.u-p6e-gb200x72. * **UltraServerCount** *(integer) --* The number of UltraServers included in this reserved capacity. * **InstanceType** *(string) --* The instance type for the reserved capacity. * **TotalInstanceCount** *(integer) --* The total number of instances in the reserved capacity. * **Status** *(string) --* The current status of the reserved capacity. * **AvailabilityZone** *(string) --* The availability zone for the reserved capacity. * **DurationHours** *(integer) --* The number of whole hours in the total duration for this reserved capacity. * **DurationMinutes** *(integer) --* The additional minutes beyond whole hours in the total duration for this reserved capacity. * **StartTime** *(datetime) --* The start time of the reserved capacity. * **EndTime** *(datetime) --* The end time of the reserved capacity. **Exceptions** * "SageMaker.Client.exceptions.ResourceNotFound" SageMaker / Client / batch_describe_model_package batch_describe_model_package **************************** SageMaker.Client.batch_describe_model_package(**kwargs) This action batch describes a list of versioned model packages See also: AWS API Documentation **Request Syntax** response = client.batch_describe_model_package( ModelPackageArnList=[ 'string', ] ) Parameters: **ModelPackageArnList** (*list*) -- **[REQUIRED]** The list of Amazon Resource Name (ARN) of the model package groups. * *(string) --* Return type: dict Returns: **Response Syntax** { 'ModelPackageSummaries': { 'string': { 'ModelPackageGroupName': 'string', 'ModelPackageVersion': 123, 'ModelPackageArn': 'string', 'ModelPackageDescription': 'string', 'CreationTime': datetime(2015, 1, 1), 'InferenceSpecification': { 'Containers': [ { 'ContainerHostname': 'string', 'Image': 'string', 'ImageDigest': 'string', 'ModelDataUrl': 'string', 'ModelDataSource': { 'S3DataSource': { 'S3Uri': 'string', 'S3DataType': 'S3Prefix'|'S3Object', 'CompressionType': 'None'|'Gzip', 'ModelAccessConfig': { 'AcceptEula': True|False }, 'HubAccessConfig': { 'HubContentArn': 'string' }, 'ManifestS3Uri': 'string', 'ETag': 'string', 'ManifestEtag': 'string' } }, 'ProductId': 'string', 'Environment': { 'string': 'string' }, 'ModelInput': { 'DataInputConfig': 'string' }, 'Framework': 'string', 'FrameworkVersion': 'string', 'NearestModelName': 'string', 'AdditionalS3DataSource': { 'S3DataType': 'S3Object'|'S3Prefix', 'S3Uri': 'string', 'CompressionType': 'None'|'Gzip', 'ETag': 'string' }, 'ModelDataETag': 'string' }, ], 'SupportedTransformInstanceTypes': [ 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge', ], 'SupportedRealtimeInferenceInstanceTypes': [ 'ml.t2.medium'|'ml.t2.large'|'ml.t2.xlarge'|'ml.t2.2xlarge'|'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m5d.large'|'ml.m5d.xlarge'|'ml.m5d.2xlarge'|'ml.m5d.4xlarge'|'ml.m5d.12xlarge'|'ml.m5d.24xlarge'|'ml.c4.large'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.c5d.large'|'ml.c5d.xlarge'|'ml.c5d.2xlarge'|'ml.c5d.4xlarge'|'ml.c5d.9xlarge'|'ml.c5d.18xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.r5.large'|'ml.r5.xlarge'|'ml.r5.2xlarge'|'ml.r5.4xlarge'|'ml.r5.12xlarge'|'ml.r5.24xlarge'|'ml.r5d.large'|'ml.r5d.xlarge'|'ml.r5d.2xlarge'|'ml.r5d.4xlarge'|'ml.r5d.12xlarge'|'ml.r5d.24xlarge'|'ml.inf1.xlarge'|'ml.inf1.2xlarge'|'ml.inf1.6xlarge'|'ml.inf1.24xlarge'|'ml.dl1.24xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.g6.xlarge'|'ml.g6.2xlarge'|'ml.g6.4xlarge'|'ml.g6.8xlarge'|'ml.g6.12xlarge'|'ml.g6.16xlarge'|'ml.g6.24xlarge'|'ml.g6.48xlarge'|'ml.r8g.medium'|'ml.r8g.large'|'ml.r8g.xlarge'|'ml.r8g.2xlarge'|'ml.r8g.4xlarge'|'ml.r8g.8xlarge'|'ml.r8g.12xlarge'|'ml.r8g.16xlarge'|'ml.r8g.24xlarge'|'ml.r8g.48xlarge'|'ml.g6e.xlarge'|'ml.g6e.2xlarge'|'ml.g6e.4xlarge'|'ml.g6e.8xlarge'|'ml.g6e.12xlarge'|'ml.g6e.16xlarge'|'ml.g6e.24xlarge'|'ml.g6e.48xlarge'|'ml.p4d.24xlarge'|'ml.c7g.large'|'ml.c7g.xlarge'|'ml.c7g.2xlarge'|'ml.c7g.4xlarge'|'ml.c7g.8xlarge'|'ml.c7g.12xlarge'|'ml.c7g.16xlarge'|'ml.m6g.large'|'ml.m6g.xlarge'|'ml.m6g.2xlarge'|'ml.m6g.4xlarge'|'ml.m6g.8xlarge'|'ml.m6g.12xlarge'|'ml.m6g.16xlarge'|'ml.m6gd.large'|'ml.m6gd.xlarge'|'ml.m6gd.2xlarge'|'ml.m6gd.4xlarge'|'ml.m6gd.8xlarge'|'ml.m6gd.12xlarge'|'ml.m6gd.16xlarge'|'ml.c6g.large'|'ml.c6g.xlarge'|'ml.c6g.2xlarge'|'ml.c6g.4xlarge'|'ml.c6g.8xlarge'|'ml.c6g.12xlarge'|'ml.c6g.16xlarge'|'ml.c6gd.large'|'ml.c6gd.xlarge'|'ml.c6gd.2xlarge'|'ml.c6gd.4xlarge'|'ml.c6gd.8xlarge'|'ml.c6gd.12xlarge'|'ml.c6gd.16xlarge'|'ml.c6gn.large'|'ml.c6gn.xlarge'|'ml.c6gn.2xlarge'|'ml.c6gn.4xlarge'|'ml.c6gn.8xlarge'|'ml.c6gn.12xlarge'|'ml.c6gn.16xlarge'|'ml.r6g.large'|'ml.r6g.xlarge'|'ml.r6g.2xlarge'|'ml.r6g.4xlarge'|'ml.r6g.8xlarge'|'ml.r6g.12xlarge'|'ml.r6g.16xlarge'|'ml.r6gd.large'|'ml.r6gd.xlarge'|'ml.r6gd.2xlarge'|'ml.r6gd.4xlarge'|'ml.r6gd.8xlarge'|'ml.r6gd.12xlarge'|'ml.r6gd.16xlarge'|'ml.p4de.24xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.trn2.48xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge'|'ml.p5.48xlarge'|'ml.p5e.48xlarge'|'ml.p5en.48xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.c8g.medium'|'ml.c8g.large'|'ml.c8g.xlarge'|'ml.c8g.2xlarge'|'ml.c8g.4xlarge'|'ml.c8g.8xlarge'|'ml.c8g.12xlarge'|'ml.c8g.16xlarge'|'ml.c8g.24xlarge'|'ml.c8g.48xlarge'|'ml.r7gd.medium'|'ml.r7gd.large'|'ml.r7gd.xlarge'|'ml.r7gd.2xlarge'|'ml.r7gd.4xlarge'|'ml.r7gd.8xlarge'|'ml.r7gd.12xlarge'|'ml.r7gd.16xlarge'|'ml.m8g.medium'|'ml.m8g.large'|'ml.m8g.xlarge'|'ml.m8g.2xlarge'|'ml.m8g.4xlarge'|'ml.m8g.8xlarge'|'ml.m8g.12xlarge'|'ml.m8g.16xlarge'|'ml.m8g.24xlarge'|'ml.m8g.48xlarge'|'ml.c6in.large'|'ml.c6in.xlarge'|'ml.c6in.2xlarge'|'ml.c6in.4xlarge'|'ml.c6in.8xlarge'|'ml.c6in.12xlarge'|'ml.c6in.16xlarge'|'ml.c6in.24xlarge'|'ml.c6in.32xlarge'|'ml.p6-b200.48xlarge'|'ml.p6e-gb200.36xlarge', ], 'SupportedContentTypes': [ 'string', ], 'SupportedResponseMIMETypes': [ 'string', ] }, 'ModelPackageStatus': 'Pending'|'InProgress'|'Completed'|'Failed'|'Deleting', 'ModelApprovalStatus': 'Approved'|'Rejected'|'PendingManualApproval' } }, 'BatchDescribeModelPackageErrorMap': { 'string': { 'ErrorCode': 'string', 'ErrorResponse': 'string' } } } **Response Structure** * *(dict) --* * **ModelPackageSummaries** *(dict) --* The summaries for the model package versions * *(string) --* * *(dict) --* Provides summary information about the model package. * **ModelPackageGroupName** *(string) --* The group name for the model package * **ModelPackageVersion** *(integer) --* The version number of a versioned model. * **ModelPackageArn** *(string) --* The Amazon Resource Name (ARN) of the model package. * **ModelPackageDescription** *(string) --* The description of the model package. * **CreationTime** *(datetime) --* The creation time of the mortgage package summary. * **InferenceSpecification** *(dict) --* Defines how to perform inference generation after a training job is run. * **Containers** *(list) --* The Amazon ECR registry path of the Docker image that contains the inference code. * *(dict) --* Describes the Docker container for the model package. * **ContainerHostname** *(string) --* The DNS host name for the Docker container. * **Image** *(string) --* The Amazon Elastic Container Registry (Amazon ECR) path where inference code is stored. If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both "registry/repository[:tag]" and "registry/repository[@digest]" image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker. * **ImageDigest** *(string) --* An MD5 hash of the training algorithm that identifies the Docker image used for training. * **ModelDataUrl** *(string) --* The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single "gzip" compressed tar archive ( ".tar.gz" suffix). Note: The model artifacts must be in an S3 bucket that is in the same region as the model package. * **ModelDataSource** *(dict) --* Specifies the location of ML model data to deploy during endpoint creation. * **S3DataSource** *(dict) --* Specifies the S3 location of ML model data to deploy. * **S3Uri** *(string) --* Specifies the S3 path of ML model data to deploy. * **S3DataType** *(string) --* Specifies the type of ML model data to deploy. If you choose "S3Prefix", "S3Uri" identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by "S3Uri" always ends with a forward slash (/). If you choose "S3Object", "S3Uri" identifies an object that is the ML model data to deploy. * **CompressionType** *(string) --* Specifies how the ML model data is prepared. If you choose "Gzip" and choose "S3Object" as the value of "S3DataType", "S3Uri" identifies an object that is a gzip- compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment. If you choose "None" and chooose "S3Object" as the value of "S3DataType", "S3Uri" identifies an object that represents an uncompressed ML model to deploy. If you choose None and choose "S3Prefix" as the value of "S3DataType", "S3Uri" identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy. If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code: * If you choose "S3Object" as the value of "S3DataType", then SageMaker will split the key of the S3 object referenced by "S3Uri" by slash (/), and use the last part as the filename of the file holding the content of the S3 object. * If you choose "S3Prefix" as the value of "S3DataType", then for each S3 object under the key name pefix referenced by "S3Uri", SageMaker will trim its key by the prefix, and use the remainder as the path (relative to "/opt/ml/model") of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object. * Do not use any of the following as file names or directory names: * An empty or blank string * A string which contains null bytes * A string longer than 255 bytes * A single dot ( ".") * A double dot ( "..") * Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects "s3://mybucket/model/weights" and "s3://mybucket/model/weights/part1" and you specify "s3://mybucket/model/" as the value of "S3Uri" and "S3Prefix" as the value of "S3DataType", then it will result in name clash between "/opt/ml/model/weights" (a regular file) and "/opt/ml/model/weights/" (a directory). * Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure. * **ModelAccessConfig** *(dict) --* Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the "ModelAccessConfig". You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model. * **AcceptEula** *(boolean) --* Specifies agreement to the model end-user license agreement (EULA). The "AcceptEula" value must be explicitly defined as "True" in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model. * **HubAccessConfig** *(dict) --* Configuration information for hub access. * **HubContentArn** *(string) --* The ARN of the hub content for which deployment access is allowed. * **ManifestS3Uri** *(string) --* The Amazon S3 URI of the manifest file. The manifest file is a CSV file that stores the artifact locations. * **ETag** *(string) --* The ETag associated with S3 URI. * **ManifestEtag** *(string) --* The ETag associated with Manifest S3 URI. * **ProductId** *(string) --* The Amazon Web Services Marketplace product ID of the model package. * **Environment** *(dict) --* The environment variables to set in the Docker container. Each key and value in the "Environment" string to string map can have length of up to 1024. We support up to 16 entries in the map. * *(string) --* * *(string) --* * **ModelInput** *(dict) --* A structure with Model Input details. * **DataInputConfig** *(string) --* The input configuration object for the model. * **Framework** *(string) --* The machine learning framework of the model package container image. * **FrameworkVersion** *(string) --* The framework version of the Model Package Container Image. * **NearestModelName** *(string) --* The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling "ListModelMetadata". * **AdditionalS3DataSource** *(dict) --* The additional data source that is used during inference in the Docker container for your model package. * **S3DataType** *(string) --* The data type of the additional data source that you specify for use in inference or training. * **S3Uri** *(string) --* The uniform resource identifier (URI) used to identify an additional data source used in inference or training. * **CompressionType** *(string) --* The type of compression used for an additional data source used in inference or training. Specify "None" if your additional data source is not compressed. * **ETag** *(string) --* The ETag associated with S3 URI. * **ModelDataETag** *(string) --* The ETag associated with Model Data URL. * **SupportedTransformInstanceTypes** *(list) --* A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed. This parameter is required for unversioned models, and optional for versioned models. * *(string) --* * **SupportedRealtimeInferenceInstanceTypes** *(list) --* A list of the instance types that are used to generate inferences in real-time. This parameter is required for unversioned models, and optional for versioned models. * *(string) --* * **SupportedContentTypes** *(list) --* The supported MIME types for the input data. * *(string) --* * **SupportedResponseMIMETypes** *(list) --* The supported MIME types for the output data. * *(string) --* * **ModelPackageStatus** *(string) --* The status of the mortgage package. * **ModelApprovalStatus** *(string) --* The approval status of the model. * **BatchDescribeModelPackageErrorMap** *(dict) --* A map of the resource and BatchDescribeModelPackageError objects reporting the error associated with describing the model package. * *(string) --* * *(dict) --* The error code and error description associated with the resource. * **ErrorCode** *(string) --* * **ErrorResponse** *(string) --* SageMaker / Client / create_transform_job create_transform_job ******************** SageMaker.Client.create_transform_job(**kwargs) Starts a transform job. A transform job uses a trained model to get inferences on a dataset and saves these results to an Amazon S3 location that you specify. To perform batch transformations, you create a transform job and use the data that you have readily available. In the request body, you provide the following: * "TransformJobName" - Identifies the transform job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account. * "ModelName" - Identifies the model to use. "ModelName" must be the name of an existing Amazon SageMaker model in the same Amazon Web Services Region and Amazon Web Services account. For information on creating a model, see CreateModel. * "TransformInput" - Describes the dataset to be transformed and the Amazon S3 location where it is stored. * "TransformOutput" - Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job. * "TransformResources" - Identifies the ML compute instances and AMI image versions for the transform job. For more information about how batch transformation works, see Batch Transform. See also: AWS API Documentation **Request Syntax** response = client.create_transform_job( TransformJobName='string', ModelName='string', MaxConcurrentTransforms=123, ModelClientConfig={ 'InvocationsTimeoutInSeconds': 123, 'InvocationsMaxRetries': 123 }, MaxPayloadInMB=123, BatchStrategy='MultiRecord'|'SingleRecord', Environment={ 'string': 'string' }, TransformInput={ 'DataSource': { 'S3DataSource': { 'S3DataType': 'ManifestFile'|'S3Prefix'|'AugmentedManifestFile'|'Converse', 'S3Uri': 'string' } }, 'ContentType': 'string', 'CompressionType': 'None'|'Gzip', 'SplitType': 'None'|'Line'|'RecordIO'|'TFRecord' }, TransformOutput={ 'S3OutputPath': 'string', 'Accept': 'string', 'AssembleWith': 'None'|'Line', 'KmsKeyId': 'string' }, DataCaptureConfig={ 'DestinationS3Uri': 'string', 'KmsKeyId': 'string', 'GenerateInferenceId': True|False }, TransformResources={ 'InstanceType': 'ml.m4.xlarge'|'ml.m4.2xlarge'|'ml.m4.4xlarge'|'ml.m4.10xlarge'|'ml.m4.16xlarge'|'ml.c4.xlarge'|'ml.c4.2xlarge'|'ml.c4.4xlarge'|'ml.c4.8xlarge'|'ml.p2.xlarge'|'ml.p2.8xlarge'|'ml.p2.16xlarge'|'ml.p3.2xlarge'|'ml.p3.8xlarge'|'ml.p3.16xlarge'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.12xlarge'|'ml.m5.24xlarge'|'ml.m6i.large'|'ml.m6i.xlarge'|'ml.m6i.2xlarge'|'ml.m6i.4xlarge'|'ml.m6i.8xlarge'|'ml.m6i.12xlarge'|'ml.m6i.16xlarge'|'ml.m6i.24xlarge'|'ml.m6i.32xlarge'|'ml.c6i.large'|'ml.c6i.xlarge'|'ml.c6i.2xlarge'|'ml.c6i.4xlarge'|'ml.c6i.8xlarge'|'ml.c6i.12xlarge'|'ml.c6i.16xlarge'|'ml.c6i.24xlarge'|'ml.c6i.32xlarge'|'ml.r6i.large'|'ml.r6i.xlarge'|'ml.r6i.2xlarge'|'ml.r6i.4xlarge'|'ml.r6i.8xlarge'|'ml.r6i.12xlarge'|'ml.r6i.16xlarge'|'ml.r6i.24xlarge'|'ml.r6i.32xlarge'|'ml.m7i.large'|'ml.m7i.xlarge'|'ml.m7i.2xlarge'|'ml.m7i.4xlarge'|'ml.m7i.8xlarge'|'ml.m7i.12xlarge'|'ml.m7i.16xlarge'|'ml.m7i.24xlarge'|'ml.m7i.48xlarge'|'ml.c7i.large'|'ml.c7i.xlarge'|'ml.c7i.2xlarge'|'ml.c7i.4xlarge'|'ml.c7i.8xlarge'|'ml.c7i.12xlarge'|'ml.c7i.16xlarge'|'ml.c7i.24xlarge'|'ml.c7i.48xlarge'|'ml.r7i.large'|'ml.r7i.xlarge'|'ml.r7i.2xlarge'|'ml.r7i.4xlarge'|'ml.r7i.8xlarge'|'ml.r7i.12xlarge'|'ml.r7i.16xlarge'|'ml.r7i.24xlarge'|'ml.r7i.48xlarge'|'ml.g4dn.xlarge'|'ml.g4dn.2xlarge'|'ml.g4dn.4xlarge'|'ml.g4dn.8xlarge'|'ml.g4dn.12xlarge'|'ml.g4dn.16xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.trn1.2xlarge'|'ml.trn1.32xlarge'|'ml.inf2.xlarge'|'ml.inf2.8xlarge'|'ml.inf2.24xlarge'|'ml.inf2.48xlarge', 'InstanceCount': 123, 'VolumeKmsKeyId': 'string', 'TransformAmiVersion': 'string' }, DataProcessing={ 'InputFilter': 'string', 'OutputFilter': 'string', 'JoinSource': 'Input'|'None' }, Tags=[ { 'Key': 'string', 'Value': 'string' }, ], ExperimentConfig={ 'ExperimentName': 'string', 'TrialName': 'string', 'TrialComponentDisplayName': 'string', 'RunName': 'string' } ) Parameters: * **TransformJobName** (*string*) -- **[REQUIRED]** The name of the transform job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account. * **ModelName** (*string*) -- **[REQUIRED]** The name of the model that you want to use for the transform job. "ModelName" must be the name of an existing Amazon SageMaker model within an Amazon Web Services Region in an Amazon Web Services account. * **MaxConcurrentTransforms** (*integer*) -- The maximum number of parallel requests that can be sent to each instance in a transform job. If "MaxConcurrentTransforms" is set to "0" or left unset, Amazon SageMaker checks the optional execution- parameters to determine the settings for your chosen algorithm. If the execution-parameters endpoint is not enabled, the default value is "1". For more information on execution-parameters, see How Containers Serve Requests. For built-in algorithms, you don't need to set a value for "MaxConcurrentTransforms". * **ModelClientConfig** (*dict*) -- Configures the timeout and maximum number of retries for processing a transform job invocation. * **InvocationsTimeoutInSeconds** *(integer) --* The timeout value in seconds for an invocation request. The default value is 600. * **InvocationsMaxRetries** *(integer) --* The maximum number of retries when invocation requests are failing. The default value is 3. * **MaxPayloadInMB** (*integer*) -- The maximum allowed size of the payload, in MB. A *payload* is the data portion of a record (without metadata). The value in "MaxPayloadInMB" must be greater than, or equal to, the size of a single record. To estimate the size of a record in MB, divide the size of your dataset by the number of records. To ensure that the records fit within the maximum payload size, we recommend using a slightly larger value. The default value is "6" MB. The value of "MaxPayloadInMB" cannot be greater than 100 MB. If you specify the "MaxConcurrentTransforms" parameter, the value of "(MaxConcurrentTransforms * MaxPayloadInMB)" also cannot exceed 100 MB. For cases where the payload might be arbitrarily large and is transmitted using HTTP chunked encoding, set the value to "0". This feature works only in supported algorithms. Currently, Amazon SageMaker built-in algorithms do not support HTTP chunked encoding. * **BatchStrategy** (*string*) -- Specifies the number of records to include in a mini-batch for an HTTP inference request. A *record* is a single unit of input data that inference can be made on. For example, a single line in a CSV file is a record. To enable the batch strategy, you must set the "SplitType" property to "Line", "RecordIO", or "TFRecord". To use only one record when making an HTTP invocation request to a container, set "BatchStrategy" to "SingleRecord" and "SplitType" to "Line". To fit as many records in a mini-batch as can fit within the "MaxPayloadInMB" limit, set "BatchStrategy" to "MultiRecord" and "SplitType" to "Line". * **Environment** (*dict*) -- The environment variables to set in the Docker container. Don't include any sensitive data in your environment variables. We support up to 16 key and values entries in the map. * *(string) --* * *(string) --* * **TransformInput** (*dict*) -- **[REQUIRED]** Describes the input source and the way the transform job consumes it. * **DataSource** *(dict) --* **[REQUIRED]** Describes the location of the channel data, which is, the S3 location of the input data that the model can consume. * **S3DataSource** *(dict) --* **[REQUIRED]** The S3 location of the data source that is associated with a channel. * **S3DataType** *(string) --* **[REQUIRED]** If you choose "S3Prefix", "S3Uri" identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform. If you choose "ManifestFile", "S3Uri" identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform. The following values are compatible: "ManifestFile", "S3Prefix" The following value is not compatible: "AugmentedManifestFile" * **S3Uri** *(string) --* **[REQUIRED]** Depending on the value specified for the "S3DataType", identifies either a key name prefix or a manifest. For example: * A key name prefix might look like this: "s3://bucketname/exampleprefix/". * A manifest might look like this: "s3://bucketname/example.manifest" The manifest is an S3 object which is a JSON file with the following format: "[ {"prefix": "s3://customer_bucket/some/prefix/"}," ""relative/path/to/custdata-1"," ""relative/path/custdata-2"," "..." ""relative/path/custdata-N"" "]" The preceding JSON matches the following "S3Uris": "s3://customer_bucket /some/prefix/relative/path/to/custdata-1" "s3://custo mer_bucket/some/prefix/relative/path/custdata-2" "..." "s3://customer_bucket/some/prefix/relative/path/custd ata-N" The complete set of "S3Uris" in this manifest constitutes the input data for the channel for this datasource. The object that each "S3Uris" points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf. * **ContentType** *(string) --* The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job. * **CompressionType** *(string) --* If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is "None". * **SplitType** *(string) --* The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for "SplitType" is "None", which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to "Line" to split records on a newline character boundary. "SplitType" also supports a number of record- oriented binary data formats. Currently, the supported record formats are: * RecordIO * TFRecord When splitting is enabled, the size of a mini-batch depends on the values of the "BatchStrategy" and "MaxPayloadInMB" parameters. When the value of "BatchStrategy" is "MultiRecord", Amazon SageMaker sends the maximum number of records in each request, up to the "MaxPayloadInMB" limit. If the value of "BatchStrategy" is "SingleRecord", Amazon SageMaker sends individual records in each request. Note: Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of "BatchStrategy" is set to "SingleRecord". Padding is not removed if the value of "BatchStrategy" is set to "MultiRecord".For more information about "RecordIO", see Create a Dataset Using RecordIO in the MXNet documentation. For more information about "TFRecord", see Consuming TFRecord data in the TensorFlow documentation. * **TransformOutput** (*dict*) -- **[REQUIRED]** Describes the results of the transform job. * **S3OutputPath** *(string) --* **[REQUIRED]** The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example, "s3://bucket- name/key-name-prefix". For every S3 object used as input for the transform job, batch transform stores the transformed data with an . "out" suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored at "s3 ://bucket-name/input-name-prefix/dataset01/data.csv", batch transform stores the transformed data at "s3://bucket-name /output-name-prefix/input-name-prefix/data.csv.out". Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an . "out" file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation. * **Accept** *(string) --* The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job. * **AssembleWith** *(string) --* Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify "None". To add a newline character at the end of every transformed record, specify "Line". * **KmsKeyId** *(string) --* The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The "KmsKeyId" can be any of the following formats: * Key ID: "1234abcd-12ab-34cd-56ef-1234567890ab" * Key ARN: "arn:aws:kms:us-west-2:111122223333:key/1234abcd- 12ab-34cd-56ef-1234567890ab" * Alias name: "alias/ExampleAlias" * Alias name ARN: "arn:aws:kms:us- west-2:111122223333:alias/ExampleAlias" If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the *Amazon Simple Storage Service Developer Guide.* The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the *Amazon Web Services Key Management Service Developer Guide*. * **DataCaptureConfig** (*dict*) -- Configuration to control how SageMaker captures inference data. * **DestinationS3Uri** *(string) --* **[REQUIRED]** The Amazon S3 location being used to capture the data. * **KmsKeyId** *(string) --* The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the batch transform job. The KmsKeyId can be any of the following formats: * Key ID: "1234abcd-12ab-34cd-56ef-1234567890ab" * Key ARN: "arn:aws:kms:us-west-2:111122223333:key/1234abcd- 12ab-34cd-56ef-1234567890ab" * Alias name: "alias/ExampleAlias" * Alias name ARN: "arn:aws:kms:us- west-2:111122223333:alias/ExampleAlias" * **GenerateInferenceId** *(boolean) --* Flag that indicates whether to append inference id to the output. * **TransformResources** (*dict*) -- **[REQUIRED]** Describes the resources, including ML instance types and ML instance count, to use for the transform job. * **InstanceType** *(string) --* **[REQUIRED]** The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or >>``<>``<