%% WARNING: DO NOT EDIT, AUTO-GENERATED CODE! %% See https://github.com/aws-beam/aws-codegen for more details. %% @doc Provides APIs for creating and managing Amazon SageMaker resources. %% %% Other Resources: %% %% -module(aws_sagemaker). -export([add_association/2, add_association/3, add_tags/2, add_tags/3, associate_trial_component/2, associate_trial_component/3, batch_describe_model_package/2, batch_describe_model_package/3, create_action/2, create_action/3, create_algorithm/2, create_algorithm/3, create_app/2, create_app/3, create_app_image_config/2, create_app_image_config/3, create_artifact/2, create_artifact/3, create_auto_ml_job/2, create_auto_ml_job/3, create_code_repository/2, create_code_repository/3, create_compilation_job/2, create_compilation_job/3, create_context/2, create_context/3, create_data_quality_job_definition/2, create_data_quality_job_definition/3, create_device_fleet/2, create_device_fleet/3, create_domain/2, create_domain/3, create_edge_packaging_job/2, create_edge_packaging_job/3, create_endpoint/2, create_endpoint/3, create_endpoint_config/2, create_endpoint_config/3, create_experiment/2, create_experiment/3, create_feature_group/2, create_feature_group/3, create_flow_definition/2, create_flow_definition/3, create_human_task_ui/2, create_human_task_ui/3, create_hyper_parameter_tuning_job/2, create_hyper_parameter_tuning_job/3, create_image/2, create_image/3, create_image_version/2, create_image_version/3, create_labeling_job/2, create_labeling_job/3, create_model/2, create_model/3, create_model_bias_job_definition/2, create_model_bias_job_definition/3, create_model_explainability_job_definition/2, create_model_explainability_job_definition/3, create_model_package/2, create_model_package/3, create_model_package_group/2, create_model_package_group/3, create_model_quality_job_definition/2, create_model_quality_job_definition/3, create_monitoring_schedule/2, create_monitoring_schedule/3, create_notebook_instance/2, create_notebook_instance/3, create_notebook_instance_lifecycle_config/2, create_notebook_instance_lifecycle_config/3, create_pipeline/2, create_pipeline/3, create_presigned_domain_url/2, create_presigned_domain_url/3, create_presigned_notebook_instance_url/2, create_presigned_notebook_instance_url/3, create_processing_job/2, create_processing_job/3, create_project/2, create_project/3, create_studio_lifecycle_config/2, create_studio_lifecycle_config/3, create_training_job/2, create_training_job/3, create_transform_job/2, create_transform_job/3, create_trial/2, create_trial/3, create_trial_component/2, create_trial_component/3, create_user_profile/2, create_user_profile/3, create_workforce/2, create_workforce/3, create_workteam/2, create_workteam/3, delete_action/2, delete_action/3, delete_algorithm/2, delete_algorithm/3, delete_app/2, delete_app/3, delete_app_image_config/2, delete_app_image_config/3, delete_artifact/2, delete_artifact/3, delete_association/2, delete_association/3, delete_code_repository/2, delete_code_repository/3, delete_context/2, delete_context/3, delete_data_quality_job_definition/2, delete_data_quality_job_definition/3, delete_device_fleet/2, delete_device_fleet/3, delete_domain/2, delete_domain/3, delete_endpoint/2, delete_endpoint/3, delete_endpoint_config/2, delete_endpoint_config/3, delete_experiment/2, delete_experiment/3, delete_feature_group/2, delete_feature_group/3, delete_flow_definition/2, delete_flow_definition/3, delete_human_task_ui/2, delete_human_task_ui/3, delete_image/2, delete_image/3, delete_image_version/2, delete_image_version/3, delete_model/2, delete_model/3, delete_model_bias_job_definition/2, delete_model_bias_job_definition/3, delete_model_explainability_job_definition/2, delete_model_explainability_job_definition/3, delete_model_package/2, delete_model_package/3, delete_model_package_group/2, delete_model_package_group/3, delete_model_package_group_policy/2, delete_model_package_group_policy/3, delete_model_quality_job_definition/2, delete_model_quality_job_definition/3, delete_monitoring_schedule/2, delete_monitoring_schedule/3, delete_notebook_instance/2, delete_notebook_instance/3, delete_notebook_instance_lifecycle_config/2, delete_notebook_instance_lifecycle_config/3, delete_pipeline/2, delete_pipeline/3, delete_project/2, delete_project/3, delete_studio_lifecycle_config/2, delete_studio_lifecycle_config/3, delete_tags/2, delete_tags/3, delete_trial/2, delete_trial/3, delete_trial_component/2, delete_trial_component/3, delete_user_profile/2, delete_user_profile/3, delete_workforce/2, delete_workforce/3, delete_workteam/2, delete_workteam/3, deregister_devices/2, deregister_devices/3, describe_action/2, describe_action/3, describe_algorithm/2, describe_algorithm/3, describe_app/2, describe_app/3, describe_app_image_config/2, describe_app_image_config/3, describe_artifact/2, describe_artifact/3, describe_auto_ml_job/2, describe_auto_ml_job/3, describe_code_repository/2, describe_code_repository/3, describe_compilation_job/2, describe_compilation_job/3, describe_context/2, describe_context/3, describe_data_quality_job_definition/2, describe_data_quality_job_definition/3, describe_device/2, describe_device/3, describe_device_fleet/2, describe_device_fleet/3, describe_domain/2, describe_domain/3, describe_edge_packaging_job/2, describe_edge_packaging_job/3, describe_endpoint/2, describe_endpoint/3, describe_endpoint_config/2, describe_endpoint_config/3, describe_experiment/2, describe_experiment/3, describe_feature_group/2, describe_feature_group/3, describe_flow_definition/2, describe_flow_definition/3, describe_human_task_ui/2, describe_human_task_ui/3, describe_hyper_parameter_tuning_job/2, describe_hyper_parameter_tuning_job/3, describe_image/2, describe_image/3, describe_image_version/2, describe_image_version/3, describe_labeling_job/2, describe_labeling_job/3, describe_model/2, describe_model/3, describe_model_bias_job_definition/2, describe_model_bias_job_definition/3, describe_model_explainability_job_definition/2, describe_model_explainability_job_definition/3, describe_model_package/2, describe_model_package/3, describe_model_package_group/2, describe_model_package_group/3, describe_model_quality_job_definition/2, describe_model_quality_job_definition/3, describe_monitoring_schedule/2, describe_monitoring_schedule/3, describe_notebook_instance/2, describe_notebook_instance/3, describe_notebook_instance_lifecycle_config/2, describe_notebook_instance_lifecycle_config/3, describe_pipeline/2, describe_pipeline/3, describe_pipeline_definition_for_execution/2, describe_pipeline_definition_for_execution/3, describe_pipeline_execution/2, describe_pipeline_execution/3, describe_processing_job/2, describe_processing_job/3, describe_project/2, describe_project/3, describe_studio_lifecycle_config/2, describe_studio_lifecycle_config/3, describe_subscribed_workteam/2, describe_subscribed_workteam/3, describe_training_job/2, describe_training_job/3, describe_transform_job/2, describe_transform_job/3, describe_trial/2, describe_trial/3, describe_trial_component/2, describe_trial_component/3, describe_user_profile/2, describe_user_profile/3, describe_workforce/2, describe_workforce/3, describe_workteam/2, describe_workteam/3, disable_sagemaker_servicecatalog_portfolio/2, disable_sagemaker_servicecatalog_portfolio/3, disassociate_trial_component/2, disassociate_trial_component/3, enable_sagemaker_servicecatalog_portfolio/2, enable_sagemaker_servicecatalog_portfolio/3, get_device_fleet_report/2, get_device_fleet_report/3, get_model_package_group_policy/2, get_model_package_group_policy/3, get_sagemaker_servicecatalog_portfolio_status/2, get_sagemaker_servicecatalog_portfolio_status/3, get_search_suggestions/2, get_search_suggestions/3, list_actions/2, list_actions/3, list_algorithms/2, list_algorithms/3, list_app_image_configs/2, list_app_image_configs/3, list_apps/2, list_apps/3, list_artifacts/2, list_artifacts/3, list_associations/2, list_associations/3, list_auto_ml_jobs/2, list_auto_ml_jobs/3, list_candidates_for_auto_ml_job/2, list_candidates_for_auto_ml_job/3, list_code_repositories/2, list_code_repositories/3, list_compilation_jobs/2, list_compilation_jobs/3, list_contexts/2, list_contexts/3, list_data_quality_job_definitions/2, list_data_quality_job_definitions/3, list_device_fleets/2, list_device_fleets/3, list_devices/2, list_devices/3, list_domains/2, list_domains/3, list_edge_packaging_jobs/2, list_edge_packaging_jobs/3, list_endpoint_configs/2, list_endpoint_configs/3, list_endpoints/2, list_endpoints/3, list_experiments/2, list_experiments/3, list_feature_groups/2, list_feature_groups/3, list_flow_definitions/2, list_flow_definitions/3, list_human_task_uis/2, list_human_task_uis/3, list_hyper_parameter_tuning_jobs/2, list_hyper_parameter_tuning_jobs/3, list_image_versions/2, list_image_versions/3, list_images/2, list_images/3, list_labeling_jobs/2, list_labeling_jobs/3, list_labeling_jobs_for_workteam/2, list_labeling_jobs_for_workteam/3, list_model_bias_job_definitions/2, list_model_bias_job_definitions/3, list_model_explainability_job_definitions/2, list_model_explainability_job_definitions/3, list_model_package_groups/2, list_model_package_groups/3, list_model_packages/2, list_model_packages/3, list_model_quality_job_definitions/2, list_model_quality_job_definitions/3, list_models/2, list_models/3, list_monitoring_executions/2, list_monitoring_executions/3, list_monitoring_schedules/2, list_monitoring_schedules/3, list_notebook_instance_lifecycle_configs/2, list_notebook_instance_lifecycle_configs/3, list_notebook_instances/2, list_notebook_instances/3, list_pipeline_execution_steps/2, list_pipeline_execution_steps/3, list_pipeline_executions/2, list_pipeline_executions/3, list_pipeline_parameters_for_execution/2, list_pipeline_parameters_for_execution/3, list_pipelines/2, list_pipelines/3, list_processing_jobs/2, list_processing_jobs/3, list_projects/2, list_projects/3, list_studio_lifecycle_configs/2, list_studio_lifecycle_configs/3, list_subscribed_workteams/2, list_subscribed_workteams/3, list_tags/2, list_tags/3, list_training_jobs/2, list_training_jobs/3, list_training_jobs_for_hyper_parameter_tuning_job/2, list_training_jobs_for_hyper_parameter_tuning_job/3, list_transform_jobs/2, list_transform_jobs/3, list_trial_components/2, list_trial_components/3, list_trials/2, list_trials/3, list_user_profiles/2, list_user_profiles/3, list_workforces/2, list_workforces/3, list_workteams/2, list_workteams/3, put_model_package_group_policy/2, put_model_package_group_policy/3, register_devices/2, register_devices/3, render_ui_template/2, render_ui_template/3, retry_pipeline_execution/2, retry_pipeline_execution/3, search/2, search/3, send_pipeline_execution_step_failure/2, send_pipeline_execution_step_failure/3, send_pipeline_execution_step_success/2, send_pipeline_execution_step_success/3, start_monitoring_schedule/2, start_monitoring_schedule/3, start_notebook_instance/2, start_notebook_instance/3, start_pipeline_execution/2, start_pipeline_execution/3, stop_auto_ml_job/2, stop_auto_ml_job/3, stop_compilation_job/2, stop_compilation_job/3, stop_edge_packaging_job/2, stop_edge_packaging_job/3, stop_hyper_parameter_tuning_job/2, stop_hyper_parameter_tuning_job/3, stop_labeling_job/2, stop_labeling_job/3, stop_monitoring_schedule/2, stop_monitoring_schedule/3, stop_notebook_instance/2, stop_notebook_instance/3, stop_pipeline_execution/2, stop_pipeline_execution/3, stop_processing_job/2, stop_processing_job/3, stop_training_job/2, stop_training_job/3, stop_transform_job/2, stop_transform_job/3, update_action/2, update_action/3, update_app_image_config/2, update_app_image_config/3, update_artifact/2, update_artifact/3, update_code_repository/2, update_code_repository/3, update_context/2, update_context/3, update_device_fleet/2, update_device_fleet/3, update_devices/2, update_devices/3, update_domain/2, update_domain/3, update_endpoint/2, update_endpoint/3, update_endpoint_weights_and_capacities/2, update_endpoint_weights_and_capacities/3, update_experiment/2, update_experiment/3, update_image/2, update_image/3, update_model_package/2, update_model_package/3, update_monitoring_schedule/2, update_monitoring_schedule/3, update_notebook_instance/2, update_notebook_instance/3, update_notebook_instance_lifecycle_config/2, update_notebook_instance_lifecycle_config/3, update_pipeline/2, update_pipeline/3, update_pipeline_execution/2, update_pipeline_execution/3, update_project/2, update_project/3, update_training_job/2, update_training_job/3, update_trial/2, update_trial/3, update_trial_component/2, update_trial_component/3, update_user_profile/2, update_user_profile/3, update_workforce/2, update_workforce/3, update_workteam/2, update_workteam/3]). -include_lib("hackney/include/hackney_lib.hrl"). %%==================================================================== %% API %%==================================================================== %% @doc 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. add_association(Client, Input) when is_map(Client), is_map(Input) -> add_association(Client, Input, []). add_association(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"AddAssociation">>, Input, Options). %% @doc Adds or overwrites one or more tags for the specified Amazon %% SageMaker resource. %% %% You can add tags to notebook instances, training jobs, hyperparameter %% tuning jobs, batch transform jobs, models, labeling jobs, work teams, %% endpoint configurations, and endpoints. %% %% Each tag consists of a key and an optional value. Tag keys must be unique %% per resource. For more information about tags, see For more information, %% see Amazon Web Services Tagging Strategies. %% %% Tags that you add to a hyperparameter tuning job by calling this API are %% also added to any training jobs that the hyperparameter tuning job %% launches after you call this API, but not to training jobs that the %% hyperparameter tuning job launched before you called this API. To make %% sure that the tags associated with a hyperparameter tuning job are also %% added to all training jobs that the hyperparameter tuning job launches, %% add the tags when you first create the tuning job by specifying them in %% the `Tags' parameter of `CreateHyperParameterTuningJob' %% %% Tags that you add to a SageMaker Studio Domain or User Profile by calling %% this API are also added to any Apps that the Domain or User Profile %% launches after you call this API, but not to Apps that the Domain or User %% Profile launched before you called this API. To make sure that the tags %% associated with a Domain or User Profile are also added to all Apps that %% the Domain or User Profile launches, add the tags when you first create %% the Domain or User Profile by specifying them in the `Tags' parameter of %% `CreateDomain' or `CreateUserProfile'. add_tags(Client, Input) when is_map(Client), is_map(Input) -> add_tags(Client, Input, []). add_tags(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"AddTags">>, Input, Options). %% @doc Associates a trial component with a trial. %% %% A trial component can be associated with multiple trials. To disassociate %% a trial component from a trial, call the `DisassociateTrialComponent' API. associate_trial_component(Client, Input) when is_map(Client), is_map(Input) -> associate_trial_component(Client, Input, []). associate_trial_component(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"AssociateTrialComponent">>, Input, Options). %% @doc This action batch describes a list of versioned model packages batch_describe_model_package(Client, Input) when is_map(Client), is_map(Input) -> batch_describe_model_package(Client, Input, []). batch_describe_model_package(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"BatchDescribeModelPackage">>, Input, Options). %% @doc Creates an action. %% %% An action is a lineage tracking entity that represents an action or %% activity. For example, a model deployment or an HPO job. Generally, an %% action involves at least one input or output artifact. For more %% information, see Amazon SageMaker ML Lineage Tracking. create_action(Client, Input) when is_map(Client), is_map(Input) -> create_action(Client, Input, []). create_action(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateAction">>, Input, Options). %% @doc Create a machine learning algorithm that you can use in Amazon %% SageMaker and list in the Amazon Web Services Marketplace. create_algorithm(Client, Input) when is_map(Client), is_map(Input) -> create_algorithm(Client, Input, []). create_algorithm(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateAlgorithm">>, Input, Options). %% @doc Creates a running app for the specified UserProfile. %% %% Supported apps are `JupyterServer' and `KernelGateway'. This operation is %% automatically invoked by Amazon SageMaker Studio upon access to the %% associated Domain, and when new kernel configurations are selected by the %% user. A user may have multiple Apps active simultaneously. create_app(Client, Input) when is_map(Client), is_map(Input) -> create_app(Client, Input, []). create_app(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateApp">>, Input, Options). %% @doc Creates a configuration for running a SageMaker image as a %% KernelGateway app. %% %% The configuration specifies the Amazon Elastic File System (EFS) storage %% volume on the image, and a list of the kernels in the image. create_app_image_config(Client, Input) when is_map(Client), is_map(Input) -> create_app_image_config(Client, Input, []). create_app_image_config(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateAppImageConfig">>, Input, Options). %% @doc Creates an artifact. %% %% An artifact is a lineage tracking entity that represents a URI addressable %% object or data. Some examples are the S3 URI of a dataset and the ECR %% registry path of an image. For more information, see Amazon SageMaker ML %% Lineage Tracking. create_artifact(Client, Input) when is_map(Client), is_map(Input) -> create_artifact(Client, Input, []). create_artifact(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateArtifact">>, Input, Options). %% @doc Creates an Autopilot job. %% %% Find the best-performing model after you run an Autopilot job by calling . %% %% For information about how to use Autopilot, see Automate Model Development %% with Amazon SageMaker Autopilot. create_auto_ml_job(Client, Input) when is_map(Client), is_map(Input) -> create_auto_ml_job(Client, Input, []). create_auto_ml_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateAutoMLJob">>, Input, Options). %% @doc Creates a Git repository as a resource in your Amazon SageMaker %% account. %% %% You can associate the repository with notebook instances so that you can %% use Git source control for the notebooks you create. The Git repository is %% a resource in your Amazon SageMaker account, so it can be associated with %% more than one notebook instance, and it persists independently from the %% lifecycle of any notebook instances it is associated with. %% %% The repository can be hosted either in Amazon Web Services CodeCommit or %% in any other Git repository. create_code_repository(Client, Input) when is_map(Client), is_map(Input) -> create_code_repository(Client, Input, []). create_code_repository(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateCodeRepository">>, Input, Options). %% @doc Starts a model compilation job. %% %% After the model has been compiled, Amazon SageMaker saves the resulting %% model artifacts to an Amazon Simple Storage Service (Amazon S3) bucket %% that you specify. %% %% If you choose to host your model using Amazon SageMaker hosting services, %% you can use the resulting model artifacts as part of the model. You can %% also use the artifacts with Amazon Web Services IoT Greengrass. In that %% case, deploy them as an ML resource. %% %% In the request body, you provide the following: %% %% You can also provide a `Tag' to track the model compilation %% job's resource use and costs. The response body contains the %% `CompilationJobArn' for the compiled job. %% %% To stop a model compilation job, use `StopCompilationJob'. To get %% information about a particular model compilation job, use %% `DescribeCompilationJob'. To get information about multiple model %% compilation jobs, use `ListCompilationJobs'. create_compilation_job(Client, Input) when is_map(Client), is_map(Input) -> create_compilation_job(Client, Input, []). create_compilation_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateCompilationJob">>, Input, Options). %% @doc Creates a context. %% %% A context is a lineage tracking entity that represents a logical grouping %% of other tracking or experiment entities. Some examples are an endpoint %% and a model package. For more information, see Amazon SageMaker ML Lineage %% Tracking. create_context(Client, Input) when is_map(Client), is_map(Input) -> create_context(Client, Input, []). create_context(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateContext">>, Input, Options). %% @doc Creates a definition for a job that monitors data quality and drift. %% %% For information about model monitor, see Amazon SageMaker Model Monitor. create_data_quality_job_definition(Client, Input) when is_map(Client), is_map(Input) -> create_data_quality_job_definition(Client, Input, []). create_data_quality_job_definition(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateDataQualityJobDefinition">>, Input, Options). %% @doc Creates a device fleet. create_device_fleet(Client, Input) when is_map(Client), is_map(Input) -> create_device_fleet(Client, Input, []). create_device_fleet(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateDeviceFleet">>, Input, Options). %% @doc Creates a `Domain' used by Amazon SageMaker Studio. %% %% A domain consists of an associated Amazon Elastic File System (EFS) %% volume, a list of authorized users, and a variety of security, %% application, policy, and Amazon Virtual Private Cloud (VPC) %% configurations. An Amazon Web Services account is limited to one domain %% per region. Users within a domain can share notebook files and other %% artifacts with each other. %% %% EFS storage %% %% When a domain is created, an EFS volume is created for use by all of the %% users within the domain. Each user receives a private home directory %% within the EFS volume for notebooks, Git repositories, and data files. %% %% SageMaker uses the Amazon Web Services Key Management Service (Amazon Web %% Services KMS) to encrypt the EFS volume attached to the domain with an %% Amazon Web Services managed key by default. For more control, you can %% specify a customer managed key. For more information, see Protect Data at %% Rest Using Encryption. %% %% VPC configuration %% %% All SageMaker Studio traffic between the domain and the EFS volume is %% through the specified VPC and subnets. For other Studio traffic, you can %% specify the `AppNetworkAccessType' parameter. `AppNetworkAccessType' %% corresponds to the network access type that you choose when you onboard to %% Studio. The following options are available: %% %% NFS traffic over TCP on port 2049 needs to be allowed in both %% inbound and outbound rules in order to launch a SageMaker Studio app %% successfully. %% %% For more information, see Connect SageMaker Studio Notebooks to Resources %% in a VPC. create_domain(Client, Input) when is_map(Client), is_map(Input) -> create_domain(Client, Input, []). create_domain(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateDomain">>, Input, Options). %% @doc Starts a SageMaker Edge Manager model packaging job. %% %% Edge Manager will use the model artifacts from the Amazon Simple Storage %% Service bucket that you specify. After the model has been packaged, Amazon %% SageMaker saves the resulting artifacts to an S3 bucket that you specify. create_edge_packaging_job(Client, Input) when is_map(Client), is_map(Input) -> create_edge_packaging_job(Client, Input, []). create_edge_packaging_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateEdgePackagingJob">>, Input, Options). %% @doc Creates an endpoint using the endpoint configuration specified in the %% request. %% %% Amazon SageMaker uses the endpoint to provision resources and deploy %% models. You create the endpoint configuration with the %% `CreateEndpointConfig' API. %% %% Use this API to deploy models using Amazon SageMaker hosting services. %% %% For an example that calls this method when deploying a model to Amazon %% SageMaker hosting services, see the Create Endpoint example notebook. %% %% You must not delete an `EndpointConfig' that is in use by an endpoint that %% is live or while the `UpdateEndpoint' or `CreateEndpoint' operations are %% being performed on the endpoint. To update an endpoint, you must create a %% new `EndpointConfig'. %% %% The endpoint name must be unique within an Amazon Web Services Region in %% your Amazon Web Services account. %% %% When it receives the request, Amazon SageMaker creates the endpoint, %% launches the resources (ML compute instances), and deploys the model(s) on %% them. %% %% When you call `CreateEndpoint', a load call is made to DynamoDB to verify %% that your endpoint configuration exists. When you read data from a %% DynamoDB table supporting `Eventually Consistent Reads' , the response %% might not reflect the results of a recently completed write operation. The %% response might include some stale data. If the dependent entities are not %% yet in DynamoDB, this causes a validation error. If you repeat your read %% request after a short time, the response should return the latest data. So %% retry logic is recommended to handle these possible issues. We also %% recommend that customers call `DescribeEndpointConfig' before calling %% `CreateEndpoint' to minimize the potential impact of a DynamoDB eventually %% consistent read. %% %% When Amazon SageMaker receives the request, it sets the endpoint status to %% `Creating'. After it creates the endpoint, it sets the status to %% `InService'. Amazon SageMaker can then process incoming requests for %% inferences. To check the status of an endpoint, use the `DescribeEndpoint' %% API. %% %% If any of the models hosted at this endpoint get model data from an Amazon %% S3 location, Amazon SageMaker uses Amazon Web Services Security Token %% Service to download model artifacts from the S3 path you provided. Amazon %% Web Services STS is activated in your IAM user 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. %% %% To add the IAM role policies for using this API operation, go to the IAM %% console, and choose Roles in the left navigation pane. Search the IAM role %% that you want to grant access to use the `CreateEndpoint' and %% `CreateEndpointConfig' API operations, add the following policies to the %% role. %% %% Option 1: For a full SageMaker access, search and attach the %% `AmazonSageMakerFullAccess' policy. %% %% Option 2: For granting a limited access to an IAM role, paste the %% following Action elements manually into the JSON file of the IAM role: %% %% `"Action": ["sagemaker:CreateEndpoint", "sagemaker:CreateEndpointConfig"]' %% %% `"Resource": [' %% %% `"arn:aws:sagemaker:region:account-id:endpoint/endpointName"' %% %% `"arn:aws:sagemaker:region:account-id:endpoint-config/endpointConfigName"' %% %% `]' %% %% For more information, see SageMaker API Permissions: Actions, Permissions, %% and Resources Reference. create_endpoint(Client, Input) when is_map(Client), is_map(Input) -> create_endpoint(Client, Input, []). create_endpoint(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateEndpoint">>, Input, Options). %% @doc Creates an endpoint configuration that Amazon SageMaker hosting %% services uses to deploy models. %% %% In the configuration, you identify one or more models, created using the %% `CreateModel' API, to deploy and the resources that you want Amazon %% SageMaker to provision. Then you call the `CreateEndpoint' API. %% %% Use this API if you want to use Amazon SageMaker hosting services to %% deploy models into production. %% %% In the request, you define a `ProductionVariant', for each model that you %% want to deploy. Each `ProductionVariant' parameter also describes the %% resources that you want Amazon SageMaker to provision. This includes the %% number and type of ML compute instances to deploy. %% %% If you are hosting multiple models, you also assign a `VariantWeight' to %% specify how much traffic you want to allocate to each model. For example, %% suppose that you want to host two models, A and B, and you assign traffic %% weight 2 for model A and 1 for model B. Amazon SageMaker distributes %% two-thirds of the traffic to Model A, and one-third to model B. %% %% When you call `CreateEndpoint', a load call is made to DynamoDB to verify %% that your endpoint configuration exists. When you read data from a %% DynamoDB table supporting `Eventually Consistent Reads' , the response %% might not reflect the results of a recently completed write operation. The %% response might include some stale data. If the dependent entities are not %% yet in DynamoDB, this causes a validation error. If you repeat your read %% request after a short time, the response should return the latest data. So %% retry logic is recommended to handle these possible issues. We also %% recommend that customers call `DescribeEndpointConfig' before calling %% `CreateEndpoint' to minimize the potential impact of a DynamoDB eventually %% consistent read. create_endpoint_config(Client, Input) when is_map(Client), is_map(Input) -> create_endpoint_config(Client, Input, []). create_endpoint_config(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateEndpointConfig">>, Input, Options). %% @doc Creates an SageMaker experiment. %% %% An experiment is a collection of trials that are observed, compared and %% evaluated as a group. A trial is a set of steps, called trial components, %% that produce a machine learning model. %% %% The goal of an experiment is to determine the components that produce the %% best model. Multiple trials are performed, each one isolating and %% measuring the impact of a change to one or more inputs, while keeping the %% remaining inputs constant. %% %% When you use SageMaker Studio or the SageMaker Python SDK, all %% experiments, trials, and trial components are automatically tracked, %% logged, and indexed. When you use the Amazon Web Services SDK for Python %% (Boto), you must use the logging APIs provided by the SDK. %% %% You can add tags to experiments, trials, trial components and then use the %% `Search' API to search for the tags. %% %% To add a description to an experiment, specify the optional `Description' %% parameter. To add a description later, or to change the description, call %% the `UpdateExperiment' API. %% %% To get a list of all your experiments, call the `ListExperiments' API. To %% view an experiment's properties, call the `DescribeExperiment' API. To get %% a list of all the trials associated with an experiment, call the %% `ListTrials' API. To create a trial call the `CreateTrial' API. create_experiment(Client, Input) when is_map(Client), is_map(Input) -> create_experiment(Client, Input, []). create_experiment(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateExperiment">>, Input, Options). %% @doc Create a new `FeatureGroup'. %% %% A `FeatureGroup' is a group of `Features' defined in the `FeatureStore' to %% describe a `Record'. %% %% The `FeatureGroup' defines the schema and features contained in the %% FeatureGroup. A `FeatureGroup' definition is composed of a list of %% `Features', a `RecordIdentifierFeatureName', an `EventTimeFeatureName' and %% configurations for its `OnlineStore' and `OfflineStore'. Check Amazon Web %% Services service quotas to see the `FeatureGroup's quota for your Amazon %% Web Services account. %% %% You must include at least one of `OnlineStoreConfig' and %% `OfflineStoreConfig' to create a `FeatureGroup'. create_feature_group(Client, Input) when is_map(Client), is_map(Input) -> create_feature_group(Client, Input, []). create_feature_group(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateFeatureGroup">>, Input, Options). %% @doc Creates a flow definition. create_flow_definition(Client, Input) when is_map(Client), is_map(Input) -> create_flow_definition(Client, Input, []). create_flow_definition(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateFlowDefinition">>, Input, Options). %% @doc Defines the settings you will use for the human review workflow user %% interface. %% %% Reviewers will see a three-panel interface with an instruction area, the %% item to review, and an input area. create_human_task_ui(Client, Input) when is_map(Client), is_map(Input) -> create_human_task_ui(Client, Input, []). create_human_task_ui(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateHumanTaskUi">>, Input, Options). %% @doc 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. create_hyper_parameter_tuning_job(Client, Input) when is_map(Client), is_map(Input) -> create_hyper_parameter_tuning_job(Client, Input, []). create_hyper_parameter_tuning_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateHyperParameterTuningJob">>, Input, Options). %% @doc Creates a custom SageMaker image. %% %% A SageMaker image is a set of image versions. Each image version %% represents a container image stored in Amazon Container Registry (ECR). %% For more information, see Bring your own SageMaker image. create_image(Client, Input) when is_map(Client), is_map(Input) -> create_image(Client, Input, []). create_image(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateImage">>, Input, Options). %% @doc Creates a version of the SageMaker image specified by `ImageName'. %% %% The version represents the Amazon Container Registry (ECR) container image %% specified by `BaseImage'. create_image_version(Client, Input) when is_map(Client), is_map(Input) -> create_image_version(Client, Input, []). create_image_version(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateImageVersion">>, Input, Options). %% @doc Creates a job that uses workers to label the data objects in your %% input dataset. %% %% You can use the labeled data to train machine learning models. %% %% You can select your workforce from one of three providers: %% %% You can also use automated data labeling to reduce the number %% of data objects that need to be labeled by a human. Automated data %% labeling uses active learning to determine if a data object can be labeled %% by machine or if it needs to be sent to a human worker. For more %% information, see Using Automated Data Labeling. %% %% The data objects to be labeled are contained in an Amazon S3 bucket. You %% create a manifest file that describes the location of each object. For %% more information, see Using Input and Output Data. %% %% The output can be used as the manifest file for another labeling job or as %% training data for your machine learning models. %% %% You can use this operation to create a static labeling job or a streaming %% labeling job. A static labeling job stops if all data objects in the input %% manifest file identified in `ManifestS3Uri' have been labeled. A streaming %% labeling job runs perpetually until it is manually stopped, or remains %% idle for 10 days. You can send new data objects to an active %% (`InProgress') streaming labeling job in real time. To learn how to create %% a static labeling job, see Create a Labeling Job (API) in the Amazon %% SageMaker Developer Guide. To learn how to create a streaming labeling %% job, see Create a Streaming Labeling Job. create_labeling_job(Client, Input) when is_map(Client), is_map(Input) -> create_labeling_job(Client, Input, []). create_labeling_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateLabelingJob">>, Input, Options). %% @doc Creates a model in Amazon SageMaker. %% %% In the request, you name the model and describe a primary container. For %% the primary container, you specify the Docker image that contains %% inference code, artifacts (from prior training), and a custom environment %% map that the inference code uses when you deploy the model for %% predictions. %% %% Use this API to create a model if you want to use Amazon SageMaker hosting %% services or run a batch transform job. %% %% To host your model, you create an endpoint configuration with the %% `CreateEndpointConfig' API, and then create an endpoint with the %% `CreateEndpoint' API. Amazon SageMaker then deploys all of the containers %% that you defined for the model in the hosting environment. %% %% For an example that calls this method when deploying a model to Amazon %% SageMaker hosting services, see Deploy the Model to Amazon SageMaker %% Hosting Services (Amazon Web Services SDK for Python (Boto 3)). %% %% To run a batch transform using your model, you start a job with the %% `CreateTransformJob' API. Amazon SageMaker uses your model and your %% dataset to get inferences which are then saved to a specified S3 location. %% %% In the `CreateModel' request, you must define a container with the %% `PrimaryContainer' parameter. %% %% In the request, you also provide an IAM role that Amazon SageMaker can %% assume to access model artifacts and docker image for deployment on ML %% compute hosting instances or for batch transform jobs. In addition, you %% also use the IAM role to manage permissions the inference code needs. For %% example, if the inference code access any other Amazon Web Services %% resources, you grant necessary permissions via this role. create_model(Client, Input) when is_map(Client), is_map(Input) -> create_model(Client, Input, []). create_model(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateModel">>, Input, Options). %% @doc Creates the definition for a model bias job. create_model_bias_job_definition(Client, Input) when is_map(Client), is_map(Input) -> create_model_bias_job_definition(Client, Input, []). create_model_bias_job_definition(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateModelBiasJobDefinition">>, Input, Options). %% @doc Creates the definition for a model explainability job. create_model_explainability_job_definition(Client, Input) when is_map(Client), is_map(Input) -> create_model_explainability_job_definition(Client, Input, []). create_model_explainability_job_definition(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateModelExplainabilityJobDefinition">>, Input, Options). %% @doc Creates a model package that you can use to create Amazon 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 Amazon 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'. %% %% 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. create_model_package(Client, Input) when is_map(Client), is_map(Input) -> create_model_package(Client, Input, []). create_model_package(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateModelPackage">>, Input, Options). %% @doc Creates a model group. %% %% A model group contains a group of model versions. create_model_package_group(Client, Input) when is_map(Client), is_map(Input) -> create_model_package_group(Client, Input, []). create_model_package_group(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateModelPackageGroup">>, Input, Options). %% @doc Creates a definition for a job that monitors model quality and drift. %% %% For information about model monitor, see Amazon SageMaker Model Monitor. create_model_quality_job_definition(Client, Input) when is_map(Client), is_map(Input) -> create_model_quality_job_definition(Client, Input, []). create_model_quality_job_definition(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateModelQualityJobDefinition">>, Input, Options). %% @doc Creates a schedule that regularly starts Amazon SageMaker Processing %% Jobs to monitor the data captured for an Amazon SageMaker Endoint. create_monitoring_schedule(Client, Input) when is_map(Client), is_map(Input) -> create_monitoring_schedule(Client, Input, []). create_monitoring_schedule(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateMonitoringSchedule">>, Input, Options). %% @doc Creates an Amazon SageMaker notebook instance. %% %% A notebook instance is a machine learning (ML) compute instance running on %% a Jupyter notebook. %% %% In a `CreateNotebookInstance' request, specify the type of ML compute %% instance that you want to run. Amazon SageMaker launches the instance, %% installs common libraries that you can use to explore datasets for model %% training, and attaches an ML storage volume to the notebook instance. %% %% Amazon SageMaker also provides a set of example notebooks. Each notebook %% demonstrates how to use Amazon SageMaker with a specific algorithm or with %% a machine learning framework. %% %% After receiving the request, Amazon SageMaker does the following: %% %%
  1. Creates a network interface in the Amazon SageMaker VPC. %% %%
  2. (Option) If you specified `SubnetId', Amazon SageMaker creates %% a network interface in your own VPC, which is inferred from the subnet ID %% that you provide in the input. When creating this network interface, %% Amazon SageMaker attaches the security group that you specified in the %% request to the network interface that it creates in your VPC. %% %%
  3. Launches an EC2 instance of the type specified in the request %% in the Amazon SageMaker VPC. If you specified `SubnetId' of your VPC, %% Amazon SageMaker specifies both network interfaces when launching this %% instance. This enables inbound traffic from your own VPC to the notebook %% instance, assuming that the security groups allow it. %% %%
After creating the notebook instance, Amazon SageMaker returns %% its Amazon Resource Name (ARN). You can't change the name of a notebook %% instance after you create it. %% %% After Amazon SageMaker creates the notebook instance, you can connect to %% the Jupyter server and work in Jupyter notebooks. For example, you can %% write code to explore a dataset that you can use for model training, train %% a model, host models by creating Amazon SageMaker endpoints, and validate %% hosted models. %% %% For more information, see How It Works. create_notebook_instance(Client, Input) when is_map(Client), is_map(Input) -> create_notebook_instance(Client, Input, []). create_notebook_instance(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateNotebookInstance">>, Input, Options). %% @doc Creates a lifecycle configuration that you can associate with a %% notebook instance. %% %% A lifecycle configuration is a collection of shell scripts that run when %% you create or start a notebook instance. %% %% Each lifecycle configuration script has a limit of 16384 characters. %% %% The value of the `$PATH' environment variable that is available to both %% scripts is `/sbin:bin:/usr/sbin:/usr/bin'. %% %% View CloudWatch Logs for notebook instance lifecycle configurations in log %% group `/aws/sagemaker/NotebookInstances' in log stream %% `[notebook-instance-name]/[LifecycleConfigHook]'. %% %% Lifecycle configuration scripts cannot run for longer than 5 minutes. If a %% script runs for longer than 5 minutes, it fails and the notebook instance %% is not created or started. %% %% For information about notebook instance lifestyle configurations, see Step %% 2.1: (Optional) Customize a Notebook Instance. create_notebook_instance_lifecycle_config(Client, Input) when is_map(Client), is_map(Input) -> create_notebook_instance_lifecycle_config(Client, Input, []). create_notebook_instance_lifecycle_config(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateNotebookInstanceLifecycleConfig">>, Input, Options). %% @doc Creates a pipeline using a JSON pipeline definition. create_pipeline(Client, Input) when is_map(Client), is_map(Input) -> create_pipeline(Client, Input, []). create_pipeline(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreatePipeline">>, Input, Options). %% @doc Creates a URL for a specified UserProfile in a Domain. %% %% When accessed in a web browser, the user will be automatically signed in %% to Amazon SageMaker Studio, and granted access to all of the Apps and %% files associated with the Domain's Amazon Elastic File System (EFS) %% volume. This operation can only be called when the authentication mode %% equals IAM. %% %% The IAM role or user used to call this API defines the permissions to %% access the app. Once the presigned URL is created, no additional %% permission is required to access this URL. IAM authorization policies for %% this API are also enforced for every HTTP request and WebSocket frame that %% attempts to connect to the app. %% %% You can restrict access to this API and to the URL that it returns to a %% list of IP addresses, Amazon VPCs or Amazon VPC Endpoints that you %% specify. For more information, see Connect to SageMaker Studio Through an %% Interface VPC Endpoint . %% %% The URL that you get from a call to `CreatePresignedDomainUrl' has a %% default timeout of 5 minutes. You can configure this value using %% `ExpiresInSeconds'. If you try to use the URL after the timeout limit %% expires, you are directed to the Amazon Web Services console sign-in page. create_presigned_domain_url(Client, Input) when is_map(Client), is_map(Input) -> create_presigned_domain_url(Client, Input, []). create_presigned_domain_url(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreatePresignedDomainUrl">>, Input, Options). %% @doc Returns a URL that you can use to connect to the Jupyter server from %% a notebook instance. %% %% In the Amazon SageMaker console, when you choose `Open' next to a notebook %% instance, Amazon SageMaker opens a new tab showing the Jupyter server home %% page from the notebook instance. The console uses this API to get the URL %% and show the page. %% %% The IAM role or user used to call this API defines the permissions to %% access the notebook instance. Once the presigned URL is created, no %% additional permission is required to access this URL. IAM authorization %% policies for this API are also enforced for every HTTP request and %% WebSocket frame that attempts to connect to the notebook instance. %% %% You can restrict access to this API and to the URL that it returns to a %% list of IP addresses that you specify. Use the `NotIpAddress' condition %% operator and the `aws:SourceIP' condition context key to specify the list %% of IP addresses that you want to have access to the notebook instance. For %% more information, see Limit Access to a Notebook Instance by IP Address. %% %% The URL that you get from a call to `CreatePresignedNotebookInstanceUrl' %% is valid only for 5 minutes. If you try to use the URL after the 5-minute %% limit expires, you are directed to the Amazon Web Services console sign-in %% page. create_presigned_notebook_instance_url(Client, Input) when is_map(Client), is_map(Input) -> create_presigned_notebook_instance_url(Client, Input, []). create_presigned_notebook_instance_url(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreatePresignedNotebookInstanceUrl">>, Input, Options). %% @doc Creates a processing job. create_processing_job(Client, Input) when is_map(Client), is_map(Input) -> create_processing_job(Client, Input, []). create_processing_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateProcessingJob">>, Input, Options). %% @doc Creates a machine learning (ML) project that can contain one or more %% templates that set up an ML pipeline from training to deploying an %% approved model. create_project(Client, Input) when is_map(Client), is_map(Input) -> create_project(Client, Input, []). create_project(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateProject">>, Input, Options). %% @doc Creates a new Studio Lifecycle Configuration. create_studio_lifecycle_config(Client, Input) when is_map(Client), is_map(Input) -> create_studio_lifecycle_config(Client, Input, []). create_studio_lifecycle_config(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateStudioLifecycleConfig">>, Input, Options). %% @doc Starts a model training job. %% %% After training completes, Amazon SageMaker saves the resulting model %% artifacts to an Amazon S3 location that you specify. %% %% If you choose to host your model using Amazon SageMaker hosting services, %% you can use the resulting model artifacts as part of the model. You can %% also use the artifacts in a machine learning service other than Amazon %% SageMaker, provided that you know how to use them for inference. %% %% In the request body, you provide the following: %% %% For more information about Amazon SageMaker, see How It Works. create_training_job(Client, Input) when is_map(Client), is_map(Input) -> create_training_job(Client, Input, []). create_training_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateTrainingJob">>, Input, Options). %% @doc 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: %% %% For more information about how batch transformation works, see %% Batch Transform. create_transform_job(Client, Input) when is_map(Client), is_map(Input) -> create_transform_job(Client, Input, []). create_transform_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateTransformJob">>, Input, Options). %% @doc Creates an SageMaker trial. %% %% A trial is a set of steps called trial components that produce a machine %% learning model. A trial is part of a single SageMaker experiment. %% %% When you use SageMaker Studio or the SageMaker Python SDK, all %% experiments, trials, and trial components are automatically tracked, %% logged, and indexed. When you use the Amazon Web Services SDK for Python %% (Boto), you must use the logging APIs provided by the SDK. %% %% You can add tags to a trial and then use the `Search' API to search for %% the tags. %% %% To get a list of all your trials, call the `ListTrials' API. To view a %% trial's properties, call the `DescribeTrial' API. To create a trial %% component, call the `CreateTrialComponent' API. create_trial(Client, Input) when is_map(Client), is_map(Input) -> create_trial(Client, Input, []). create_trial(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateTrial">>, Input, Options). %% @doc Creates a trial component, which is a stage of a machine learning %% trial. %% %% A trial is composed of one or more trial components. A trial component can %% be used in multiple trials. %% %% Trial components include pre-processing jobs, training jobs, and batch %% transform jobs. %% %% When you use SageMaker Studio or the SageMaker Python SDK, all %% experiments, trials, and trial components are automatically tracked, %% logged, and indexed. When you use the Amazon Web Services SDK for Python %% (Boto), you must use the logging APIs provided by the SDK. %% %% You can add tags to a trial component and then use the `Search' API to %% search for the tags. create_trial_component(Client, Input) when is_map(Client), is_map(Input) -> create_trial_component(Client, Input, []). create_trial_component(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateTrialComponent">>, Input, Options). %% @doc Creates a user profile. %% %% A user profile represents a single user within a domain, and is the main %% way to reference a "person" for the purposes of sharing, reporting, and %% other user-oriented features. This entity is created when a user onboards %% to Amazon SageMaker Studio. If an administrator invites a person by email %% or imports them from SSO, a user profile is automatically created. A user %% profile is the primary holder of settings for an individual user and has a %% reference to the user's private Amazon Elastic File System (EFS) home %% directory. create_user_profile(Client, Input) when is_map(Client), is_map(Input) -> create_user_profile(Client, Input, []). create_user_profile(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateUserProfile">>, Input, Options). %% @doc 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 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). create_workforce(Client, Input) when is_map(Client), is_map(Input) -> create_workforce(Client, Input, []). create_workforce(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateWorkforce">>, Input, Options). %% @doc 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. create_workteam(Client, Input) when is_map(Client), is_map(Input) -> create_workteam(Client, Input, []). create_workteam(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateWorkteam">>, Input, Options). %% @doc Deletes an action. delete_action(Client, Input) when is_map(Client), is_map(Input) -> delete_action(Client, Input, []). delete_action(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteAction">>, Input, Options). %% @doc Removes the specified algorithm from your account. delete_algorithm(Client, Input) when is_map(Client), is_map(Input) -> delete_algorithm(Client, Input, []). delete_algorithm(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteAlgorithm">>, Input, Options). %% @doc Used to stop and delete an app. delete_app(Client, Input) when is_map(Client), is_map(Input) -> delete_app(Client, Input, []). delete_app(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteApp">>, Input, Options). %% @doc Deletes an AppImageConfig. delete_app_image_config(Client, Input) when is_map(Client), is_map(Input) -> delete_app_image_config(Client, Input, []). delete_app_image_config(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteAppImageConfig">>, Input, Options). %% @doc Deletes an artifact. %% %% Either `ArtifactArn' or `Source' must be specified. delete_artifact(Client, Input) when is_map(Client), is_map(Input) -> delete_artifact(Client, Input, []). delete_artifact(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteArtifact">>, Input, Options). %% @doc Deletes an association. delete_association(Client, Input) when is_map(Client), is_map(Input) -> delete_association(Client, Input, []). delete_association(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteAssociation">>, Input, Options). %% @doc Deletes the specified Git repository from your account. delete_code_repository(Client, Input) when is_map(Client), is_map(Input) -> delete_code_repository(Client, Input, []). delete_code_repository(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteCodeRepository">>, Input, Options). %% @doc Deletes an context. delete_context(Client, Input) when is_map(Client), is_map(Input) -> delete_context(Client, Input, []). delete_context(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteContext">>, Input, Options). %% @doc Deletes a data quality monitoring job definition. delete_data_quality_job_definition(Client, Input) when is_map(Client), is_map(Input) -> delete_data_quality_job_definition(Client, Input, []). delete_data_quality_job_definition(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteDataQualityJobDefinition">>, Input, Options). %% @doc Deletes a fleet. delete_device_fleet(Client, Input) when is_map(Client), is_map(Input) -> delete_device_fleet(Client, Input, []). delete_device_fleet(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteDeviceFleet">>, Input, Options). %% @doc Used to delete a domain. %% %% If you onboarded with IAM mode, you will need to delete your domain to %% onboard again using SSO. Use with caution. All of the members of the %% domain will lose access to their EFS volume, including data, notebooks, %% and other artifacts. delete_domain(Client, Input) when is_map(Client), is_map(Input) -> delete_domain(Client, Input, []). delete_domain(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteDomain">>, Input, Options). %% @doc Deletes an endpoint. %% %% Amazon SageMaker frees up all of the resources that were deployed when the %% endpoint was created. %% %% Amazon SageMaker retires any custom KMS key grants associated with the %% endpoint, meaning you don't need to use the RevokeGrant API call. delete_endpoint(Client, Input) when is_map(Client), is_map(Input) -> delete_endpoint(Client, Input, []). delete_endpoint(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteEndpoint">>, Input, Options). %% @doc Deletes an endpoint configuration. %% %% The `DeleteEndpointConfig' API deletes only the specified configuration. %% It does not delete endpoints created using the configuration. %% %% You must not delete an `EndpointConfig' in use by an endpoint that is live %% or while the `UpdateEndpoint' or `CreateEndpoint' operations are being %% performed on the endpoint. If you delete the `EndpointConfig' of an %% endpoint that is active or being created or updated you may lose %% visibility into the instance type the endpoint is using. The endpoint must %% be deleted in order to stop incurring charges. delete_endpoint_config(Client, Input) when is_map(Client), is_map(Input) -> delete_endpoint_config(Client, Input, []). delete_endpoint_config(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteEndpointConfig">>, Input, Options). %% @doc 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. delete_experiment(Client, Input) when is_map(Client), is_map(Input) -> delete_experiment(Client, Input, []). delete_experiment(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteExperiment">>, Input, Options). %% @doc Delete the `FeatureGroup' and any data that was written to the %% `OnlineStore' of the `FeatureGroup'. %% %% Data cannot be accessed from the `OnlineStore' immediately after %% `DeleteFeatureGroup' is called. %% %% Data written into the `OfflineStore' will not be deleted. The Amazon Web %% Services Glue database and tables that are automatically created for your %% `OfflineStore' are not deleted. delete_feature_group(Client, Input) when is_map(Client), is_map(Input) -> delete_feature_group(Client, Input, []). delete_feature_group(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteFeatureGroup">>, Input, Options). %% @doc Deletes the specified flow definition. delete_flow_definition(Client, Input) when is_map(Client), is_map(Input) -> delete_flow_definition(Client, Input, []). delete_flow_definition(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteFlowDefinition">>, Input, Options). %% @doc Use this operation to delete a human task user interface (worker task %% template). %% %% To see a list of human task user interfaces (work task templates) in your %% account, use . When you delete a worker task template, it no longer %% appears when you call `ListHumanTaskUis'. delete_human_task_ui(Client, Input) when is_map(Client), is_map(Input) -> delete_human_task_ui(Client, Input, []). delete_human_task_ui(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteHumanTaskUi">>, Input, Options). %% @doc Deletes a SageMaker image and all versions of the image. %% %% The container images aren't deleted. delete_image(Client, Input) when is_map(Client), is_map(Input) -> delete_image(Client, Input, []). delete_image(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteImage">>, Input, Options). %% @doc Deletes a version of a SageMaker image. %% %% The container image the version represents isn't deleted. delete_image_version(Client, Input) when is_map(Client), is_map(Input) -> delete_image_version(Client, Input, []). delete_image_version(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteImageVersion">>, Input, Options). %% @doc Deletes a model. %% %% The `DeleteModel' API deletes only the model entry that was created in %% Amazon 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. delete_model(Client, Input) when is_map(Client), is_map(Input) -> delete_model(Client, Input, []). delete_model(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteModel">>, Input, Options). %% @doc Deletes an Amazon SageMaker model bias job definition. delete_model_bias_job_definition(Client, Input) when is_map(Client), is_map(Input) -> delete_model_bias_job_definition(Client, Input, []). delete_model_bias_job_definition(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteModelBiasJobDefinition">>, Input, Options). %% @doc Deletes an Amazon SageMaker model explainability job definition. delete_model_explainability_job_definition(Client, Input) when is_map(Client), is_map(Input) -> delete_model_explainability_job_definition(Client, Input, []). delete_model_explainability_job_definition(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteModelExplainabilityJobDefinition">>, Input, Options). %% @doc Deletes a model package. %% %% A model package is used to create Amazon SageMaker models or list on %% Amazon Web Services Marketplace. Buyers can subscribe to model packages %% listed on Amazon Web Services Marketplace to create models in Amazon %% SageMaker. delete_model_package(Client, Input) when is_map(Client), is_map(Input) -> delete_model_package(Client, Input, []). delete_model_package(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteModelPackage">>, Input, Options). %% @doc Deletes the specified model group. delete_model_package_group(Client, Input) when is_map(Client), is_map(Input) -> delete_model_package_group(Client, Input, []). delete_model_package_group(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteModelPackageGroup">>, Input, Options). %% @doc Deletes a model group resource policy. delete_model_package_group_policy(Client, Input) when is_map(Client), is_map(Input) -> delete_model_package_group_policy(Client, Input, []). delete_model_package_group_policy(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteModelPackageGroupPolicy">>, Input, Options). %% @doc Deletes the secified model quality monitoring job definition. delete_model_quality_job_definition(Client, Input) when is_map(Client), is_map(Input) -> delete_model_quality_job_definition(Client, Input, []). delete_model_quality_job_definition(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteModelQualityJobDefinition">>, Input, Options). %% @doc 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. delete_monitoring_schedule(Client, Input) when is_map(Client), is_map(Input) -> delete_monitoring_schedule(Client, Input, []). delete_monitoring_schedule(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteMonitoringSchedule">>, Input, Options). %% @doc Deletes an Amazon SageMaker notebook instance. %% %% Before you can delete a notebook instance, you must call the %% `StopNotebookInstance' API. %% %% When you delete a notebook instance, you lose all of your data. Amazon %% SageMaker removes the ML compute instance, and deletes the ML storage %% volume and the network interface associated with the notebook instance. delete_notebook_instance(Client, Input) when is_map(Client), is_map(Input) -> delete_notebook_instance(Client, Input, []). delete_notebook_instance(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteNotebookInstance">>, Input, Options). %% @doc Deletes a notebook instance lifecycle configuration. delete_notebook_instance_lifecycle_config(Client, Input) when is_map(Client), is_map(Input) -> delete_notebook_instance_lifecycle_config(Client, Input, []). delete_notebook_instance_lifecycle_config(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteNotebookInstanceLifecycleConfig">>, Input, Options). %% @doc Deletes a pipeline if there are no running instances of the pipeline. %% %% To delete a pipeline, you must stop all running instances of the pipeline %% using the `StopPipelineExecution' API. When you delete a pipeline, all %% instances of the pipeline are deleted. delete_pipeline(Client, Input) when is_map(Client), is_map(Input) -> delete_pipeline(Client, Input, []). delete_pipeline(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeletePipeline">>, Input, Options). %% @doc Delete the specified project. delete_project(Client, Input) when is_map(Client), is_map(Input) -> delete_project(Client, Input, []). delete_project(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteProject">>, Input, Options). %% @doc Deletes the Studio Lifecycle Configuration. %% %% In order to delete the Lifecycle Configuration, there must be no running %% apps using the Lifecycle Configuration. You must also remove the Lifecycle %% Configuration from UserSettings in all Domains and UserProfiles. delete_studio_lifecycle_config(Client, Input) when is_map(Client), is_map(Input) -> delete_studio_lifecycle_config(Client, Input, []). delete_studio_lifecycle_config(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteStudioLifecycleConfig">>, Input, Options). %% @doc Deletes the specified tags from an Amazon SageMaker resource. %% %% To list a resource's tags, use the `ListTags' API. %% %% When you call this API to delete tags from a hyperparameter tuning job, %% the deleted tags are not removed from training jobs that the %% hyperparameter tuning job launched before you called this API. %% %% When you call this API to delete tags from a SageMaker Studio Domain or %% User Profile, the deleted tags are not removed from Apps that the %% SageMaker Studio Domain or User Profile launched before you called this %% API. delete_tags(Client, Input) when is_map(Client), is_map(Input) -> delete_tags(Client, Input, []). delete_tags(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteTags">>, Input, Options). %% @doc Deletes the specified trial. %% %% All trial components that make up the trial must be deleted first. Use the %% `DescribeTrialComponent' API to get the list of trial components. delete_trial(Client, Input) when is_map(Client), is_map(Input) -> delete_trial(Client, Input, []). delete_trial(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteTrial">>, Input, Options). %% @doc Deletes the specified trial component. %% %% A trial component must be disassociated from all trials before the trial %% component can be deleted. To disassociate a trial component from a trial, %% call the `DisassociateTrialComponent' API. delete_trial_component(Client, Input) when is_map(Client), is_map(Input) -> delete_trial_component(Client, Input, []). delete_trial_component(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteTrialComponent">>, Input, Options). %% @doc Deletes a user profile. %% %% When a user profile is deleted, the user loses access to their EFS volume, %% including data, notebooks, and other artifacts. delete_user_profile(Client, Input) when is_map(Client), is_map(Input) -> delete_user_profile(Client, Input, []). delete_user_profile(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteUserProfile">>, Input, Options). %% @doc 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 to create a new workforce. %% %% If a private workforce contains one or more work teams, you must use the %% 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 %% recieve a `ResourceInUse' error. delete_workforce(Client, Input) when is_map(Client), is_map(Input) -> delete_workforce(Client, Input, []). delete_workforce(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteWorkforce">>, Input, Options). %% @doc Deletes an existing work team. %% %% This operation can't be undone. delete_workteam(Client, Input) when is_map(Client), is_map(Input) -> delete_workteam(Client, Input, []). delete_workteam(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteWorkteam">>, Input, Options). %% @doc Deregisters the specified devices. %% %% After you deregister a device, you will need to re-register the devices. deregister_devices(Client, Input) when is_map(Client), is_map(Input) -> deregister_devices(Client, Input, []). deregister_devices(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeregisterDevices">>, Input, Options). %% @doc Describes an action. describe_action(Client, Input) when is_map(Client), is_map(Input) -> describe_action(Client, Input, []). describe_action(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeAction">>, Input, Options). %% @doc Returns a description of the specified algorithm that is in your %% account. describe_algorithm(Client, Input) when is_map(Client), is_map(Input) -> describe_algorithm(Client, Input, []). describe_algorithm(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeAlgorithm">>, Input, Options). %% @doc Describes the app. describe_app(Client, Input) when is_map(Client), is_map(Input) -> describe_app(Client, Input, []). describe_app(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeApp">>, Input, Options). %% @doc Describes an AppImageConfig. describe_app_image_config(Client, Input) when is_map(Client), is_map(Input) -> describe_app_image_config(Client, Input, []). describe_app_image_config(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeAppImageConfig">>, Input, Options). %% @doc Describes an artifact. describe_artifact(Client, Input) when is_map(Client), is_map(Input) -> describe_artifact(Client, Input, []). describe_artifact(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeArtifact">>, Input, Options). %% @doc Returns information about an Amazon SageMaker AutoML job. describe_auto_ml_job(Client, Input) when is_map(Client), is_map(Input) -> describe_auto_ml_job(Client, Input, []). describe_auto_ml_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeAutoMLJob">>, Input, Options). %% @doc Gets details about the specified Git repository. describe_code_repository(Client, Input) when is_map(Client), is_map(Input) -> describe_code_repository(Client, Input, []). describe_code_repository(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeCodeRepository">>, Input, Options). %% @doc 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'. describe_compilation_job(Client, Input) when is_map(Client), is_map(Input) -> describe_compilation_job(Client, Input, []). describe_compilation_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeCompilationJob">>, Input, Options). %% @doc Describes a context. describe_context(Client, Input) when is_map(Client), is_map(Input) -> describe_context(Client, Input, []). describe_context(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeContext">>, Input, Options). %% @doc Gets the details of a data quality monitoring job definition. describe_data_quality_job_definition(Client, Input) when is_map(Client), is_map(Input) -> describe_data_quality_job_definition(Client, Input, []). describe_data_quality_job_definition(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeDataQualityJobDefinition">>, Input, Options). %% @doc Describes the device. describe_device(Client, Input) when is_map(Client), is_map(Input) -> describe_device(Client, Input, []). describe_device(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeDevice">>, Input, Options). %% @doc A description of the fleet the device belongs to. describe_device_fleet(Client, Input) when is_map(Client), is_map(Input) -> describe_device_fleet(Client, Input, []). describe_device_fleet(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeDeviceFleet">>, Input, Options). %% @doc The description of the domain. describe_domain(Client, Input) when is_map(Client), is_map(Input) -> describe_domain(Client, Input, []). describe_domain(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeDomain">>, Input, Options). %% @doc A description of edge packaging jobs. describe_edge_packaging_job(Client, Input) when is_map(Client), is_map(Input) -> describe_edge_packaging_job(Client, Input, []). describe_edge_packaging_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeEdgePackagingJob">>, Input, Options). %% @doc Returns the description of an endpoint. describe_endpoint(Client, Input) when is_map(Client), is_map(Input) -> describe_endpoint(Client, Input, []). describe_endpoint(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeEndpoint">>, Input, Options). %% @doc Returns the description of an endpoint configuration created using %% the `CreateEndpointConfig' API. describe_endpoint_config(Client, Input) when is_map(Client), is_map(Input) -> describe_endpoint_config(Client, Input, []). describe_endpoint_config(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeEndpointConfig">>, Input, Options). %% @doc Provides a list of an experiment's properties. describe_experiment(Client, Input) when is_map(Client), is_map(Input) -> describe_experiment(Client, Input, []). describe_experiment(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeExperiment">>, Input, Options). %% @doc 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. describe_feature_group(Client, Input) when is_map(Client), is_map(Input) -> describe_feature_group(Client, Input, []). describe_feature_group(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeFeatureGroup">>, Input, Options). %% @doc Returns information about the specified flow definition. describe_flow_definition(Client, Input) when is_map(Client), is_map(Input) -> describe_flow_definition(Client, Input, []). describe_flow_definition(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeFlowDefinition">>, Input, Options). %% @doc Returns information about the requested human task user interface %% (worker task template). describe_human_task_ui(Client, Input) when is_map(Client), is_map(Input) -> describe_human_task_ui(Client, Input, []). describe_human_task_ui(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeHumanTaskUi">>, Input, Options). %% @doc Gets a description of a hyperparameter tuning job. describe_hyper_parameter_tuning_job(Client, Input) when is_map(Client), is_map(Input) -> describe_hyper_parameter_tuning_job(Client, Input, []). describe_hyper_parameter_tuning_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeHyperParameterTuningJob">>, Input, Options). %% @doc Describes a SageMaker image. describe_image(Client, Input) when is_map(Client), is_map(Input) -> describe_image(Client, Input, []). describe_image(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeImage">>, Input, Options). %% @doc Describes a version of a SageMaker image. describe_image_version(Client, Input) when is_map(Client), is_map(Input) -> describe_image_version(Client, Input, []). describe_image_version(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeImageVersion">>, Input, Options). %% @doc Gets information about a labeling job. describe_labeling_job(Client, Input) when is_map(Client), is_map(Input) -> describe_labeling_job(Client, Input, []). describe_labeling_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeLabelingJob">>, Input, Options). %% @doc Describes a model that you created using the `CreateModel' API. describe_model(Client, Input) when is_map(Client), is_map(Input) -> describe_model(Client, Input, []). describe_model(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeModel">>, Input, Options). %% @doc Returns a description of a model bias job definition. describe_model_bias_job_definition(Client, Input) when is_map(Client), is_map(Input) -> describe_model_bias_job_definition(Client, Input, []). describe_model_bias_job_definition(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeModelBiasJobDefinition">>, Input, Options). %% @doc Returns a description of a model explainability job definition. describe_model_explainability_job_definition(Client, Input) when is_map(Client), is_map(Input) -> describe_model_explainability_job_definition(Client, Input, []). describe_model_explainability_job_definition(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeModelExplainabilityJobDefinition">>, Input, Options). %% @doc Returns a description of the specified model package, which is used %% to create SageMaker models or list them on Amazon Web Services %% Marketplace. %% %% To create models in SageMaker, buyers can subscribe to model packages %% listed on Amazon Web Services Marketplace. describe_model_package(Client, Input) when is_map(Client), is_map(Input) -> describe_model_package(Client, Input, []). describe_model_package(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeModelPackage">>, Input, Options). %% @doc Gets a description for the specified model group. describe_model_package_group(Client, Input) when is_map(Client), is_map(Input) -> describe_model_package_group(Client, Input, []). describe_model_package_group(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeModelPackageGroup">>, Input, Options). %% @doc Returns a description of a model quality job definition. describe_model_quality_job_definition(Client, Input) when is_map(Client), is_map(Input) -> describe_model_quality_job_definition(Client, Input, []). describe_model_quality_job_definition(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeModelQualityJobDefinition">>, Input, Options). %% @doc Describes the schedule for a monitoring job. describe_monitoring_schedule(Client, Input) when is_map(Client), is_map(Input) -> describe_monitoring_schedule(Client, Input, []). describe_monitoring_schedule(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeMonitoringSchedule">>, Input, Options). %% @doc Returns information about a notebook instance. describe_notebook_instance(Client, Input) when is_map(Client), is_map(Input) -> describe_notebook_instance(Client, Input, []). describe_notebook_instance(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeNotebookInstance">>, Input, Options). %% @doc Returns a description of a notebook instance lifecycle configuration. %% %% For information about notebook instance lifestyle configurations, see Step %% 2.1: (Optional) Customize a Notebook Instance. describe_notebook_instance_lifecycle_config(Client, Input) when is_map(Client), is_map(Input) -> describe_notebook_instance_lifecycle_config(Client, Input, []). describe_notebook_instance_lifecycle_config(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeNotebookInstanceLifecycleConfig">>, Input, Options). %% @doc Describes the details of a pipeline. describe_pipeline(Client, Input) when is_map(Client), is_map(Input) -> describe_pipeline(Client, Input, []). describe_pipeline(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribePipeline">>, Input, Options). %% @doc Describes the details of an execution's pipeline definition. describe_pipeline_definition_for_execution(Client, Input) when is_map(Client), is_map(Input) -> describe_pipeline_definition_for_execution(Client, Input, []). describe_pipeline_definition_for_execution(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribePipelineDefinitionForExecution">>, Input, Options). %% @doc Describes the details of a pipeline execution. describe_pipeline_execution(Client, Input) when is_map(Client), is_map(Input) -> describe_pipeline_execution(Client, Input, []). describe_pipeline_execution(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribePipelineExecution">>, Input, Options). %% @doc Returns a description of a processing job. describe_processing_job(Client, Input) when is_map(Client), is_map(Input) -> describe_processing_job(Client, Input, []). describe_processing_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeProcessingJob">>, Input, Options). %% @doc Describes the details of a project. describe_project(Client, Input) when is_map(Client), is_map(Input) -> describe_project(Client, Input, []). describe_project(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeProject">>, Input, Options). %% @doc Describes the Studio Lifecycle Configuration. describe_studio_lifecycle_config(Client, Input) when is_map(Client), is_map(Input) -> describe_studio_lifecycle_config(Client, Input, []). describe_studio_lifecycle_config(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeStudioLifecycleConfig">>, Input, Options). %% @doc Gets information about a work team provided by a vendor. %% %% It returns details about the subscription with a vendor in the Amazon Web %% Services Marketplace. describe_subscribed_workteam(Client, Input) when is_map(Client), is_map(Input) -> describe_subscribed_workteam(Client, Input, []). describe_subscribed_workteam(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeSubscribedWorkteam">>, Input, Options). %% @doc Returns information about a training job. %% %% Some of the attributes below only appear if the training job successfully %% starts. If the training job fails, `TrainingJobStatus' is `Failed' and, %% depending on the `FailureReason', attributes like `TrainingStartTime', %% `TrainingTimeInSeconds', `TrainingEndTime', and `BillableTimeInSeconds' %% may not be present in the response. describe_training_job(Client, Input) when is_map(Client), is_map(Input) -> describe_training_job(Client, Input, []). describe_training_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeTrainingJob">>, Input, Options). %% @doc Returns information about a transform job. describe_transform_job(Client, Input) when is_map(Client), is_map(Input) -> describe_transform_job(Client, Input, []). describe_transform_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeTransformJob">>, Input, Options). %% @doc Provides a list of a trial's properties. describe_trial(Client, Input) when is_map(Client), is_map(Input) -> describe_trial(Client, Input, []). describe_trial(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeTrial">>, Input, Options). %% @doc Provides a list of a trials component's properties. describe_trial_component(Client, Input) when is_map(Client), is_map(Input) -> describe_trial_component(Client, Input, []). describe_trial_component(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeTrialComponent">>, Input, Options). %% @doc Describes a user profile. %% %% For more information, see `CreateUserProfile'. describe_user_profile(Client, Input) when is_map(Client), is_map(Input) -> describe_user_profile(Client, Input, []). describe_user_profile(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeUserProfile">>, Input, Options). %% @doc Lists private workforce information, including workforce name, Amazon %% Resource Name (ARN), and, if applicable, allowed IP address ranges %% (CIDRs). %% %% Allowable IP address ranges are the IP addresses that workers can use to %% access tasks. %% %% This operation applies only to private workforces. describe_workforce(Client, Input) when is_map(Client), is_map(Input) -> describe_workforce(Client, Input, []). describe_workforce(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeWorkforce">>, Input, Options). %% @doc Gets information about a specific work team. %% %% You can see information such as the create date, the last updated date, %% membership information, and the work team's Amazon Resource Name (ARN). describe_workteam(Client, Input) when is_map(Client), is_map(Input) -> describe_workteam(Client, Input, []). describe_workteam(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeWorkteam">>, Input, Options). %% @doc Disables using Service Catalog in SageMaker. %% %% Service Catalog is used to create SageMaker projects. disable_sagemaker_servicecatalog_portfolio(Client, Input) when is_map(Client), is_map(Input) -> disable_sagemaker_servicecatalog_portfolio(Client, Input, []). disable_sagemaker_servicecatalog_portfolio(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DisableSagemakerServicecatalogPortfolio">>, Input, Options). %% @doc 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'. disassociate_trial_component(Client, Input) when is_map(Client), is_map(Input) -> disassociate_trial_component(Client, Input, []). disassociate_trial_component(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DisassociateTrialComponent">>, Input, Options). %% @doc Enables using Service Catalog in SageMaker. %% %% Service Catalog is used to create SageMaker projects. enable_sagemaker_servicecatalog_portfolio(Client, Input) when is_map(Client), is_map(Input) -> enable_sagemaker_servicecatalog_portfolio(Client, Input, []). enable_sagemaker_servicecatalog_portfolio(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"EnableSagemakerServicecatalogPortfolio">>, Input, Options). %% @doc Describes a fleet. get_device_fleet_report(Client, Input) when is_map(Client), is_map(Input) -> get_device_fleet_report(Client, Input, []). get_device_fleet_report(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"GetDeviceFleetReport">>, Input, Options). %% @doc Gets a resource policy that manages access for a model group. %% %% For information about resource policies, see Identity-based policies and %% resource-based policies in the Amazon Web Services Identity and Access %% Management User Guide.. get_model_package_group_policy(Client, Input) when is_map(Client), is_map(Input) -> get_model_package_group_policy(Client, Input, []). get_model_package_group_policy(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"GetModelPackageGroupPolicy">>, Input, Options). %% @doc Gets the status of Service Catalog in SageMaker. %% %% Service Catalog is used to create SageMaker projects. get_sagemaker_servicecatalog_portfolio_status(Client, Input) when is_map(Client), is_map(Input) -> get_sagemaker_servicecatalog_portfolio_status(Client, Input, []). get_sagemaker_servicecatalog_portfolio_status(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"GetSagemakerServicecatalogPortfolioStatus">>, Input, Options). %% @doc An auto-complete API for the search functionality in the Amazon %% SageMaker console. %% %% It returns suggestions of possible matches for the property name to use in %% `Search' queries. Provides suggestions for `HyperParameters', `Tags', and %% `Metrics'. get_search_suggestions(Client, Input) when is_map(Client), is_map(Input) -> get_search_suggestions(Client, Input, []). get_search_suggestions(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"GetSearchSuggestions">>, Input, Options). %% @doc Lists the actions in your account and their properties. list_actions(Client, Input) when is_map(Client), is_map(Input) -> list_actions(Client, Input, []). list_actions(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListActions">>, Input, Options). %% @doc Lists the machine learning algorithms that have been created. list_algorithms(Client, Input) when is_map(Client), is_map(Input) -> list_algorithms(Client, Input, []). list_algorithms(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListAlgorithms">>, Input, Options). %% @doc Lists the AppImageConfigs in your account and their properties. %% %% The list can be filtered by creation time or modified time, and whether %% the AppImageConfig name contains a specified string. list_app_image_configs(Client, Input) when is_map(Client), is_map(Input) -> list_app_image_configs(Client, Input, []). list_app_image_configs(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListAppImageConfigs">>, Input, Options). %% @doc Lists apps. list_apps(Client, Input) when is_map(Client), is_map(Input) -> list_apps(Client, Input, []). list_apps(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListApps">>, Input, Options). %% @doc Lists the artifacts in your account and their properties. list_artifacts(Client, Input) when is_map(Client), is_map(Input) -> list_artifacts(Client, Input, []). list_artifacts(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListArtifacts">>, Input, Options). %% @doc Lists the associations in your account and their properties. list_associations(Client, Input) when is_map(Client), is_map(Input) -> list_associations(Client, Input, []). list_associations(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListAssociations">>, Input, Options). %% @doc Request a list of jobs. list_auto_ml_jobs(Client, Input) when is_map(Client), is_map(Input) -> list_auto_ml_jobs(Client, Input, []). list_auto_ml_jobs(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListAutoMLJobs">>, Input, Options). %% @doc List the candidates created for the job. list_candidates_for_auto_ml_job(Client, Input) when is_map(Client), is_map(Input) -> list_candidates_for_auto_ml_job(Client, Input, []). list_candidates_for_auto_ml_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListCandidatesForAutoMLJob">>, Input, Options). %% @doc Gets a list of the Git repositories in your account. list_code_repositories(Client, Input) when is_map(Client), is_map(Input) -> list_code_repositories(Client, Input, []). list_code_repositories(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListCodeRepositories">>, Input, Options). %% @doc 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'. list_compilation_jobs(Client, Input) when is_map(Client), is_map(Input) -> list_compilation_jobs(Client, Input, []). list_compilation_jobs(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListCompilationJobs">>, Input, Options). %% @doc Lists the contexts in your account and their properties. list_contexts(Client, Input) when is_map(Client), is_map(Input) -> list_contexts(Client, Input, []). list_contexts(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListContexts">>, Input, Options). %% @doc Lists the data quality job definitions in your account. list_data_quality_job_definitions(Client, Input) when is_map(Client), is_map(Input) -> list_data_quality_job_definitions(Client, Input, []). list_data_quality_job_definitions(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListDataQualityJobDefinitions">>, Input, Options). %% @doc Returns a list of devices in the fleet. list_device_fleets(Client, Input) when is_map(Client), is_map(Input) -> list_device_fleets(Client, Input, []). list_device_fleets(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListDeviceFleets">>, Input, Options). %% @doc A list of devices. list_devices(Client, Input) when is_map(Client), is_map(Input) -> list_devices(Client, Input, []). list_devices(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListDevices">>, Input, Options). %% @doc Lists the domains. list_domains(Client, Input) when is_map(Client), is_map(Input) -> list_domains(Client, Input, []). list_domains(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListDomains">>, Input, Options). %% @doc Returns a list of edge packaging jobs. list_edge_packaging_jobs(Client, Input) when is_map(Client), is_map(Input) -> list_edge_packaging_jobs(Client, Input, []). list_edge_packaging_jobs(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListEdgePackagingJobs">>, Input, Options). %% @doc Lists endpoint configurations. list_endpoint_configs(Client, Input) when is_map(Client), is_map(Input) -> list_endpoint_configs(Client, Input, []). list_endpoint_configs(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListEndpointConfigs">>, Input, Options). %% @doc Lists endpoints. list_endpoints(Client, Input) when is_map(Client), is_map(Input) -> list_endpoints(Client, Input, []). list_endpoints(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListEndpoints">>, Input, Options). %% @doc Lists all the experiments in your account. %% %% The list can be filtered to show only experiments that were created in a %% specific time range. The list can be sorted by experiment name or creation %% time. list_experiments(Client, Input) when is_map(Client), is_map(Input) -> list_experiments(Client, Input, []). list_experiments(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListExperiments">>, Input, Options). %% @doc List `FeatureGroup's based on given filter and order. list_feature_groups(Client, Input) when is_map(Client), is_map(Input) -> list_feature_groups(Client, Input, []). list_feature_groups(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListFeatureGroups">>, Input, Options). %% @doc Returns information about the flow definitions in your account. list_flow_definitions(Client, Input) when is_map(Client), is_map(Input) -> list_flow_definitions(Client, Input, []). list_flow_definitions(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListFlowDefinitions">>, Input, Options). %% @doc Returns information about the human task user interfaces in your %% account. list_human_task_uis(Client, Input) when is_map(Client), is_map(Input) -> list_human_task_uis(Client, Input, []). list_human_task_uis(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListHumanTaskUis">>, Input, Options). %% @doc Gets a list of `HyperParameterTuningJobSummary' objects that describe %% the hyperparameter tuning jobs launched in your account. list_hyper_parameter_tuning_jobs(Client, Input) when is_map(Client), is_map(Input) -> list_hyper_parameter_tuning_jobs(Client, Input, []). list_hyper_parameter_tuning_jobs(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListHyperParameterTuningJobs">>, Input, Options). %% @doc Lists the versions of a specified image and their properties. %% %% The list can be filtered by creation time or modified time. list_image_versions(Client, Input) when is_map(Client), is_map(Input) -> list_image_versions(Client, Input, []). list_image_versions(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListImageVersions">>, Input, Options). %% @doc Lists the images in your account and their properties. %% %% The list can be filtered by creation time or modified time, and whether %% the image name contains a specified string. list_images(Client, Input) when is_map(Client), is_map(Input) -> list_images(Client, Input, []). list_images(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListImages">>, Input, Options). %% @doc Gets a list of labeling jobs. list_labeling_jobs(Client, Input) when is_map(Client), is_map(Input) -> list_labeling_jobs(Client, Input, []). list_labeling_jobs(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListLabelingJobs">>, Input, Options). %% @doc Gets a list of labeling jobs assigned to a specified work team. list_labeling_jobs_for_workteam(Client, Input) when is_map(Client), is_map(Input) -> list_labeling_jobs_for_workteam(Client, Input, []). list_labeling_jobs_for_workteam(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListLabelingJobsForWorkteam">>, Input, Options). %% @doc Lists model bias jobs definitions that satisfy various filters. list_model_bias_job_definitions(Client, Input) when is_map(Client), is_map(Input) -> list_model_bias_job_definitions(Client, Input, []). list_model_bias_job_definitions(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListModelBiasJobDefinitions">>, Input, Options). %% @doc Lists model explainability job definitions that satisfy various %% filters. list_model_explainability_job_definitions(Client, Input) when is_map(Client), is_map(Input) -> list_model_explainability_job_definitions(Client, Input, []). list_model_explainability_job_definitions(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListModelExplainabilityJobDefinitions">>, Input, Options). %% @doc Gets a list of the model groups in your Amazon Web Services account. list_model_package_groups(Client, Input) when is_map(Client), is_map(Input) -> list_model_package_groups(Client, Input, []). list_model_package_groups(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListModelPackageGroups">>, Input, Options). %% @doc Lists the model packages that have been created. list_model_packages(Client, Input) when is_map(Client), is_map(Input) -> list_model_packages(Client, Input, []). list_model_packages(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListModelPackages">>, Input, Options). %% @doc Gets a list of model quality monitoring job definitions in your %% account. list_model_quality_job_definitions(Client, Input) when is_map(Client), is_map(Input) -> list_model_quality_job_definitions(Client, Input, []). list_model_quality_job_definitions(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListModelQualityJobDefinitions">>, Input, Options). %% @doc Lists models created with the `CreateModel' API. list_models(Client, Input) when is_map(Client), is_map(Input) -> list_models(Client, Input, []). list_models(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListModels">>, Input, Options). %% @doc Returns list of all monitoring job executions. list_monitoring_executions(Client, Input) when is_map(Client), is_map(Input) -> list_monitoring_executions(Client, Input, []). list_monitoring_executions(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListMonitoringExecutions">>, Input, Options). %% @doc Returns list of all monitoring schedules. list_monitoring_schedules(Client, Input) when is_map(Client), is_map(Input) -> list_monitoring_schedules(Client, Input, []). list_monitoring_schedules(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListMonitoringSchedules">>, Input, Options). %% @doc Lists notebook instance lifestyle configurations created with the %% `CreateNotebookInstanceLifecycleConfig' API. list_notebook_instance_lifecycle_configs(Client, Input) when is_map(Client), is_map(Input) -> list_notebook_instance_lifecycle_configs(Client, Input, []). list_notebook_instance_lifecycle_configs(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListNotebookInstanceLifecycleConfigs">>, Input, Options). %% @doc Returns a list of the Amazon SageMaker notebook instances in the %% requester's account in an Amazon Web Services Region. list_notebook_instances(Client, Input) when is_map(Client), is_map(Input) -> list_notebook_instances(Client, Input, []). list_notebook_instances(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListNotebookInstances">>, Input, Options). %% @doc Gets a list of `PipeLineExecutionStep' objects. list_pipeline_execution_steps(Client, Input) when is_map(Client), is_map(Input) -> list_pipeline_execution_steps(Client, Input, []). list_pipeline_execution_steps(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListPipelineExecutionSteps">>, Input, Options). %% @doc Gets a list of the pipeline executions. list_pipeline_executions(Client, Input) when is_map(Client), is_map(Input) -> list_pipeline_executions(Client, Input, []). list_pipeline_executions(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListPipelineExecutions">>, Input, Options). %% @doc Gets a list of parameters for a pipeline execution. list_pipeline_parameters_for_execution(Client, Input) when is_map(Client), is_map(Input) -> list_pipeline_parameters_for_execution(Client, Input, []). list_pipeline_parameters_for_execution(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListPipelineParametersForExecution">>, Input, Options). %% @doc Gets a list of pipelines. list_pipelines(Client, Input) when is_map(Client), is_map(Input) -> list_pipelines(Client, Input, []). list_pipelines(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListPipelines">>, Input, Options). %% @doc Lists processing jobs that satisfy various filters. list_processing_jobs(Client, Input) when is_map(Client), is_map(Input) -> list_processing_jobs(Client, Input, []). list_processing_jobs(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListProcessingJobs">>, Input, Options). %% @doc Gets a list of the projects in an Amazon Web Services account. list_projects(Client, Input) when is_map(Client), is_map(Input) -> list_projects(Client, Input, []). list_projects(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListProjects">>, Input, Options). %% @doc Lists the Studio Lifecycle Configurations in your Amazon Web Services %% Account. list_studio_lifecycle_configs(Client, Input) when is_map(Client), is_map(Input) -> list_studio_lifecycle_configs(Client, Input, []). list_studio_lifecycle_configs(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListStudioLifecycleConfigs">>, Input, Options). %% @doc Gets a list of the work teams that you are subscribed to in the %% Amazon Web Services Marketplace. %% %% The list may be empty if no work team satisfies the filter specified in %% the `NameContains' parameter. list_subscribed_workteams(Client, Input) when is_map(Client), is_map(Input) -> list_subscribed_workteams(Client, Input, []). list_subscribed_workteams(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListSubscribedWorkteams">>, Input, Options). %% @doc Returns the tags for the specified Amazon SageMaker resource. list_tags(Client, Input) when is_map(Client), is_map(Input) -> list_tags(Client, Input, []). list_tags(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListTags">>, Input, Options). %% @doc Lists training jobs. %% %% When `StatusEquals' and `MaxResults' are set at the same time, the %% `MaxResults' number of training jobs are first retrieved ignoring the %% `StatusEquals' parameter and then they are filtered by the `StatusEquals' %% parameter, which is returned as a response. %% %% For example, if `ListTrainingJobs' is invoked with the following %% parameters: %% %% `{ ... MaxResults: 100, StatusEquals: InProgress ... }' %% %% First, 100 trainings jobs with any status, including those other than %% `InProgress', are selected (sorted according to the creation time, from %% the most current to the oldest). Next, those with a status of `InProgress' %% are returned. %% %% You can quickly test the API using the following Amazon Web Services CLI %% code. %% %% `aws sagemaker list-training-jobs --max-results 100 --status-equals %% InProgress' list_training_jobs(Client, Input) when is_map(Client), is_map(Input) -> list_training_jobs(Client, Input, []). list_training_jobs(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListTrainingJobs">>, Input, Options). %% @doc Gets a list of `TrainingJobSummary' objects that describe the %% training jobs that a hyperparameter tuning job launched. list_training_jobs_for_hyper_parameter_tuning_job(Client, Input) when is_map(Client), is_map(Input) -> list_training_jobs_for_hyper_parameter_tuning_job(Client, Input, []). list_training_jobs_for_hyper_parameter_tuning_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListTrainingJobsForHyperParameterTuningJob">>, Input, Options). %% @doc Lists transform jobs. list_transform_jobs(Client, Input) when is_map(Client), is_map(Input) -> list_transform_jobs(Client, Input, []). list_transform_jobs(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListTransformJobs">>, Input, Options). %% @doc Lists the trial components in your account. %% %% You can sort the list by trial component name or creation time. You can %% filter the list to show only components that were created in a specific %% time range. You can also filter on one of the following: %% %% list_trial_components(Client, Input) when is_map(Client), is_map(Input) -> list_trial_components(Client, Input, []). list_trial_components(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListTrialComponents">>, Input, Options). %% @doc Lists the trials in your account. %% %% Specify an experiment name to limit the list to the trials that are part %% of that experiment. Specify a trial component name to limit the list to %% the trials that associated with that trial component. The list can be %% filtered to show only trials that were created in a specific time range. %% The list can be sorted by trial name or creation time. list_trials(Client, Input) when is_map(Client), is_map(Input) -> list_trials(Client, Input, []). list_trials(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListTrials">>, Input, Options). %% @doc Lists user profiles. list_user_profiles(Client, Input) when is_map(Client), is_map(Input) -> list_user_profiles(Client, Input, []). list_user_profiles(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListUserProfiles">>, Input, Options). %% @doc Use this operation to list all private and vendor workforces in an %% Amazon Web Services Region. %% %% Note that you can only have one private workforce per Amazon Web Services %% Region. list_workforces(Client, Input) when is_map(Client), is_map(Input) -> list_workforces(Client, Input, []). list_workforces(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListWorkforces">>, Input, Options). %% @doc Gets a list of private work teams that you have defined in a region. %% %% The list may be empty if no work team satisfies the filter specified in %% the `NameContains' parameter. list_workteams(Client, Input) when is_map(Client), is_map(Input) -> list_workteams(Client, Input, []). list_workteams(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListWorkteams">>, Input, Options). %% @doc Adds a resouce policy to control access to a model group. %% %% For information about resoure policies, see Identity-based policies and %% resource-based policies in the Amazon Web Services Identity and Access %% Management User Guide.. put_model_package_group_policy(Client, Input) when is_map(Client), is_map(Input) -> put_model_package_group_policy(Client, Input, []). put_model_package_group_policy(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"PutModelPackageGroupPolicy">>, Input, Options). %% @doc Register devices. register_devices(Client, Input) when is_map(Client), is_map(Input) -> register_devices(Client, Input, []). register_devices(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"RegisterDevices">>, Input, Options). %% @doc Renders the UI template so that you can preview the worker's %% experience. render_ui_template(Client, Input) when is_map(Client), is_map(Input) -> render_ui_template(Client, Input, []). render_ui_template(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"RenderUiTemplate">>, Input, Options). %% @doc Retry the execution of the pipeline. retry_pipeline_execution(Client, Input) when is_map(Client), is_map(Input) -> retry_pipeline_execution(Client, Input, []). retry_pipeline_execution(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"RetryPipelineExecution">>, Input, Options). %% @doc Finds Amazon 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. search(Client, Input) when is_map(Client), is_map(Input) -> search(Client, Input, []). search(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"Search">>, Input, Options). %% @doc Notifies the pipeline that the execution of a callback step failed, %% along with a message describing why. %% %% When a callback step is run, the pipeline generates a callback token and %% includes the token in a message sent to Amazon Simple Queue Service %% (Amazon SQS). send_pipeline_execution_step_failure(Client, Input) when is_map(Client), is_map(Input) -> send_pipeline_execution_step_failure(Client, Input, []). send_pipeline_execution_step_failure(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"SendPipelineExecutionStepFailure">>, Input, Options). %% @doc Notifies the pipeline that the execution of a callback step succeeded %% and provides a list of the step's output parameters. %% %% When a callback step is run, the pipeline generates a callback token and %% includes the token in a message sent to Amazon Simple Queue Service %% (Amazon SQS). send_pipeline_execution_step_success(Client, Input) when is_map(Client), is_map(Input) -> send_pipeline_execution_step_success(Client, Input, []). send_pipeline_execution_step_success(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"SendPipelineExecutionStepSuccess">>, Input, Options). %% @doc Starts a previously stopped monitoring schedule. %% %% By default, when you successfully create a new schedule, the status of a %% monitoring schedule is `scheduled'. start_monitoring_schedule(Client, Input) when is_map(Client), is_map(Input) -> start_monitoring_schedule(Client, Input, []). start_monitoring_schedule(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"StartMonitoringSchedule">>, Input, Options). %% @doc Launches an ML compute instance with the latest version of the %% libraries and attaches your ML storage volume. %% %% After configuring the notebook instance, Amazon SageMaker sets the %% notebook instance status to `InService'. A notebook instance's status must %% be `InService' before you can connect to your Jupyter notebook. start_notebook_instance(Client, Input) when is_map(Client), is_map(Input) -> start_notebook_instance(Client, Input, []). start_notebook_instance(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"StartNotebookInstance">>, Input, Options). %% @doc Starts a pipeline execution. start_pipeline_execution(Client, Input) when is_map(Client), is_map(Input) -> start_pipeline_execution(Client, Input, []). start_pipeline_execution(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"StartPipelineExecution">>, Input, Options). %% @doc A method for forcing the termination of a running job. stop_auto_ml_job(Client, Input) when is_map(Client), is_map(Input) -> stop_auto_ml_job(Client, Input, []). stop_auto_ml_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"StopAutoMLJob">>, Input, Options). %% @doc Stops a model compilation job. %% %% To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal. %% This gracefully shuts the job down. If the job hasn't stopped, it sends %% the SIGKILL signal. %% %% When it receives a `StopCompilationJob' request, Amazon SageMaker changes %% the `CompilationJobSummary$CompilationJobStatus' of the job to `Stopping'. %% After Amazon SageMaker stops the job, it sets the %% `CompilationJobSummary$CompilationJobStatus' to `Stopped'. stop_compilation_job(Client, Input) when is_map(Client), is_map(Input) -> stop_compilation_job(Client, Input, []). stop_compilation_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"StopCompilationJob">>, Input, Options). %% @doc Request to stop an edge packaging job. stop_edge_packaging_job(Client, Input) when is_map(Client), is_map(Input) -> stop_edge_packaging_job(Client, Input, []). stop_edge_packaging_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"StopEdgePackagingJob">>, Input, Options). %% @doc Stops a running hyperparameter tuning job and all running training %% jobs that the tuning job launched. %% %% All model artifacts output from the training jobs are stored in Amazon %% Simple Storage Service (Amazon S3). All data that the training jobs write %% to Amazon CloudWatch Logs are still available in CloudWatch. After the %% tuning job moves to the `Stopped' state, it releases all reserved %% resources for the tuning job. stop_hyper_parameter_tuning_job(Client, Input) when is_map(Client), is_map(Input) -> stop_hyper_parameter_tuning_job(Client, Input, []). stop_hyper_parameter_tuning_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"StopHyperParameterTuningJob">>, Input, Options). %% @doc Stops a running labeling job. %% %% A job that is stopped cannot be restarted. Any results obtained before the %% job is stopped are placed in the Amazon S3 output bucket. stop_labeling_job(Client, Input) when is_map(Client), is_map(Input) -> stop_labeling_job(Client, Input, []). stop_labeling_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"StopLabelingJob">>, Input, Options). %% @doc Stops a previously started monitoring schedule. stop_monitoring_schedule(Client, Input) when is_map(Client), is_map(Input) -> stop_monitoring_schedule(Client, Input, []). stop_monitoring_schedule(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"StopMonitoringSchedule">>, Input, Options). %% @doc Terminates the ML compute instance. %% %% Before terminating the instance, Amazon SageMaker disconnects the ML %% storage volume from it. Amazon SageMaker preserves the ML storage volume. %% Amazon SageMaker stops charging you for the ML compute instance when you %% call `StopNotebookInstance'. %% %% To access data on the ML storage volume for a notebook instance that has %% been terminated, call the `StartNotebookInstance' API. %% `StartNotebookInstance' launches another ML compute instance, configures %% it, and attaches the preserved ML storage volume so you can continue your %% work. stop_notebook_instance(Client, Input) when is_map(Client), is_map(Input) -> stop_notebook_instance(Client, Input, []). stop_notebook_instance(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"StopNotebookInstance">>, Input, Options). %% @doc Stops a pipeline execution. %% %% Callback Step %% %% A pipeline execution won't stop while a callback step is running. When you %% call `StopPipelineExecution' on a pipeline execution with a running %% callback step, SageMaker Pipelines sends an additional Amazon SQS message %% to the specified SQS queue. The body of the SQS message contains a %% "Status" field which is set to "Stopping". %% %% You should add logic to your Amazon SQS message consumer to take any %% needed action (for example, resource cleanup) upon receipt of the message %% followed by a call to `SendPipelineExecutionStepSuccess' or %% `SendPipelineExecutionStepFailure'. %% %% Only when SageMaker Pipelines receives one of these calls will it stop the %% pipeline execution. %% %% Lambda Step %% %% A pipeline execution can't be stopped while a lambda step is running %% because the Lambda function invoked by the lambda step can't be stopped. %% If you attempt to stop the execution while the Lambda function is running, %% the pipeline waits for the Lambda function to finish or until the timeout %% is hit, whichever occurs first, and then stops. If the Lambda function %% finishes, the pipeline execution status is `Stopped'. If the timeout is %% hit the pipeline execution status is `Failed'. stop_pipeline_execution(Client, Input) when is_map(Client), is_map(Input) -> stop_pipeline_execution(Client, Input, []). stop_pipeline_execution(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"StopPipelineExecution">>, Input, Options). %% @doc Stops a processing job. stop_processing_job(Client, Input) when is_map(Client), is_map(Input) -> stop_processing_job(Client, Input, []). stop_processing_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"StopProcessingJob">>, Input, Options). %% @doc Stops a training job. %% %% To stop a job, Amazon SageMaker sends the algorithm the `SIGTERM' signal, %% which delays job termination for 120 seconds. Algorithms might use this %% 120-second window to save the model artifacts, so the results of the %% training is not lost. %% %% When it receives a `StopTrainingJob' request, Amazon SageMaker changes the %% status of the job to `Stopping'. After Amazon SageMaker stops the job, it %% sets the status to `Stopped'. stop_training_job(Client, Input) when is_map(Client), is_map(Input) -> stop_training_job(Client, Input, []). stop_training_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"StopTrainingJob">>, Input, Options). %% @doc Stops a transform job. %% %% When Amazon SageMaker receives a `StopTransformJob' request, the status of %% the job changes to `Stopping'. After Amazon SageMaker stops the job, the %% status is set to `Stopped'. When you stop a transform job before it is %% completed, Amazon SageMaker doesn't store the job's output in Amazon S3. stop_transform_job(Client, Input) when is_map(Client), is_map(Input) -> stop_transform_job(Client, Input, []). stop_transform_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"StopTransformJob">>, Input, Options). %% @doc Updates an action. update_action(Client, Input) when is_map(Client), is_map(Input) -> update_action(Client, Input, []). update_action(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"UpdateAction">>, Input, Options). %% @doc Updates the properties of an AppImageConfig. update_app_image_config(Client, Input) when is_map(Client), is_map(Input) -> update_app_image_config(Client, Input, []). update_app_image_config(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"UpdateAppImageConfig">>, Input, Options). %% @doc Updates an artifact. update_artifact(Client, Input) when is_map(Client), is_map(Input) -> update_artifact(Client, Input, []). update_artifact(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"UpdateArtifact">>, Input, Options). %% @doc Updates the specified Git repository with the specified values. update_code_repository(Client, Input) when is_map(Client), is_map(Input) -> update_code_repository(Client, Input, []). update_code_repository(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"UpdateCodeRepository">>, Input, Options). %% @doc Updates a context. update_context(Client, Input) when is_map(Client), is_map(Input) -> update_context(Client, Input, []). update_context(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"UpdateContext">>, Input, Options). %% @doc Updates a fleet of devices. update_device_fleet(Client, Input) when is_map(Client), is_map(Input) -> update_device_fleet(Client, Input, []). update_device_fleet(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"UpdateDeviceFleet">>, Input, Options). %% @doc Updates one or more devices in a fleet. update_devices(Client, Input) when is_map(Client), is_map(Input) -> update_devices(Client, Input, []). update_devices(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"UpdateDevices">>, Input, Options). %% @doc Updates the default settings for new user profiles in the domain. update_domain(Client, Input) when is_map(Client), is_map(Input) -> update_domain(Client, Input, []). update_domain(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"UpdateDomain">>, Input, Options). %% @doc Deploys the new `EndpointConfig' specified in the request, switches %% to using newly created endpoint, and then deletes resources provisioned %% for the endpoint using the previous `EndpointConfig' (there is no %% availability loss). %% %% When Amazon SageMaker receives the request, it sets the endpoint status to %% `Updating'. After updating the endpoint, it sets the status to %% `InService'. To check the status of an endpoint, use the %% `DescribeEndpoint' API. %% %% You must not delete an `EndpointConfig' in use by an endpoint that is live %% or while the `UpdateEndpoint' or `CreateEndpoint' operations are being %% performed on the endpoint. To update an endpoint, you must create a new %% `EndpointConfig'. %% %% If you delete the `EndpointConfig' of an endpoint that is active or being %% created or updated you may lose visibility into the instance type the %% endpoint is using. The endpoint must be deleted in order to stop incurring %% charges. update_endpoint(Client, Input) when is_map(Client), is_map(Input) -> update_endpoint(Client, Input, []). update_endpoint(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"UpdateEndpoint">>, Input, Options). %% @doc Updates variant weight of one or more variants associated with an %% existing endpoint, or capacity of one variant associated with an existing %% endpoint. %% %% When it receives the request, Amazon SageMaker sets the endpoint status to %% `Updating'. After updating the endpoint, it sets the status to %% `InService'. To check the status of an endpoint, use the %% `DescribeEndpoint' API. update_endpoint_weights_and_capacities(Client, Input) when is_map(Client), is_map(Input) -> update_endpoint_weights_and_capacities(Client, Input, []). update_endpoint_weights_and_capacities(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"UpdateEndpointWeightsAndCapacities">>, Input, Options). %% @doc Adds, updates, or removes the description of an experiment. %% %% Updates the display name of an experiment. update_experiment(Client, Input) when is_map(Client), is_map(Input) -> update_experiment(Client, Input, []). update_experiment(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"UpdateExperiment">>, Input, Options). %% @doc Updates the properties of a SageMaker image. %% %% To change the image's tags, use the `AddTags' and `DeleteTags' APIs. update_image(Client, Input) when is_map(Client), is_map(Input) -> update_image(Client, Input, []). update_image(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"UpdateImage">>, Input, Options). %% @doc Updates a versioned model. update_model_package(Client, Input) when is_map(Client), is_map(Input) -> update_model_package(Client, Input, []). update_model_package(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"UpdateModelPackage">>, Input, Options). %% @doc Updates a previously created schedule. update_monitoring_schedule(Client, Input) when is_map(Client), is_map(Input) -> update_monitoring_schedule(Client, Input, []). update_monitoring_schedule(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"UpdateMonitoringSchedule">>, Input, Options). %% @doc Updates a notebook instance. %% %% NotebookInstance updates include upgrading or downgrading the ML compute %% instance used for your notebook instance to accommodate changes in your %% workload requirements. update_notebook_instance(Client, Input) when is_map(Client), is_map(Input) -> update_notebook_instance(Client, Input, []). update_notebook_instance(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"UpdateNotebookInstance">>, Input, Options). %% @doc Updates a notebook instance lifecycle configuration created with the %% `CreateNotebookInstanceLifecycleConfig' API. update_notebook_instance_lifecycle_config(Client, Input) when is_map(Client), is_map(Input) -> update_notebook_instance_lifecycle_config(Client, Input, []). update_notebook_instance_lifecycle_config(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"UpdateNotebookInstanceLifecycleConfig">>, Input, Options). %% @doc Updates a pipeline. update_pipeline(Client, Input) when is_map(Client), is_map(Input) -> update_pipeline(Client, Input, []). update_pipeline(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"UpdatePipeline">>, Input, Options). %% @doc Updates a pipeline execution. update_pipeline_execution(Client, Input) when is_map(Client), is_map(Input) -> update_pipeline_execution(Client, Input, []). update_pipeline_execution(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"UpdatePipelineExecution">>, Input, Options). %% @doc Updates a machine learning (ML) project that is created from a %% template that sets up an ML pipeline from training to deploying an %% approved model. %% %% You must not update a project that is in use. If you update the %% `ServiceCatalogProvisioningUpdateDetails' of a project that is active or %% being created, or updated, you may lose resources already created by the %% project. update_project(Client, Input) when is_map(Client), is_map(Input) -> update_project(Client, Input, []). update_project(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"UpdateProject">>, Input, Options). %% @doc Update a model training job to request a new Debugger profiling %% configuration. update_training_job(Client, Input) when is_map(Client), is_map(Input) -> update_training_job(Client, Input, []). update_training_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"UpdateTrainingJob">>, Input, Options). %% @doc Updates the display name of a trial. update_trial(Client, Input) when is_map(Client), is_map(Input) -> update_trial(Client, Input, []). update_trial(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"UpdateTrial">>, Input, Options). %% @doc Updates one or more properties of a trial component. update_trial_component(Client, Input) when is_map(Client), is_map(Input) -> update_trial_component(Client, Input, []). update_trial_component(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"UpdateTrialComponent">>, Input, Options). %% @doc Updates a user profile. update_user_profile(Client, Input) when is_map(Client), is_map(Input) -> update_user_profile(Client, Input, []). update_user_profile(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"UpdateUserProfile">>, Input, Options). %% @doc Use this operation to update your workforce. %% %% You can use this operation to require that workers use specific IP %% addresses to work on tasks and to update your OpenID Connect (OIDC) %% Identity Provider (IdP) workforce configuration. %% %% Use `SourceIpConfig' to restrict worker access to tasks to a specific %% range of IP addresses. You specify allowed IP addresses by creating a list %% of up to ten CIDRs. By default, a workforce isn't restricted to specific %% IP addresses. If you specify a range of IP addresses, workers who attempt %% to access tasks using any IP address outside the specified range are %% denied and get a `Not Found' error message on the worker portal. %% %% Use `OidcConfig' to update the configuration of a workforce created using %% your own OIDC IdP. %% %% You can only update your OIDC IdP configuration when there are no work %% teams associated with your workforce. You can delete work teams using the %% operation. %% %% After restricting access to a range of IP addresses or updating your OIDC %% IdP configuration with this operation, you can view details about your %% update workforce using the operation. %% %% This operation only applies to private workforces. update_workforce(Client, Input) when is_map(Client), is_map(Input) -> update_workforce(Client, Input, []). update_workforce(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"UpdateWorkforce">>, Input, Options). %% @doc Updates an existing work team with new member definitions or %% description. update_workteam(Client, Input) when is_map(Client), is_map(Input) -> update_workteam(Client, Input, []). update_workteam(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"UpdateWorkteam">>, Input, Options). %%==================================================================== %% Internal functions %%==================================================================== -spec request(aws_client:aws_client(), binary(), map(), list()) -> {ok, Result, {integer(), list(), hackney:client()}} | {error, Error, {integer(), list(), hackney:client()}} | {error, term()} when Result :: map() | undefined, Error :: map(). request(Client, Action, Input, Options) -> RequestFun = fun() -> do_request(Client, Action, Input, Options) end, aws_request:request(RequestFun, Options). do_request(Client, Action, Input0, Options) -> Client1 = Client#{service => <<"sagemaker">>}, Host = build_host(<<"api.sagemaker">>, Client1), URL = build_url(Host, Client1), Headers = [ {<<"Host">>, Host}, {<<"Content-Type">>, <<"application/x-amz-json-1.1">>}, {<<"X-Amz-Target">>, <<"SageMaker.", Action/binary>>} ], Input = Input0, Payload = jsx:encode(Input), SignedHeaders = aws_request:sign_request(Client1, <<"POST">>, URL, Headers, Payload), Response = hackney:request(post, URL, SignedHeaders, Payload, Options), handle_response(Response). handle_response({ok, 200, ResponseHeaders, Client}) -> case hackney:body(Client) of {ok, <<>>} -> {ok, undefined, {200, ResponseHeaders, Client}}; {ok, Body} -> Result = jsx:decode(Body), {ok, Result, {200, ResponseHeaders, Client}} end; handle_response({ok, StatusCode, ResponseHeaders, Client}) -> {ok, Body} = hackney:body(Client), Error = jsx:decode(Body), {error, Error, {StatusCode, ResponseHeaders, Client}}; handle_response({error, Reason}) -> {error, Reason}. build_host(_EndpointPrefix, #{region := <<"local">>, endpoint := Endpoint}) -> Endpoint; build_host(_EndpointPrefix, #{region := <<"local">>}) -> <<"localhost">>; build_host(EndpointPrefix, #{region := Region, endpoint := Endpoint}) -> aws_util:binary_join([EndpointPrefix, Region, Endpoint], <<".">>). build_url(Host, Client) -> Proto = maps:get(proto, Client), Port = maps:get(port, Client), aws_util:binary_join([Proto, <<"://">>, Host, <<":">>, Port, <<"/">>], <<"">>).