aws sagemaker

Provides APIs for creating and managing Amazon SageMaker resources. Other Resources: Amazon SageMaker Developer Guide Amazon Augmented AI Runtime API Reference

Subcommands

NameDescription
add-associationCreates 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-tagsAdds 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 AWS 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
associate-trial-componentAssociates 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
create-actionCreates 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-algorithmCreate a machine learning algorithm that you can use in Amazon SageMaker and list in the AWS Marketplace
create-appCreates 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-image-configCreates 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-artifactCreates 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-auto-ml-jobCreates 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-code-repositoryCreates 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 AWS CodeCommit or in any other Git repository
create-compilation-jobStarts 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 AWS IoT Greengrass. In that case, deploy them as an ML resource. In the request body, you provide the following: A name for the compilation job Information about the input model artifacts The output location for the compiled model and the device (target) that the model runs on The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker assumes to perform the model compilation job. 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-contextCreates 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-data-quality-job-definitionCreates a definition for a job that monitors data quality and drift. For information about model monitor, see Amazon SageMaker Model Monitor
create-device-fleetCreates a device fleet
create-domainCreates 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 AWS 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 AWS Key Management Service (AWS KMS) to encrypt the EFS volume attached to the domain with an AWS managed customer master key (CMK) by default. For more control, you can specify a customer managed CMK. 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: PublicInternetOnly - Non-EFS traffic goes through a VPC managed by Amazon SageMaker, which allows internet access. This is the default value. VpcOnly - All Studio traffic is through the specified VPC and subnets. Internet access is disabled by default. To allow internet access, you must specify a NAT gateway. When internet access is disabled, you won't be able to run a Studio notebook or to train or host models unless your VPC has an interface endpoint to the SageMaker API and runtime or a NAT gateway and your security groups allow outbound connections. For more information, see Connect SageMaker Studio Notebooks to Resources in a VPC
create-edge-packaging-jobStarts 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-endpointCreates 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 Deploy the Model to Amazon SageMaker Hosting Services (AWS SDK for Python (Boto 3)). 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 AWS Region in your AWS 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 AWS Security Token Service to download model artifacts from the S3 path you provided. AWS STS is activated in your IAM user account by default. If you previously deactivated AWS STS for a region, you need to reactivate AWS STS for that region. For more information, see Activating and Deactivating AWS STS in an AWS Region in the AWS 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 Amazon 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 Amazon SageMaker API Permissions: Actions, Permissions, and Resources Reference
create-endpoint-configCreates 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. 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 (AWS SDK for Python (Boto 3)). 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-experimentCreates 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 Amazon SageMaker Studio or the Amazon SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the AWS 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-feature-groupCreate 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 AWS service quotas to see the FeatureGroups quota for your AWS account. You must include at least one of OnlineStoreConfig and OfflineStoreConfig to create a FeatureGroup
create-flow-definitionCreates a flow definition
create-human-task-uiDefines 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-hyper-parameter-tuning-jobStarts 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-imageCreates 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-versionCreates a version of the SageMaker image specified by ImageName. The version represents the Amazon Container Registry (ECR) container image specified by BaseImage
create-labeling-jobCreates 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: A private workforce that you create. It can include employees, contractors, and outside experts. Use a private workforce when want the data to stay within your organization or when a specific set of skills is required. One or more vendors that you select from the AWS Marketplace. Vendors provide expertise in specific areas. The Amazon Mechanical Turk workforce. This is the largest workforce, but it should only be used for public data or data that has been stripped of any personally identifiable information. 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-modelCreates 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 (AWS 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 AWS resources, you grant necessary permissions via this role
create-model-bias-job-definitionCreates the definition for a model bias job
create-model-explainability-job-definitionCreates the definition for a model explainability job
create-model-packageCreates a model package that you can use to create Amazon SageMaker models or list on AWS Marketplace, or a versioned model that is part of a model group. Buyers can subscribe to model packages listed on AWS 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 AWS 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-groupCreates a model group. A model group contains a group of model versions
create-model-quality-job-definitionCreates a definition for a job that monitors model quality and drift. For information about model monitor, see Amazon SageMaker Model Monitor
create-monitoring-scheduleCreates a schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an Amazon SageMaker Endpoint
create-notebook-instanceCreates 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: Creates a network interface in the Amazon SageMaker VPC. (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. 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-lifecycle-configCreates 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-pipelineCreates a pipeline using a JSON pipeline definition
create-presigned-domain-urlCreates 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 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 AWS console sign-in page
create-presigned-notebook-instance-urlReturns 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 AWS console sign-in page
create-processing-jobCreates a processing job
create-projectCreates 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-training-jobStarts 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: AlgorithmSpecification - Identifies the training algorithm to use. HyperParameters - Specify these algorithm-specific parameters to enable the estimation of model parameters during training. Hyperparameters can be tuned to optimize this learning process. For a list of hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms. InputDataConfig - Describes the training dataset and the Amazon S3, EFS, or FSx location where it is stored. OutputDataConfig - Identifies the Amazon S3 bucket where you want Amazon SageMaker to save the results of model training. ResourceConfig - Identifies the resources, ML compute instances, and ML storage volumes to deploy for model training. In distributed training, you specify more than one instance. EnableManagedSpotTraining - Optimize the cost of training machine learning models by up to 80% by using Amazon EC2 Spot instances. For more information, see Managed Spot Training. RoleArn - The Amazon Resource Name (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during model training. You must grant this role the necessary permissions so that Amazon SageMaker can successfully complete model training. StoppingCondition - To help cap training costs, use MaxRuntimeInSeconds to set a time limit for training. Use MaxWaitTimeInSeconds to specify how long you are willing to wait for a managed spot training job to complete. Environment - The environment variables to set in the Docker container. For more information about Amazon SageMaker, see How It Works
create-transform-jobStarts a transform job. A transform job uses a trained model to get inferences on a dataset and saves these results to an Amazon S3 location that you specify. To perform batch transformations, you create a transform job and use the data that you have readily available. In the request body, you provide the following: TransformJobName - Identifies the transform job. The name must be unique within an AWS Region in an AWS account. ModelName - Identifies the model to use. ModelName must be the name of an existing Amazon SageMaker model in the same AWS Region and AWS account. For information on creating a model, see CreateModel. TransformInput - Describes the dataset to be transformed and the Amazon S3 location where it is stored. TransformOutput - Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job. TransformResources - Identifies the ML compute instances for the transform job. For more information about how batch transformation works, see Batch Transform
create-trialCreates an Amazon 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 Amazon SageMaker experiment. When you use Amazon SageMaker Studio or the Amazon SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the AWS 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-componentCreates 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 Amazon SageMaker Studio or the Amazon SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the AWS 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. CreateTrialComponent can only be invoked from within an Amazon SageMaker managed environment. This includes Amazon SageMaker training jobs, processing jobs, transform jobs, and Amazon SageMaker notebooks. A call to CreateTrialComponent from outside one of these environments results in an error
create-user-profileCreates 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-workforceUse this operation to create a workforce. This operation will return an error if a workforce already exists in the AWS Region that you specify. You can only create one workforce in each AWS Region per AWS account. If you want to create a new workforce in an AWS 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-workteamCreates 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
delete-actionDeletes an action
delete-algorithmRemoves the specified algorithm from your account
delete-appUsed to stop and delete an app
delete-app-image-configDeletes an AppImageConfig
delete-artifactDeletes an artifact. Either ArtifactArn or Source must be specified
delete-associationDeletes an association
delete-code-repositoryDeletes the specified Git repository from your account
delete-contextDeletes an context
delete-data-quality-job-definitionDeletes a data quality monitoring job definition
delete-device-fleetDeletes a fleet
delete-domainUsed 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-endpointDeletes 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-configDeletes 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-experimentDeletes an Amazon 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-feature-groupDelete 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 AWS Glue database and tables that are automatically created for your OfflineStore are not deleted
delete-flow-definitionDeletes the specified flow definition
delete-human-task-uiUse 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-imageDeletes a SageMaker image and all versions of the image. The container images aren't deleted
delete-image-versionDeletes a version of a SageMaker image. The container image the version represents isn't deleted
delete-modelDeletes 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-bias-job-definitionDeletes an Amazon SageMaker model bias job definition
delete-model-explainability-job-definitionDeletes an Amazon SageMaker model explainability job definition
delete-model-packageDeletes a model package. A model package is used to create Amazon SageMaker models or list on AWS Marketplace. Buyers can subscribe to model packages listed on AWS Marketplace to create models in Amazon SageMaker
delete-model-package-groupDeletes the specified model group
delete-model-package-group-policyDeletes a model group resource policy
delete-model-quality-job-definitionDeletes the secified model quality monitoring job definition
delete-monitoring-scheduleDeletes 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-notebook-instanceDeletes 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-lifecycle-configDeletes a notebook instance lifecycle configuration
delete-pipelineDeletes a pipeline if there are no in-progress executions
delete-projectDelete the specified project
delete-tagsDeletes 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
delete-trialDeletes 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-componentDeletes 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-user-profileDeletes a user profile. When a user profile is deleted, the user loses access to their EFS volume, including data, notebooks, and other artifacts
delete-workforceUse this operation to delete a workforce. If you want to create a new workforce in an AWS 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 receive a ResourceInUse error
delete-workteamDeletes an existing work team. This operation can't be undone
deregister-devicesDeregisters the specified devices. After you deregister a device, you will need to re-register the devices
describe-actionDescribes an action
describe-algorithmReturns a description of the specified algorithm that is in your account
describe-appDescribes the app
describe-app-image-configDescribes an AppImageConfig
describe-artifactDescribes an artifact
describe-auto-ml-jobReturns information about an Amazon SageMaker AutoML job
describe-code-repositoryGets details about the specified Git repository
describe-compilation-jobReturns 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-contextDescribes a context
describe-data-quality-job-definitionGets the details of a data quality monitoring job definition
describe-deviceDescribes the device
describe-device-fleetA description of the fleet the device belongs to
describe-domainThe description of the domain
describe-edge-packaging-jobA description of edge packaging jobs
describe-endpointReturns the description of an endpoint
describe-endpoint-configReturns the description of an endpoint configuration created using the CreateEndpointConfig API
describe-experimentProvides a list of an experiment's properties
describe-feature-groupUse 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-flow-definitionReturns information about the specified flow definition
describe-human-task-uiReturns information about the requested human task user interface (worker task template)
describe-hyper-parameter-tuning-jobGets a description of a hyperparameter tuning job
describe-imageDescribes a SageMaker image
describe-image-versionDescribes a version of a SageMaker image
describe-labeling-jobGets information about a labeling job
describe-modelDescribes a model that you created using the CreateModel API
describe-model-bias-job-definitionReturns a description of a model bias job definition
describe-model-explainability-job-definitionReturns a description of a model explainability job definition
describe-model-packageReturns a description of the specified model package, which is used to create Amazon SageMaker models or list them on AWS Marketplace. To create models in Amazon SageMaker, buyers can subscribe to model packages listed on AWS Marketplace
describe-model-package-groupGets a description for the specified model group
describe-model-quality-job-definitionReturns a description of a model quality job definition
describe-monitoring-scheduleDescribes the schedule for a monitoring job
describe-notebook-instanceReturns information about a notebook instance
describe-notebook-instance-lifecycle-configReturns 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-pipelineDescribes the details of a pipeline
describe-pipeline-definition-for-executionDescribes the details of an execution's pipeline definition
describe-pipeline-executionDescribes the details of a pipeline execution
describe-processing-jobReturns a description of a processing job
describe-projectDescribes the details of a project
describe-subscribed-workteamGets information about a work team provided by a vendor. It returns details about the subscription with a vendor in the AWS Marketplace
describe-training-jobReturns 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-transform-jobReturns information about a transform job
describe-trialProvides a list of a trial's properties
describe-trial-componentProvides a list of a trials component's properties
describe-user-profileDescribes a user profile. For more information, see CreateUserProfile
describe-workforceLists 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-workteamGets 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)
disable-sagemaker-servicecatalog-portfolioDisables using Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects
disassociate-trial-componentDisassociates 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
enable-sagemaker-servicecatalog-portfolioEnables using Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects
get-device-fleet-reportDescribes a fleet
get-model-package-group-policyGets 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 AWS Identity and Access Management User Guide
get-sagemaker-servicecatalog-portfolio-statusGets the status of Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects
get-search-suggestionsAn 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
list-actionsLists the actions in your account and their properties
list-algorithmsLists the machine learning algorithms that have been created
list-app-image-configsLists 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-appsLists apps
list-artifactsLists the artifacts in your account and their properties
list-associationsLists the associations in your account and their properties
list-auto-ml-jobsRequest a list of jobs
list-candidates-for-auto-ml-jobList the candidates created for the job
list-code-repositoriesGets a list of the Git repositories in your account
list-compilation-jobsLists 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-contextsLists the contexts in your account and their properties
list-data-quality-job-definitionsLists the data quality job definitions in your account
list-device-fleetsReturns a list of devices in the fleet
list-devicesA list of devices
list-domainsLists the domains
list-edge-packaging-jobsReturns a list of edge packaging jobs
list-endpoint-configsLists endpoint configurations
list-endpointsLists endpoints
list-experimentsLists 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-feature-groupsList FeatureGroups based on given filter and order
list-flow-definitionsReturns information about the flow definitions in your account
list-human-task-uisReturns information about the human task user interfaces in your account
list-hyper-parameter-tuning-jobsGets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account
list-image-versionsLists the versions of a specified image and their properties. The list can be filtered by creation time or modified time
list-imagesLists 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-labeling-jobsGets a list of labeling jobs
list-labeling-jobs-for-workteamGets a list of labeling jobs assigned to a specified work team
list-model-bias-job-definitionsLists model bias jobs definitions that satisfy various filters
list-model-explainability-job-definitionsLists model explainability job definitions that satisfy various filters
list-model-package-groupsGets a list of the model groups in your AWS account
list-model-packagesLists the model packages that have been created
list-model-quality-job-definitionsGets a list of model quality monitoring job definitions in your account
list-modelsLists models created with the CreateModel API
list-monitoring-executionsReturns list of all monitoring job executions
list-monitoring-schedulesReturns list of all monitoring schedules
list-notebook-instance-lifecycle-configsLists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API
list-notebook-instancesReturns a list of the Amazon SageMaker notebook instances in the requester's account in an AWS Region
list-pipeline-execution-stepsGets a list of PipeLineExecutionStep objects
list-pipeline-executionsGets a list of the pipeline executions
list-pipeline-parameters-for-executionGets a list of parameters for a pipeline execution
list-pipelinesGets a list of pipelines
list-processing-jobsLists processing jobs that satisfy various filters
list-projectsGets a list of the projects in an AWS account
list-subscribed-workteamsGets a list of the work teams that you are subscribed to in the AWS Marketplace. The list may be empty if no work team satisfies the filter specified in the NameContains parameter
list-tagsReturns the tags for the specified Amazon SageMaker resource
list-training-jobsLists 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 ... } Then, 100 trainings jobs with any status including those other than InProgress are selected first (sorted according the creation time, from the latest to the oldest) and those with status InProgress are returned. You can quickly test the API using the following AWS CLI code. aws sagemaker list-training-jobs --max-results 100 --status-equals InProgress
list-training-jobs-for-hyper-parameter-tuning-jobGets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched
list-transform-jobsLists transform jobs
list-trial-componentsLists 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: ExperimentName SourceArn TrialName
list-trialsLists 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-user-profilesLists user profiles
list-workforcesUse this operation to list all private and vendor workforces in an AWS Region. Note that you can only have one private workforce per AWS Region
list-workteamsGets 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
put-model-package-group-policyAdds a resource policy to control access to a model group. For information about resource policies, see Identity-based policies and resource-based policies in the AWS Identity and Access Management User Guide
register-devicesRegister devices
render-ui-templateRenders the UI template so that you can preview the worker's experience
searchFinds 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
start-monitoring-scheduleStarts a previously stopped monitoring schedule. By default, when you successfully create a new schedule, the status of a monitoring schedule is scheduled
start-notebook-instanceLaunches 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-pipeline-executionStarts a pipeline execution
stop-auto-ml-jobA method for forcing the termination of a running job
stop-compilation-jobStops 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-edge-packaging-jobRequest to stop an edge packaging job
stop-hyper-parameter-tuning-jobStops 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-labeling-jobStops 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-monitoring-scheduleStops a previously started monitoring schedule
stop-notebook-instanceTerminates 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-pipeline-executionStops a pipeline execution
stop-processing-jobStops a processing job
stop-training-jobStops 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-transform-jobStops 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
update-actionUpdates an action
update-app-image-configUpdates the properties of an AppImageConfig
update-artifactUpdates an artifact
update-code-repositoryUpdates the specified Git repository with the specified values
update-contextUpdates a context
update-device-fleetUpdates a fleet of devices
update-devicesUpdates one or more devices in a fleet
update-domainUpdates the default settings for new user profiles in the domain
update-endpointDeploys 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-weights-and-capacitiesUpdates 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-experimentAdds, updates, or removes the description of an experiment. Updates the display name of an experiment
update-imageUpdates the properties of a SageMaker image. To change the image's tags, use the AddTags and DeleteTags APIs
update-model-packageUpdates a versioned model
update-monitoring-scheduleUpdates a previously created schedule
update-notebook-instanceUpdates 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-lifecycle-configUpdates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API
update-pipelineUpdates a pipeline
update-pipeline-executionUpdates a pipeline execution
update-training-jobUpdate a model training job to request a new Debugger profiling configuration
update-trialUpdates the display name of a trial
update-trial-componentUpdates one or more properties of a trial component
update-user-profileUpdates a user profile
update-workforceUse 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-workteamUpdates an existing work team with new member definitions or description
waitWait until a particular condition is satisfied. Each subcommand polls an API until the listed requirement is met