AWS Documentation » AWS Identity and Access Management » User Guide » Reference Information for AWS Identity and Access Management » IAM JSON Policy Reference » Actions, Resources, and Condition Keys for AWS Services » Actions, Resources, and Condition Keys for Amazon SageMaker

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Actions, Resources, and Condition Keys for Amazon SageMaker

Amazon SageMaker (service prefix: sagemaker) provides the following service-specific resources, actions, and condition context keys for use in IAM permission policies.

References:

• Learn how to configure this service.

• View a list of the API operations available for this service.

• Learn how to secure this service and its resources by using IAM permission policies.

Actions Defined by Amazon SageMaker

You can specify the following actions in the Action element of an IAM policy statement. Use policies to grant permissions to perform an operation in AWS. When you use an action in a policy, you usually allow or deny access to the API operation or CLI command with the same name. However, in some cases, a single action controls access to more than one operation. Alternatively, some operations require several different actions.

The Resource column indicates whether each action supports resource-level permissions. If there is no value for this column, you must specify all resources ("*") in the Resource element of your policy statement. If the column includes a resource type, then you can specify an ARN of that type in a statement with that action. Required resources are indicated in the table with an asterisk (*). If you specify a resource-level permission ARN in a statement using this action, then it must be of this type. Some actions support multiple resource types. If the resource type is optional (not indicated as required), then you can choose to use one but not the other.

For details about the columns in the following table, see The Actions Table.

Actions	Description	Access Level	Resource Types (*required)	Condition Keys	Dependent Actions
AddTags	Adds or overwrites one or more tags for the specified Amazon SageMaker resource.	Tagging	endpoint
			endpoint-config
			hyper-parameter-tuning-job
			labeling-job
			model
			notebook-instance
			training-job
			transform-job
			workteam
				aws:RequestTag/${TagKey} aws:TagKeys
CreateAlgorithm	Create an algorithm.	Write	algorithm*
CreateCodeRepository	Create a code repository.	Write	code-repository*
CreateCompilationJob	Create a compilation job.	Write	compilation-job*		iam:PassRole
CreateEndpoint	Creates an endpoint using the endpoint configuration specified in the request.	Write	endpoint*
				aws:RequestTag/${TagKey} aws:TagKeys
CreateEndpointConfig	Creates an endpoint configuration that can be deployed using Amazon SageMaker hosting services.	Write	endpoint-config*
				aws:RequestTag/${TagKey} aws:TagKeys sagemaker:AcceleratorTypes sagemaker:InstanceTypes sagemaker:ModelArn sagemaker:VolumeKmsKey sagemaker:VpcSecurityGroupIds sagemaker:VpcSubnets
CreateHyperParameterTuningJob	Creates hyper parameter tuning job that can be deployed using Amazon SageMaker.	Write	hyper-parameter-tuning-job*		iam:PassRole
				aws:RequestTag/${TagKey} aws:TagKeys sagemaker:FileSystemAccessMode sagemaker:FileSystemDirectoryPath sagemaker:FileSystemId sagemaker:FileSystemType sagemaker:InstanceTypes sagemaker:InterContainerTrafficEncryption sagemaker:MaxRuntimeInSeconds sagemaker:NetworkIsolation sagemaker:OutputKmsKey sagemaker:VolumeKmsKey sagemaker:VpcSecurityGroupIds sagemaker:VpcSubnets
CreateLabelingJob	Starts a labeling job. A labeling job takes unlabeled data in and produces labeled data as output, which can be used for training SageMaker models.	Write	labeling-job*		iam:PassRole
				aws:RequestTag/${TagKey} aws:TagKeys
CreateModel	Creates a model in Amazon SageMaker. In the request, you specify a name for the model and describe one or more containers.	Write	model*		iam:PassRole
				aws:RequestTag/${TagKey} aws:TagKeys sagemaker:NetworkIsolation sagemaker:VpcSecurityGroupIds sagemaker:VpcSubnets
CreateModelPackage	Create a model package.	Write	model-package*
CreateNotebookInstance	Creates an Amazon SageMaker notebook instance. A notebook instance is an Amazon EC2 instance running on a Jupyter Notebook.	Write	notebook-instance*		iam:PassRole
				aws:RequestTag/${TagKey} aws:TagKeys sagemaker:AcceleratorTypes sagemaker:DirectInternetAccess sagemaker:InstanceTypes sagemaker:RootAccess sagemaker:VolumeKmsKey sagemaker:VpcSecurityGroupIds sagemaker:VpcSubnets
CreateNotebookInstanceLifecycleConfig	Creates an notebook instance lifecycle configuration that can be deployed using Amazon SageMaker.	Write	notebook-instance-lifecycle-config*
CreatePresignedNotebookInstanceUrl	Returns a URL that you can use from your browser to connect to the Notebook Instance.	Write	notebook-instance*
CreateTrainingJob	Starts a model training job. After training completes, Amazon SageMaker saves the resulting model artifacts and other optional output to an Amazon S3 location that you specify.	Write	training-job*		iam:PassRole
				aws:RequestTag/${TagKey} aws:TagKeys sagemaker:FileSystemAccessMode sagemaker:FileSystemDirectoryPath sagemaker:FileSystemId sagemaker:FileSystemType sagemaker:InstanceTypes sagemaker:InterContainerTrafficEncryption sagemaker:MaxRuntimeInSeconds sagemaker:NetworkIsolation sagemaker:OutputKmsKey sagemaker:VolumeKmsKey sagemaker:VpcSecurityGroupIds sagemaker:VpcSubnets
CreateTransformJob	Starts a transform job. After the results are obtained, Amazon SageMaker saves them to an Amazon S3 location that you specify.	Write	transform-job*
				aws:RequestTag/${TagKey} aws:TagKeys sagemaker:InstanceTypes sagemaker:ModelArn sagemaker:OutputKmsKey sagemaker:VolumeKmsKey
CreateWorkteam	Create a workteam.	Write	workteam*
				aws:RequestTag/${TagKey} aws:TagKeys
DeleteAlgorithm	Deletes an algorithm.	Write	algorithm*
DeleteCodeRepository	Deletes a code repository.	Write	code-repository*
DeleteEndpoint	Deletes an endpoint. Amazon SageMaker frees up all the resources that were deployed when the endpoint was created.	Write	endpoint*
DeleteEndpointConfig	Deletes the endpoint configuration created using the CreateEndpointConfig API. The DeleteEndpointConfig API deletes only the specified configuration. It does not delete any endpoints created using the configuration.	Write	endpoint-config*
DeleteModel	Deletes a model created using the CreateModel API. The DeleteModel API deletes only the model entry in Amazon SageMaker that you created by calling the CreateModel API. It does not delete model artifacts, inference code, or the IAM role that you specified when creating the model.	Write	model*
DeleteModelPackage	Deletes a model package.	Write	model-package*
DeleteNotebookInstance	Deletes an Amazon SageMaker notebook instance. Before you can delete a notebook instance, you must call the StopNotebookInstance API.	Write	notebook-instance*
DeleteNotebookInstanceLifecycleConfig	Deletes an notebook instance lifecycle configuration that can be deployed using Amazon SageMaker.	Write	notebook-instance-lifecycle-config*
DeleteTags	Deletes the specified set of tags from an Amazon SageMaker resource.	Tagging	compilation-job
			endpoint
			endpoint-config
			hyper-parameter-tuning-job
			labeling-job
			model
			notebook-instance
			training-job
			transform-job
			workteam
				aws:TagKeys
DeleteWorkteam	Deletes a workteam.	Write	workteam*
DescribeAlgorithm	Returns information about an algorithm.	Read	algorithm*
DescribeCodeRepository	Returns information about a code repository.	Read	code-repository*
DescribeCompilationJob	Returns information about a compilation job.	Read	compilation-job*
DescribeEndpoint	Returns the description of an endpoint.	Read	endpoint*
DescribeEndpointConfig	Returns the description of an endpoint configuration, which was created using the CreateEndpointConfig API.	Read	endpoint-config*
DescribeHyperParameterTuningJob	Describes a hyper parameter tuning job that was created via CreateHyperParameterTuningJob API.	Read	hyper-parameter-tuning-job*
DescribeLabelingJob	Returns information about a labeling job.	Read	labeling-job*
DescribeModel	Describes a model that you created using the CreateModel API.	Read	model*
DescribeModelPackage	Returns information about a model package.	Read	model-package*
DescribeNotebookInstance	Returns information about a notebook instance.	Read	notebook-instance*
DescribeNotebookInstanceLifecycleConfig	Describes an notebook instance lifecycle configuration that was created via CreateNotebookInstanceLifecycleConfig API.	Read	notebook-instance-lifecycle-config*
DescribeSubscribedWorkteam	Returns information about a subscribed workteam.	Read	workteam*
DescribeTrainingJob	Returns information about a training job.	Read	training-job*
DescribeTransformJob	Returns information about a transform job.	Read	transform-job*
DescribeWorkteam	Returns information about a workteam.	Read	workteam*
GetSearchSuggestions	Get search suggestions when provided with keyword.	Read	training-job*
InvokeEndpoint	After you deploy a model into production using Amazon SageMaker hosting services, your client applications use this API to get inferences from the model hosted at the specified endpoint.	Read	endpoint*
ListAlgorithms	Lists algorithms.	List
ListCodeRepositories	Lists code repositories.	List
ListCompilationJobs	Lists compilation jobs.	List
ListEndpointConfigs	Lists endpoint configurations.	List
ListEndpoints	Lists endpoints.	List
ListHyperParameterTuningJobs	Lists hyper parameter tuning jobs that was created using Amazon SageMaker.	List
ListLabelingJobs	Lists labeling jobs.	List
ListLabelingJobsForWorkteam	Lists labeling jobs for workteam.	List	workteam*
ListModelPackages	Lists model packages.	List
ListModels	Lists the models created with the CreateModel API.	List
ListNotebookInstanceLifecycleConfigs	Lists notebook instance lifecycle configurations that can be deployed using Amazon SageMaker.	List
ListNotebookInstances	Returns a list of the Amazon SageMaker notebook instances in the requester's account in an AWS Region.	List
ListSubscribedWorkteams	Lists subscribed workteams.	List
ListTags	Returns the tag set associated with the specified resource.	List	endpoint
			endpoint-config
			hyper-parameter-tuning-job
			labeling-job
			model
			notebook-instance
			training-job
			transform-job
			workteam
ListTrainingJobs	Lists training jobs.	List
ListTrainingJobsForHyperParameterTuningJob	Lists training jobs for a hyper parameter tuning job that was created using Amazon SageMaker.	List	hyper-parameter-tuning-job*
ListTransformJobs	Lists transform jobs.	List
ListWorkteams	Lists workteams.	List
RenderUiTemplate	Render a UI template used for a human annotation task.	Read			iam:PassRole
Search	Search for a training job.	Read	training-job*
StartNotebookInstance	Launches an EC2 instance with the latest version of the libraries and attaches your EBS volume.	Write	notebook-instance*
StopCompilationJob	Stops a compilation job.	Write	compilation-job*
StopHyperParameterTuningJob	Stops a running hyper parameter tuning job create via the CreateHyperParameterTuningJob.	Write	hyper-parameter-tuning-job*
StopLabelingJob	Stops a labeling job. Any labels already generated will be exported before stopping.	Write	labeling-job*
StopNotebookInstance	Terminates the EC2 instance. Before terminating the instance, Amazon SageMaker disconnects the EBS volume from it. Amazon SageMaker preserves the EBS volume.	Write	notebook-instance*
StopTrainingJob	Stops a training job. To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds.	Write	training-job*
StopTransformJob	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	Write	transform-job*
UpdateCodeRepository	Updates a code repository.	Write	code-repository*
UpdateEndpoint	Updates an endpoint to use the endpoint configuration specified in the request.	Write	endpoint*
UpdateEndpointWeightsAndCapacities	Updates variant weight, capacity, or both of one or more variants associated with an endpoint.	Write	endpoint*
UpdateNotebookInstance	Updates a notebook instance. Notebook instance updates include upgrading or downgrading the EC2 instance used for your notebook instance to accommodate changes in your workload requirements. You can also update the VPC security groups.	Write	notebook-instance*
				sagemaker:AcceleratorTypes sagemaker:InstanceTypes sagemaker:RootAccess
UpdateNotebookInstanceLifecycleConfig	Updates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API.	Write	notebook-instance-lifecycle-config*
UpdateWorkteam	Updates a workteam.	Write	workteam*

Resources Defined by Amazon SageMaker

The following resource types are defined by this service and can be used in the Resource element of IAM permission policy statements. Each action in the Actions table identifies the resource types that can be specified with that action. A resource type can also define which condition keys you can include in a policy. These keys are displayed in the last column of the table. For details about the columns in the following table, see The Resource Types Table.

Resource Types	ARN	Condition Keys
labeling-job	arn:${Partition}:sagemaker:${Region}:${Account}:labeling-job/${LabelingJobName}	aws:ResourceTag/${TagKey} sagemaker:ResourceTag/${TagKey}
workteam	arn:${Partition}:sagemaker:${Region}:${Account}:workteam/${WorkteamName}	aws:ResourceTag/${TagKey} sagemaker:ResourceTag/${TagKey}
notebook-instance	arn:${Partition}:sagemaker:${Region}:${Account}:notebook-instance/${NotebookInstanceName}	aws:ResourceTag/${TagKey} sagemaker:ResourceTag/${TagKey}
notebook-instance-lifecycle-config	arn:${Partition}:sagemaker:${Region}:${Account}:notebook-instance-lifecycle-config/${NotebookInstanceLifecycleConfigName}
code-repository	arn:${Partition}:sagemaker:${Region}:${Account}:code-repository/${CodeRepositoryName}
algorithm	arn:${Partition}:sagemaker:${Region}:${Account}:algorithm/${AlgorithmName}
training-job	arn:${Partition}:sagemaker:${Region}:${Account}:training-job/${TrainingJobName}	aws:ResourceTag/${TagKey} sagemaker:ResourceTag/${TagKey}
hyper-parameter-tuning-job	arn:${Partition}:sagemaker:${Region}:${Account}:hyper-parameter-tuning-job/${HyperParameterTuningJobName}	aws:ResourceTag/${TagKey} sagemaker:ResourceTag/${TagKey}
model-package	arn:${Partition}:sagemaker:${Region}:${Account}:model-package/${ModelPackageName}
model	arn:${Partition}:sagemaker:${Region}:${Account}:model/${ModelName}	aws:ResourceTag/${TagKey} sagemaker:ResourceTag/${TagKey}
endpoint-config	arn:${Partition}:sagemaker:${Region}:${Account}:endpoint-config/${EndpointConfigName}	aws:ResourceTag/${TagKey} sagemaker:ResourceTag/${TagKey}
endpoint	arn:${Partition}:sagemaker:${Region}:${Account}:endpoint/${EndpointName}	aws:ResourceTag/${TagKey} sagemaker:ResourceTag/${TagKey}
transform-job	arn:${Partition}:sagemaker:${Region}:${Account}:transform-job/${TransformJobName}	aws:ResourceTag/${TagKey} sagemaker:ResourceTag/${TagKey}
compilation-job	arn:${Partition}:sagemaker:${Region}:${Account}:compilation-job/${CompilationJobName}

Condition Keys for Amazon SageMaker

Amazon SageMaker defines the following condition keys that can be used in the Condition element of an IAM policy. You can use these keys to further refine the conditions under which the policy statement applies. For details about the columns in the following table, see The Condition Keys Table.

To view the global condition keys that are available to all services, see Available Global Condition Keys in the IAM Policy Reference.

Condition Keys	Description	Type
aws:RequestTag/${TagKey}	A key that is present in the request the user makes to the SageMaker service.	String
aws:ResourceTag/${TagKey}	A tag key and value pair.	String
aws:TagKeys	The list of all the tag key names associated with the resource in the request.	String
sagemaker:AcceleratorTypes	The list of all accelerator types associated with the resource in the request.	ArrayOfString
sagemaker:DirectInternetAccess	The direct internet access associated with the resource in the request.	String
sagemaker:FileSystemAccessMode	File system access mode associated with the resource in the request.	String
sagemaker:FileSystemDirectoryPath	File system directory path associated with the resource in the request.	String
sagemaker:FileSystemId	A file system ID associated with the resource in the request.	String
sagemaker:FileSystemType	File system type associated with the resource in the request.	String
sagemaker:InstanceTypes	The list of all instance types associated with the resource in the request.	ArrayOfString
sagemaker:InterContainerTrafficEncryption	The inter container traffic encryption associated with the resource in the request.	Bool
sagemaker:MaxRuntimeInSeconds	The max runtime in seconds associated with the resource in the request.	Numeric
sagemaker:ModelArn	The model arn associated with the resource in the request.	ARN
sagemaker:NetworkIsolation	The network isolation associated with the resource in the request.	Bool
sagemaker:OutputKmsKey	The output kms key associated with the resource in the request.	ARN
sagemaker:ResourceTag/	The preface string for a tag key and value pair attached to a resource.	String
sagemaker:ResourceTag/${TagKey}	A tag key and value pair.	String
sagemaker:RootAccess	The root access associated with the resource in the request.	String
sagemaker:VolumeKmsKey	The volume kms key associated with the resource in the request.	ARN
sagemaker:VpcSecurityGroupIds	The list of all vpc security group ids associated with the resource in the request.	ArrayOfString
sagemaker:VpcSubnets	The list of all vpc subnets associated with the resource in the request.	ArrayOfString