Actions, resources, and condition keys for Amazon SageMaker with MLflow

Amazon SageMaker with MLflow (service prefix: sagemaker-mlflow) 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.

Topics

Actions defined by Amazon SageMaker with MLflow
Resource types defined by Amazon SageMaker with MLflow
Condition keys for Amazon SageMaker with MLflow

Actions defined by Amazon SageMaker with MLflow

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 types column of the Actions table indicates whether each action supports resource-level permissions. If there is no value for this column, you must specify all resources ("*") to which the policy applies 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. If the action has one or more required resources, the caller must have permission to use the action with those resources. Required resources are indicated in the table with an asterisk (*). If you limit resource access with the Resource element in an IAM policy, you must include an ARN or pattern for each required resource type. Some actions support multiple resource types. If the resource type is optional (not indicated as required), then you can choose to use one of the optional resource types.

The Condition keys column of the Actions table includes keys that you can specify in a policy statement's Condition element. For more information on the condition keys that are associated with resources for the service, see the Condition keys column of the Resource types table.

Note

Resource condition keys are listed in the Resource types table. You can find a link to the resource type that applies to an action in the Resource types (*required) column of the Actions table. The resource type in the Resource types table includes the Condition keys column, which are the resource condition keys that apply to an action in the Actions table.

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

Actions	Description	Access level	Resource types (*required)
AccessUI	Grants permission to access the MLflow UI	Read
CreateExperiment	Grants permission to create an MLflow experiment	Write	mlflow-tracking-server*
CreateModelVersion	Grants permission to create a new model version	Write	mlflow-tracking-server*
CreateRegisteredModel	Grants permission to create a registered model	Write	mlflow-tracking-server*
CreateRun	Grants permission to create a new run within an experiment	Write	mlflow-tracking-server*
DeleteExperiment	Grants permission to mark an MLflow experiment for deletion	Write	mlflow-tracking-server*
DeleteModelVersion	Grants permission to delete a model version	Write	mlflow-tracking-server*
DeleteModelVersionTag	Grants permission to delete a model version tag	Write	mlflow-tracking-server*
DeleteRegisteredModel	Grants permission to delete a registered model	Write	mlflow-tracking-server*
DeleteRegisteredModelAlias	Grants permission to delete a registered model alias	Write	mlflow-tracking-server*
DeleteRegisteredModelTag	Grants permission to delete a registered model tag	Write	mlflow-tracking-server*
DeleteRun	Grants permission to mark a run for deletion	Write	mlflow-tracking-server*
DeleteTag	Grants permission to delete a tag on a run	Write	mlflow-tracking-server*
DeleteTraceTag	Grants permission to delete a trace tag in MLflow	Write	mlflow-tracking-server*
DeleteTraces	Grants permission to delete traces in MLflow	Write	mlflow-tracking-server*
EndTrace	Grants permission to end a trace in MLflow	Write	mlflow-tracking-server*
GetDownloadURIForModelVersionArtifacts	Grants permission to get a URI to download model artifacts for a specific model version	Read	mlflow-tracking-server*
GetExperiment	Grants permission to get metadata for an MLflow experiment	Read	mlflow-tracking-server*
GetExperimentByName	Grants permission to get metadata for an MLflow experiment by name	Read	mlflow-tracking-server*
GetLatestModelVersions	Grants permission to get the latest model versions	List	mlflow-tracking-server*
GetMetricHistory	Grants permission to get a list of all values for the specified metric for a given run	Read	mlflow-tracking-server*
GetModelVersion	Grants permission to get a model version by model name and version	Read	mlflow-tracking-server*
GetModelVersionByAlias	Grants permission to get model version by alias in MLflow	Read	mlflow-tracking-server*
GetRegisteredModel	Grants permission to get a registered model	Read	mlflow-tracking-server*
GetRun	Grants permission to get metadata, metrics, parameters, and tags for a run	Read	mlflow-tracking-server*
GetTraceInfo	Grants permission to get information about a trace in MLflow	Read	mlflow-tracking-server*
ListArtifacts	Grants permission to list artifacts for a run	List	mlflow-tracking-server*
LogBatch	Grants permission to log a batch of metrics, parameters, and tags for a run	Write	mlflow-tracking-server*
LogInputs	Grants permission to log inputs for a run	Write	mlflow-tracking-server*
LogMetric	Grants permission to log a metric for a run	Write	mlflow-tracking-server*
LogModel	Grants permission to log the model associated with a run	Write	mlflow-tracking-server*
LogParam	Grants permission to log a parameter tracked during a run	Write	mlflow-tracking-server*
RenameRegisteredModel	Grants permission to rename a registered model	Write	mlflow-tracking-server*
RestoreExperiment	Grants permission to restore an experiment marked for deletion	Write	mlflow-tracking-server*
RestoreRun	Grants permission to restore a deleted run	Write	mlflow-tracking-server*
SearchExperiments	Grants permission to search for MLflow experiments	Read	mlflow-tracking-server*
SearchModelVersions	Grants permission to search for a model version	Read	mlflow-tracking-server*
SearchRegisteredModels	Grants permission to search for registered models in MLflow	Read	mlflow-tracking-server*
SearchRuns	Grants permission to search for runs that satisfy expressions	Read	mlflow-tracking-server*
SearchTraces	Grants permission to search for traces in MLflow	Read	mlflow-tracking-server*
SetExperimentTag	Grants permission to set a tag on an experiment	Write	mlflow-tracking-server*
SetModelVersionTag	Grants permission to set a tag for the model version	Write	mlflow-tracking-server*
SetRegisteredModelAlias	Grants permission to set a registered model alias	Write	mlflow-tracking-server*
SetRegisteredModelTag	Grants permission to set a tag for a registered model	Write	mlflow-tracking-server*
SetTag	Grants permission to set a tag on a run	Write	mlflow-tracking-server*
SetTraceTag	Grants permission to set a trace tag in MLflow	Write	mlflow-tracking-server*
StartTrace	Grants permission to start a trace in MLflow	Write	mlflow-tracking-server*
TransitionModelVersionStage	Grants permission to transition a model version to a particular stage	Write	mlflow-tracking-server*
UpdateExperiment	Grants permission to update the metadata for an MLflow experiment	Write	mlflow-tracking-server*
UpdateModelVersion	Grants permission to update the model version	Write	mlflow-tracking-server*
UpdateRegisteredModel	Grants permission to update a registered model	Write	mlflow-tracking-server*
UpdateRun	Grants permission to update run metadata	Write	mlflow-tracking-server*

Resource types defined by Amazon SageMaker with MLflow

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 Resource types table. For details about the columns in the following table, see Resource types table.

Resource types ARN Condition keys

Resource types	ARN	Condition keys
mlflow-tracking-server	`arn:${Partition}:sagemaker:${Region}:${Account}:mlflow-tracking-server/${MlflowTrackingServerName}`	aws:ResourceTag/${TagKey} sagemaker:ResourceTag/${TagKey}

mlflow-tracking-server

arn:${Partition}:sagemaker:${Region}:${Account}:mlflow-tracking-server/${MlflowTrackingServerName}

aws:ResourceTag/${TagKey}

sagemaker:ResourceTag/${TagKey}

Condition keys for Amazon SageMaker with MLflow

Amazon SageMaker with MLflow 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 Condition keys table.

To view the global condition keys that are available to all services, see Available global condition keys.

Condition keys	Description	Type
aws:ResourceTag/${TagKey}	Filters access by a tag key and value pair	String
sagemaker:ResourceTag/${TagKey}	Filters access by a tag key and value pair	String

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