How to use SageMaker AI execution roles

Amazon SageMaker AI performs operations on your behalf using other AWS services. You must grant SageMaker AI permissions to use these services and the resources they act upon. You grant SageMaker AI these permissions using an AWS Identity and Access Management (IAM) execution role. For more information on IAM roles, see IAM roles.

To create and use an execution role, you can use the following procedures.

Create execution role

Use the following procedure to create an execution role with the IAM managed policy, AmazonSageMakerFullAccess, attached. If your use case requires more granular permissions, use other sections on this page to create an execution role that meets your business needs. You can create an execution role using the SageMaker AI console or the AWS CLI.

Important

The IAM managed policy, AmazonSageMakerFullAccess, used in the following procedure only grants the execution role permission to perform certain Amazon S3 actions on buckets or objects with SageMaker, Sagemaker, sagemaker, or aws-glue in the name. To learn how to add an additional policy to an execution role to grant it access to other Amazon S3 buckets and objects, see Add Additional Amazon S3 Permissions to a SageMaker AI Execution Role.

Note

You can create an execution role directly when you create a SageMaker AI domain or a notebook instance.

For information on how to create a SageMaker AI domain, see Guide to getting set up with Amazon SageMaker AI.
For information on how to create a notebook instance, see Create an Amazon SageMaker Notebook Instance for the tutorial.

To create a new execution role from the SageMaker AI console

Open the IAM console at https://console.aws.amazon.com/iam/.
Choose Roles and then choose Create role.
Keep AWS service as the Trusted entity type and then use the down arrow to find SageMaker AI in Use cases for other AWS services.
Choose SageMaker AI – Execution and then choose Next.
The IAM managed policy, AmazonSageMakerFullAccess, is automatically attached to the role. To see the permissions included in this policy, choose the plus (+) sign next to the policy name. Choose Next.
Enter a Role name and a Description.
(Optional) Add additional permissions and tags to the role.
Choose Create role.
On the Roles section of the IAM console, find the role you just created. If needed, use the text box to search for the role using the role name.
On the role summary page, make note of the ARN.

To create a new execution role from the AWS CLI

Before you create an execution role using the AWS CLI, make sure to update and configure it by following the instructions in (Optional) Configure the AWS CLI, then continue with the instructions in Custom setup using the AWS CLI.

Once you have created an execution role, you can associate it with a SageMaker AI domain, a user profile, or with a Jupyter notebook instance.

To learn about how to associate an execution role with an existing SageMaker AI domain, see Edit domain settings.
To learn about how to associate an execution role with an existing user profile, see Add user profiles.
To learn about how to associate an execution role with an existing notebook instance, see Update a Notebook Instance.

You can also pass the ARN of an execution role to your API call. For example, using Amazon SageMaker Python SDK, you can pass the ARN of your execution role to an estimator. In the code sample that follows, we create an estimator using the XGBoost algorithm container and pass the ARN of the execution role as a parameter. For the full example on GitHub, see Customer Churn Prediction with XGBoost.


import sagemaker, boto3
from sagemaker import image_uris

sess = sagemaker.Session()
region = sess.boto_region_name
bucket = sess.default_bucket()
prefix = "sagemaker/DEMO-xgboost-churn"
container = sagemaker.image_uris.retrieve("xgboost", region, "1.7-1")

xgb = sagemaker.estimator.Estimator(
    container,
    execution-role-ARN,
    instance_count=1,
    instance_type="ml.m4.xlarge",
    output_path="s3://{}/{}/output".format(bucket, prefix),
    sagemaker_session=sess,
)

...

Add Additional Amazon S3 Permissions to a SageMaker AI Execution Role

When you use a SageMaker AI feature with resources in Amazon S3, such as input data, the execution role you specify in your request (for example CreateTrainingJob) is used to access these resources.

If you attach the IAM managed policy, AmazonSageMakerFullAccess, to an execution role, that role has permission to perform certain Amazon S3 actions on buckets or objects with SageMaker, Sagemaker, sagemaker, or aws-glue in the name. It also has permission to perform the following actions on any Amazon S3 resource:


"s3:CreateBucket", 
"s3:GetBucketLocation",
"s3:ListBucket",
"s3:ListAllMyBuckets",
"s3:GetBucketCors",
"s3:PutBucketCors"

To give an execution role permissions to access one or more specific buckets in Amazon S3, you can attach a policy similar to the following to the role. This policy grants an IAM role permission to perform all actions that AmazonSageMakerFullAccess allows but restricts this access to the buckets amzn-s3-demo-bucket1 and amzn-s3-demo-bucket2. Refer to the security documentation for the specific SageMaker AI feature you are using to learn more about the Amazon S3 permissions required for that feature.

Get your execution role

You can use the SageMaker AI console, Amazon SageMaker Python SDK, or the AWS CLI to retrieve the ARN and name of the execution role attached to a SageMaker AI domain, space, or user profile.

Get domain execution role

The following provides instructions on finding your domain’s execution role.

Find the execution role attached to your domain

Open the SageMaker AI console, https://console.aws.amazon.com/sagemaker/
On the left navigation pane, choose Domains under Admin configurations.
Choose the link corresponding to your domain.
Choose the Domain settings tab.
In the General settings section, the execution role ARN is listed under Execution role.

The execution role name is after the last / in the execution role ARN.

Get space execution role

The following provides instructions on finding your space’s execution role.

Find the execution role attached to your space

Open the SageMaker AI console, https://console.aws.amazon.com/sagemaker/
On the left navigation pane, choose Domains under Admin configurations.
Choose the link corresponding to your domain.
Choose the Space management tab.
In the Details section, the execution role ARN is listed under Execution role.

The execution role name is after the last / in the execution role ARN.

Note

The following code is meant to be run in a SageMaker AI environment, like any of the IDEs in Amazon SageMaker Studio. You will receive an error if you run get_execution_role outside of a SageMaker AI environment.

The following get_execution_role Amazon SageMaker Python SDK command retrieves the ARN of the execution role attached to the space.


from sagemaker import get_execution_role
role = get_execution_role()
print(role)

The execution role name is after the last / in the execution role ARN.

Get user execution role

The following provides instructions on finding a user’s execution role.

Find the execution role attached to a user

Open the SageMaker AI console, https://console.aws.amazon.com/sagemaker/
On the left navigation pane, choose Domains under Admin configurations.
Choose the link corresponding to your domain.
Choose the User profiles tab.
Choose the link corresponding to your user.
In the Details section, the execution role ARN is listed under Execution role.

The execution role name is after the last / in the execution role ARN.

Note

To use the following examples, you must have the AWS Command Line Interface (AWS CLI) installed and configured. For information, see Get started with the AWS CLI in the AWS Command Line Interface User Guide for Version 2.

The following get-caller-identity AWS CLI command displays information about the IAM identity used to authenticate the request. The caller is an IAM user.


aws sts get-caller-identity

The execution role name is after the last / in the execution role ARN.

Change your execution role

An execution role is an IAM role that a SageMaker AI identity (like a SageMaker AI user, space, or domain) assumes. Changing the IAM role changes the permissions for all of the identities assuming that role.

When you change an execution role the corresponding space's execution role will also change. The effects of the change may take some time to propagate.

When you change a user's execution role, the private spaces created by that user will assume the changed execution role.
When you change a space's default execution role, the shared spaces in the domain will assume the changed execution role.

For more information on execution roles and spaces, see Understanding domain space permissions and execution roles.

You can change the execution role for a identity to a different IAM role by using one of the following instructions.

If, instead, you want to modify a role that an identity is assuming, see Modify permissions to execution role.

Topics

Change the domain default execution role
Change space default execution role
Change user profile execution role

Change the domain default execution role

The following provides instructions on changing your domain’s default execution role.

Change the default execution role attached to your domain

Open the SageMaker AI console, https://console.aws.amazon.com/sagemaker/
On the left navigation pane, choose Domains under Admin configurations.
Choose the link corresponding to your domain.
Choose the Domain settings tab.
In the General settings section, choose Edit.
In the Permissions section, under Default execution role expand the drop-down list.
In the drop-down list you can choose an existing role, enter a custom IAM role ARN, or create a new role.

If you wish to create a new role, you can choose Create role using the role creation wizard option.
Choose Next in the following steps and choose Submit on the last step.

Change space default execution role

The following provides instructions on changing your space’s default execution role. Changing this execution role will change the role assumed by all of the shared spaces in the domain.

Change the space default execution role for when you create a new space

Open the SageMaker AI console, https://console.aws.amazon.com/sagemaker/
On the left navigation pane, choose Domains under Admin configurations.
Choose the link corresponding to your domain.
Choose the Domain settings tab.
In the General settings section, choose Edit.
In the Permissions section, under Space default execution role expand the drop-down list.
In the drop-down list you can choose an existing role, enter a custom IAM role ARN, or create a new role.

If you wish to create a new role, you can choose Create role using the role creation wizard option.
Choose Next in the following steps and choose Submit on the last step.

Change user profile execution role

The following provides instructions on changing a user’s execution role. Changing this execution role will change the role assumed by all of the private spaces created by this user.

Change the execution role attached to a user

Open the SageMaker AI console, https://console.aws.amazon.com/sagemaker/
On the left navigation pane, choose Domains under Admin configurations.
Choose the link corresponding to your domain.
Choose the User profiles tab.
Choose the link corresponding to the user profile name.
Choose Edit.
In the drop-down list you can choose an existing role, enter a custom IAM role ARN, or create a new role.

If you wish to create a new role, you can choose Create role using the role creation wizard option.
Choose Next in the following steps and choose Submit on the last step.

Modify permissions to execution role

You can modify existing permissions to the execution role of an identity (like a SageMaker AI user, space, or domain). This is done by finding the appropriate IAM role that the identity is assuming, then modifying that IAM role. The following will provide instructions on achieving this through the console.

When you modify an execution role the corresponding space's execution role will also change. The effects of the change may not be immediate.

When you modify a user's execution role, the private spaces created by that user will assume the modified execution role.
When you modify a space's default execution role, the shared spaces in the domain will assume the modified execution role.

For more information on execution roles and spaces, see Understanding domain space permissions and execution roles.

If, instead, you want to change a role that an identity is assuming, see Change your execution role.

To modify permissions to your execution roles

First get name of the identity you would like to modify.
To modify a role that an identity is assuming, see Modifying a role in the AWS Identity and Access Management User Guide.

For more information and instructions on adding permissions to IAM identities, see Add or remove identity permissions in the AWS Identity and Access Management User Guide.

Passing Roles

Actions like passing a role between services are a common function within SageMaker AI. You can find more details on Actions, Resources, and Condition Keys for SageMaker AI in the Service Authorization Reference.

You pass the role (iam:PassRole) when making these API calls: CreateAutoMLJob, CreateCompilationJob, CreateDomain, CreateFeatureGroup, CreateFlowDefiniton, CreateHyperParameterTuningJob, CreateImage, CreateLabelingJob, CreateModel, CreateMonitoringSchedule, CreateNotebookInstance, CreateProcessingJob, CreateTrainingJob, CreateUserProfile, RenderUiTemplate, UpdateImage, and UpdateNotebookInstance.

You attach the following trust policy to the IAM role which grants SageMaker AI principal permissions to assume the role, and is the same for all of the execution roles:

The permissions that you need to grant to the role vary depending on the API that you call. The following sections explain these permissions.

Note

Instead of managing permissions by crafting a permission policy, you can use the AWS-managed AmazonSageMakerFullAccess permission policy. The permissions in this policy are fairly broad, to allow for any actions you might want to perform in SageMaker AI. For a listing of the policy including information about the reasons for adding many of the permissions, see AWS managed policy: AmazonSageMakerFullAccess. If you prefer to create custom policies and manage permissions to scope the permissions only to the actions you need to perform with the execution role, see the following topics.

Important

If you're running into issues, see Troubleshooting Amazon SageMaker AI Identity and Access.

For more information about IAM roles, see IAM Roles in the Service Authorization Reference.

Topics

CreateAutoMLJob and CreateAutoMLJobV2 API: Execution Role Permissions
CreateDomain API: Execution Role Permissions
CreateImage and UpdateImage APIs: Execution Role Permissions
CreateNotebookInstance API: Execution Role Permissions
CreateHyperParameterTuningJob API: Execution Role Permissions
CreateProcessingJob API: Execution Role Permissions
CreateTrainingJob API: Execution Role Permissions
CreateModel API: Execution Role Permissions
SageMaker geospatial capabilities roles

CreateAutoMLJob and CreateAutoMLJobV2 API: Execution Role Permissions

For an execution role that you can pass in a CreateAutoMLJob or CreateAutoMLJobV2 API request, you can attach the following minimum permission policy to the role:

If you specify a private VPC for your AutoML job, add the following permissions:


{
    "Effect": "Allow",
    "Action": [
        "ec2:CreateNetworkInterface",
        "ec2:CreateNetworkInterfacePermission",
        "ec2:DeleteNetworkInterface",
        "ec2:DeleteNetworkInterfacePermission",
        "ec2:DescribeNetworkInterfaces",
        "ec2:DescribeVpcs",
        "ec2:DescribeDhcpOptions",
        "ec2:DescribeSubnets",
        "ec2:DescribeSecurityGroups"
    ]
}

If your input is encrypted using server-side encryption with an AWS KMS–managed key (SSE-KMS), add the following permissions:


{
    "Effect": "Allow",
    "Action": [
        "kms:Decrypt"
    ]
}

If you specify a KMS key in the output configuration of your AutoML job, add the following permissions:


{
    "Effect": "Allow",
    "Action": [
    "kms:Encrypt"
    ]
}

If you specify a volume KMS key in the resource configuration of your AutoML job, add the following permissions:


{
    "Effect": "Allow",
    "Action": [
    "kms:CreateGrant"
    ]
}

CreateDomain API: Execution Role Permissions

The execution role for domains with IAM Identity Center and the user/execution role for IAM domains need the following permissions when you pass an AWS KMS customer managed key as the KmsKeyId in the CreateDomain API request. The permissions are enforced during the CreateApp API call.

For an execution role that you can pass in the CreateDomain API request, you can attach the following permission policy to the role:

Alternatively, if the permissions are specified in a KMS policy, you can attach the following policy to the role:

CreateImage and UpdateImage APIs: Execution Role Permissions

For an execution role that you can pass in a CreateImage or UpdateImage API request, you can attach the following permission policy to the role:

CreateNotebookInstance API: Execution Role Permissions

The permissions that you grant to the execution role for calling the CreateNotebookInstance API depend on what you plan to do with the notebook instance. If you plan to use it to invoke SageMaker AI APIs and pass the same role when calling the CreateTrainingJob and CreateModel APIs, attach the following permissions policy to the role:

JSON


{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "sagemaker:*",
                "ecr:GetAuthorizationToken",
                "ecr:GetDownloadUrlForLayer",
                "ecr:BatchGetImage",
                "ecr:BatchCheckLayerAvailability",
                "ecr:SetRepositoryPolicy",
                "ecr:CompleteLayerUpload",
                "ecr:BatchDeleteImage",
                "ecr:UploadLayerPart",
                "ecr:DeleteRepositoryPolicy",
                "ecr:InitiateLayerUpload",
                "ecr:DeleteRepository",
                "ecr:PutImage",
                "ecr:CreateRepository",
                "cloudwatch:PutMetricData",
                "cloudwatch:GetMetricData",
                "cloudwatch:GetMetricStatistics",
                "cloudwatch:ListMetrics",
                "logs:CreateLogGroup",
                "logs:CreateLogStream",
                "logs:DescribeLogStreams",
                "logs:PutLogEvents",
                "logs:GetLogEvents",
                "s3:CreateBucket",
                "s3:ListBucket",
                "s3:GetBucketLocation",
                "s3:GetObject",
                "s3:PutObject",
                "s3:DeleteObject",
                "robomaker:CreateSimulationApplication",
                "robomaker:DescribeSimulationApplication",
                "robomaker:DeleteSimulationApplication",
                "robomaker:CreateSimulationJob",
                "robomaker:DescribeSimulationJob",
                "robomaker:CancelSimulationJob",
                "ec2:CreateVpcEndpoint",
                "ec2:DescribeRouteTables",
                "elasticfilesystem:DescribeMountTargets"
            ],
            "Resource": "*"
        },
        {
            "Effect": "Allow",
            "Action": [
                "codecommit:GitPull",
                "codecommit:GitPush"
            ],
            "Resource": [
                "arn:aws:codecommit:*:*:*sagemaker*",
                "arn:aws:codecommit:*:*:*SageMaker*",
                "arn:aws:codecommit:*:*:*Sagemaker*"
            ]
        },
        {
            "Effect": "Allow",
            "Action": [
                "iam:PassRole"
            ],
            "Resource": "*",
            "Condition": {
                "StringEquals": {
                    "iam:PassedToService": "sagemaker.amazonaws.com"
                }
            }
        }
    ]
}

To tighten the permissions, limit them to specific Amazon S3 and Amazon ECR resources, by restricting "Resource": "*", as follows:

JSON


{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "sagemaker:*",
                "ecr:GetAuthorizationToken",
                "cloudwatch:PutMetricData",
                "logs:CreateLogGroup",
                "logs:CreateLogStream",
                "logs:DescribeLogStreams",
                "logs:PutLogEvents",
                "logs:GetLogEvents"
            ],
            "Resource": "*"
        },
        {
            "Effect": "Allow",
            "Action": [
                "iam:PassRole"
            ],
            "Resource": "*",
            "Condition": {
                "StringEquals": {
                    "iam:PassedToService": "sagemaker.amazonaws.com"
                }
            }
        },
        {
            "Effect": "Allow",
            "Action": [
                "s3:ListBucket"
            ],
            "Resource": [
                "arn:aws:s3:::inputbucket"
            ]
        },
        {
            "Effect": "Allow",
            "Action": [
                "s3:GetObject",
                "s3:PutObject",
                "s3:DeleteObject"
            ],
            "Resource": [
                "arn:aws:s3:::inputbucket/object1",
                "arn:aws:s3:::outputbucket/path",
                "arn:aws:s3:::inputbucket/object2",
                "arn:aws:s3:::inputbucket/object3"
            ]
        },
        {
            "Effect": "Allow",
            "Action": [
                "ecr:BatchCheckLayerAvailability",
                "ecr:GetDownloadUrlForLayer",
                "ecr:BatchGetImage"
            ],
            "Resource": [
                "arn:aws:ecr:region::repository/my-repo1",
                "arn:aws:ecr:region::repository/my-repo2",
                "arn:aws:ecr:region::repository/my-repo3"
            ]
        }
    ]
}

If you plan to access other resources, such as Amazon DynamoDB or Amazon Relational Database Service, add the relevant permissions to this policy.

In the preceding policy, you scope the policy as follows:

Scope the s3:ListBucket permission to the specific bucket that you specify as InputDataConfig.DataSource.S3DataSource.S3Uri in a CreateTrainingJob request.
Scope s3:GetObject , s3:PutObject, and s3:DeleteObject permissions as follows:
- Scope to the following values that you specify in a CreateTrainingJob request:
  
  InputDataConfig.DataSource.S3DataSource.S3Uri
  
  OutputDataConfig.S3OutputPath
- Scope to the following values that you specify in a CreateModel request:
  
  PrimaryContainer.ModelDataUrl
  
  SuplementalContainers.ModelDataUrl
Scope ecr permissions as follows:
- Scope to the AlgorithmSpecification.TrainingImage value that you specify in a CreateTrainingJob request.
- Scope to the PrimaryContainer.Image value that you specify in a CreateModel request:

The cloudwatch and logs actions are applicable for "*" resources. For more information, see CloudWatch Resources and Operations in the Amazon CloudWatch User Guide.

CreateHyperParameterTuningJob API: Execution Role Permissions

For an execution role that you can pass in a CreateHyperParameterTuningJob API request, you can attach the following permission policy to the role:

Instead of the specifying "Resource": "*", you could scope these permissions to specific Amazon S3, Amazon ECR, and Amazon CloudWatch Logs resources:

If the training container associated with the hyperparameter tuning job needs to access other data sources, such as DynamoDB or Amazon RDS resources, add relevant permissions to this policy.

In the preceding policy, you scope the policy as follows:

Scope the s3:ListBucket permission to a specific bucket that you specify as the InputDataConfig.DataSource.S3DataSource.S3Uri in a CreateTrainingJob request.
Scope the s3:GetObject and s3:PutObject permissions to the following objects that you specify in the input and output data configuration in a CreateHyperParameterTuningJob request:

InputDataConfig.DataSource.S3DataSource.S3Uri

OutputDataConfig.S3OutputPath
Scope Amazon ECR permissions to the registry path (AlgorithmSpecification.TrainingImage) that you specify in a CreateHyperParameterTuningJob request.
Scope Amazon CloudWatch Logs permissions to log group of SageMaker training jobs.

The cloudwatch actions are applicable for "*" resources. For more information, see CloudWatch Resources and Operations in the Amazon CloudWatch User Guide.

If you specify a private VPC for your hyperparameter tuning job, add the following permissions:


{
    "Effect": "Allow",
    "Action": [
        "ec2:CreateNetworkInterface",
        "ec2:CreateNetworkInterfacePermission",
        "ec2:DeleteNetworkInterface",
        "ec2:DeleteNetworkInterfacePermission",
        "ec2:DescribeNetworkInterfaces",
        "ec2:DescribeVpcs",
        "ec2:DescribeDhcpOptions",
        "ec2:DescribeSubnets",
        "ec2:DescribeSecurityGroups"
    ]
}

If your input is encrypted using server-side encryption with an AWS KMS–managed key (SSE-KMS), add the following permissions:


{
    "Effect": "Allow",
    "Action": [
        "kms:Decrypt"
    ]
}

If you specify a KMS key in the output configuration of your hyperparameter tuning job, add the following permissions:


{
    "Effect": "Allow",
    "Action": [
    "kms:Encrypt"
    ]
}

If you specify a volume KMS key in the resource configuration of your hyperparameter tuning job, add the following permissions:


{
    "Effect": "Allow",
    "Action": [
    "kms:CreateGrant"
    ]
}

CreateProcessingJob API: Execution Role Permissions

For an execution role that you can pass in a CreateProcessingJob API request, you can attach the following permission policy to the role:

Instead of the specifying "Resource": "*", you could scope these permissions to specific Amazon S3 and Amazon ECR resources:

If CreateProcessingJob.AppSpecification.ImageUri needs to access other data sources, such as DynamoDB or Amazon RDS resources, add relevant permissions to this policy.

In the preceding policy, you scope the policy as follows:

Scope the s3:ListBucket permission to a specific bucket that you specify as the ProcessingInputs in a CreateProcessingJob request.
Scope the s3:GetObject and s3:PutObject permissions to the objects that will be downloaded or uploaded in the ProcessingInputs and ProcessingOutputConfig in a CreateProcessingJob request.
Scope Amazon ECR permissions to the registry path (AppSpecification.ImageUri) that you specify in a CreateProcessingJob request.

The cloudwatch and logs actions are applicable for "*" resources. For more information, see CloudWatch Resources and Operations in the Amazon CloudWatch User Guide.

If you specify a private VPC for your processing job, add the following permissions. Don't scope in the policy with any conditions or resource filters. Otherwise, the validation checks that occur during the creation of the processing job fail.


{
    "Effect": "Allow",
    "Action": [
        "ec2:CreateNetworkInterface",
        "ec2:CreateNetworkInterfacePermission",
        "ec2:DeleteNetworkInterface",
        "ec2:DeleteNetworkInterfacePermission",
        "ec2:DescribeNetworkInterfaces",
        "ec2:DescribeVpcs",
        "ec2:DescribeDhcpOptions",
        "ec2:DescribeSubnets",
        "ec2:DescribeSecurityGroups"
    ]
}

If your input is encrypted using server-side encryption with an AWS KMS–managed key (SSE-KMS), add the following permissions:


{
    "Effect": "Allow",
    "Action": [
        "kms:Decrypt"
    ]
}

If you specify a KMS key in the output configuration of your processing job, add the following permissions:


{
    "Effect": "Allow",
    "Action": [
    "kms:Encrypt"
    ]
}

If you specify a volume KMS key in the resource configuration of your processing job, add the following permissions:


{
    "Effect": "Allow",
    "Action": [
    "kms:CreateGrant"
    ]
}

CreateTrainingJob API: Execution Role Permissions

For an execution role that you can pass in a CreateTrainingJob API request, you can attach the following permission policy to the role:

Instead of the specifying "Resource": "*", you could scope these permissions to specific Amazon S3 and Amazon ECR resources:

If CreateTrainingJob.AlgorithSpecifications.TrainingImage needs to access other data sources, such as DynamoDB or Amazon RDS resources, add relevant permissions to this policy.

In the preceding policy, you scope the policy as follows:

Scope the s3:ListBucket permission to a specific bucket that you specify as the InputDataConfig.DataSource.S3DataSource.S3Uri in a CreateTrainingJob request.
Scope the s3:GetObject and s3:PutObject permissions to the following objects that you specify in the input and output data configuration in a CreateTrainingJob request:

InputDataConfig.DataSource.S3DataSource.S3Uri

OutputDataConfig.S3OutputPath
Scope Amazon ECR permissions to the registry path (AlgorithmSpecification.TrainingImage) that you specify in a CreateTrainingJob request.

The cloudwatch and logs actions are applicable for "*" resources. For more information, see CloudWatch Resources and Operations in the Amazon CloudWatch User Guide.

If you specify a private VPC for your training job, add the following permissions:


{
    "Effect": "Allow",
    "Action": [
      "ec2:CreateNetworkInterface",
      "ec2:CreateNetworkInterfacePermission",
      "ec2:DeleteNetworkInterface",
      "ec2:DeleteNetworkInterfacePermission",
      "ec2:DescribeNetworkInterfaces",
      "ec2:DescribeVpcs",
      "ec2:DescribeDhcpOptions",
      "ec2:DescribeSubnets",
      "ec2:DescribeSecurityGroups"
    ]
}

If your input is encrypted using server-side encryption with an AWS KMS–managed key (SSE-KMS), add the following permissions:


{
    "Effect": "Allow",
    "Action": [
        "kms:Decrypt"
    ]
}

If you specify a KMS key in the output configuration of your training job, add the following permissions:


{
    "Effect": "Allow",
    "Action": [
    "kms:Encrypt"
    ]
}

If you specify a volume KMS key in the resource configuration of your training job, add the following permissions:


{
    "Effect": "Allow",
    "Action": [
    "kms:CreateGrant"
    ]
}

CreateModel API: Execution Role Permissions

For an execution role that you can pass in a CreateModel API request, you can attach the following permission policy to the role:

Instead of the specifying "Resource": "*", you can scope these permissions to specific Amazon S3 and Amazon ECR resources:

If CreateModel.PrimaryContainer.Image need to access other data sources, such as Amazon DynamoDB or Amazon RDS resources, add relevant permissions to this policy.

In the preceding policy, you scope the policy as follows:

Scope S3 permissions to objects that you specify in the PrimaryContainer.ModelDataUrl in a CreateModel request.
Scope Amazon ECR permissions to a specific registry path that you specify as the PrimaryContainer.Image and SecondaryContainer.Image in a CreateModel request.

The cloudwatch and logs actions are applicable for "*" resources. For more information, see CloudWatch Resources and Operations in the Amazon CloudWatch User Guide.

Note

If you plan to use the SageMaker AI deployment guardrails feature for model deployment in production, ensure that your execution role has permission to perform the cloudwatch:DescribeAlarms action on your auto-rollback alarms.

If you specify a private VPC for your model, add the following permissions:


{
    "Effect": "Allow",
    "Action": [
        "ec2:CreateNetworkInterface",
        "ec2:CreateNetworkInterfacePermission",
        "ec2:DeleteNetworkInterface",
        "ec2:DeleteNetworkInterfacePermission",
        "ec2:DescribeNetworkInterfaces",
        "ec2:DescribeVpcs",
        "ec2:DescribeDhcpOptions",
        "ec2:DescribeSubnets",
        "ec2:DescribeSecurityGroups"
    ]
}

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Cross-service confused deputy prevention

SageMaker geospatial capabilities roles