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AWS Documentation » Amazon SageMaker » Developer Guide » Authentication and Access Control for Amazon SageMaker » Amazon SageMaker Roles

Amazon SageMaker Roles

As a managed service, Amazon SageMaker performs operations on your behalf on the AWS hardware that is managed by Amazon SageMaker. Amazon SageMaker can perform only operations that the user permits.

An Amazon SageMaker user can grant these permissions with an IAM role (referred to as an execution role). The user passes the role when making these API calls: CreateNotebookInstance, CreateHyperParameterTuningJob, CreateTrainingJob, and CreateModel.

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

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Principal": {
                "Service": "sagemaker.amazonaws.com"
            },
            "Action": "sts:AssumeRole"
        }
    ]
}

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 well scoped to the actions you need to perform. For more information see Using the AWS Managed Permission Policy (AmazonSageMakerFullAccess) for an Execution Role. If you prefer to create custom policies and manage permissions, see the following topics.

For more information about IAM roles, see IAM Roles in the IAM User Guide.

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 Amazon SageMaker APIs and pass the same role when calling the CreateTrainingJob and CreateModel APIs, attach the following permissions policy to the role: 

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "sagemaker:*",
                "ecr:GetAuthorizationToken",
                "ecr:GetDownloadUrlForLayer",
                "ecr:BatchGetImage",
                "ecr:BatchCheckLayerAvailability",
                "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"
            ],
            "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 replacing "Resource": "*", as follows: 

{
    "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:::repository/my-repo1",
                "arn:aws:ecr:::repository/my-repo2",
                "arn:aws:ecr:::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: 

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "cloudwatch:PutMetricData",
                "logs:CreateLogStream",
                "logs:PutLogEvents",
                "logs:CreateLogGroup",
                "logs:DescribeLogStreams",
                "s3:GetObject",
                "s3:PutObject",
                "s3:ListBucket",
                "ecr:GetAuthorizationToken",
                "ecr:BatchCheckLayerAvailability",
                "ecr:GetDownloadUrlForLayer",
                "ecr:BatchGetImage"
            ],
            "Resource": "*"
        }
    ]
}

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

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "cloudwatch:PutMetricData",
                "logs:CreateLogStream",
                "logs:PutLogEvents",
                "logs:CreateLogGroup",
                "logs:DescribeLogStreams",
                "ecr:GetAuthorizationToken"
            ],
            "Resource": "*"
        },
        {
            "Effect": "Allow",
            "Action": [
                "s3:ListBucket"
            ],
            "Resource": [
                "arn:aws:s3:::inputbucket"
            ]
        },
        {
            "Effect": "Allow",
            "Action": [
                "s3:GetObject",
                "s3:PutObject"
            ],
            "Resource": [
                "arn:aws:s3:::inputbucket/object",
                "arn:aws:s3:::outputbucket/path"
            ]
        },
        {
            "Effect": "Allow",
            "Action": [
                "ecr:BatchCheckLayerAvailability",
                "ecr:GetDownloadUrlForLayer",
                "ecr:BatchGetImage"
            ],
            "Resource": "arn:aws:ecr:::repository/my-repo"
        }
    ]
}

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.

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 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"

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: 

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "cloudwatch:PutMetricData",
                "logs:CreateLogStream",
                "logs:PutLogEvents",
                "logs:CreateLogGroup",
                "logs:DescribeLogStreams",
                "s3:GetObject",
                "s3:PutObject",
                "s3:ListBucket",
                "ecr:GetAuthorizationToken",
                "ecr:BatchCheckLayerAvailability",
                "ecr:GetDownloadUrlForLayer",
                "ecr:BatchGetImage"
            ],
            "Resource": "*"
        }
    ]
}

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

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "cloudwatch:PutMetricData",
                "logs:CreateLogStream",
                "logs:PutLogEvents",
                "logs:CreateLogGroup",
                "logs:DescribeLogStreams",
                "ecr:GetAuthorizationToken"
            ],
            "Resource": "*"
        },
        {
            "Effect": "Allow",
            "Action": [
                "s3:ListBucket"
            ],
            "Resource": [
                "arn:aws:s3:::inputbucket"
            ]
        },
        {
            "Effect": "Allow",
            "Action": [
                "s3:GetObject",
                "s3:PutObject"
            ],
            "Resource": [
                "arn:aws:s3:::inputbucket/object",
                "arn:aws:s3:::outputbucket/path"
            ]
        },
        {
            "Effect": "Allow",
            "Action": [
                "ecr:BatchCheckLayerAvailability",
                "ecr:GetDownloadUrlForLayer",
                "ecr:BatchGetImage"
            ],
            "Resource": "arn:aws:ecr:::repository/my-repo"
        }
    ]
}

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"

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: 

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "cloudwatch:PutMetricData",
                "logs:CreateLogStream",
                "logs:PutLogEvents",
                "logs:CreateLogGroup",
                "logs:DescribeLogStreams",
                "s3:GetObject",
                "ecr:GetAuthorizationToken",
                "ecr:BatchCheckLayerAvailability",
                "ecr:GetDownloadUrlForLayer",
                "ecr:BatchGetImage"
            ],
            "Resource": "*"
        }
    ]
}

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

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "cloudwatch:PutMetricData",
                "logs:CreateLogStream",
                "logs:PutLogEvents",
                "logs:CreateLogGroup",
                "logs:DescribeLogStreams",
                "ecr:GetAuthorizationToken"
            ],
            "Resource": "*"
        },
        {
            "Effect": "Allow",
            "Action": [
                "s3:GetObject"
            ],
            "Resource": [
                "arn:aws:s3:::inputbucket/object",
                "arn:aws:s3:::inputbucket/object"
            ]
        },
        {
            "Effect": "Allow",
            "Action": [
                "ecr:BatchCheckLayerAvailability",
                "ecr:GetDownloadUrlForLayer",
                "ecr:BatchGetImage"
            ],
            "Resource": [
                "arn:aws:ecr:::repository/my-repo",
                "arn:aws:ecr:::repository/my-repo"
             ]
        }
    ]
}

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.

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"

Using the AWS Managed Permission Policy (AmazonSageMakerFullAccess) for an Execution Role

You can create an execution role one of two ways:

  • In the Amazon SageMaker console when you create a notebook instance, training job, or model.

  • In the AWS Identity and Access Management (IAM) console. You then specify the role as you follow the notebook instance, training job, and model creation workflows in the Amazon SageMaker console.

Regardless of how you create an execution role, you can attach the AWS-managed permission policy (AmazonSageMakerFullAccess) to the role.

When attaching the AmazonSageMakerFullAccess policy to a role, you must do one of the following to allow Amazon SageMaker to access your S3 bucket:

  • Include the string "SageMaker" or "sagemaker" in the name of the bucket where you store training data, or the model artifacts resulting from model training, or both.

  • Include the string "SageMaker" or "sagemaker" in the object name of the training data object(s).

  • Tag the S3 object with "sagemaker=true". The key and value are case sensitive. For more information, see Object Tagging in the Amazon Simple Storage Service Developer Guide.

  • Add a bucket policy that allows access for the execution role. For more information, see Using Bucket Policies and User Policies in the Amazon Simple Storage Service Developer Guide.

You can attach additional policies that specify the resources for which you want to grant permissions for the s3:GetObject, s3:PutObject, and s3:ListBucket actions. In the IAM console, you can attach a customer managed policy or an inline policy to your execution role(s). Alternatively, when you create a role in the Amazon SageMaker console, you can attach a customer managed policy that specifies the S3 buckets. This resulting execution role has the prefix "AmazonSageMaker-ExecutionRole-".