Services or capabilities described in Amazon Web Services documentation might vary by Region. To see the differences applicable to the China Regions, see Getting Started with Amazon Web Services in China (PDF).

IAM Access Management

The following sections describe the Amazon Identity and Access Management (IAM) requirements for Amazon SageMaker Model Building Pipelines. For an example of how you can implement these permissions, see Prerequisites.

Pipeline Role Permissions

Your pipeline requires an IAM pipeline execution role that is passed to SageMaker Pipelines when you create a pipeline. The role for the SageMaker instance that is creating the pipeline must have the iam:PassRole permission for the pipeline execution role in order to pass it. For more information on IAM roles, see IAM Roles.

Your pipeline execution role requires the following permissions:

Pipeline Step Permissions

SageMaker Pipelines include steps that run SageMaker jobs. In order for the pipeline steps to run these jobs, they require an IAM role in your account that provides access for the needed resource. This role is passed to the SageMaker service principal by your pipeline. For more information on IAM roles, see IAM Roles.

By default, each step takes on the pipeline execution role. You can optionally pass a different role to any of the steps in your pipeline. This ensures that the code in each step does not have the ability to impact resources used in other steps unless there is a direct relationship between the two steps specified in the pipeline definition. You pass these roles when defining the processor or estimator for your step. For examples of how to include these roles in these definitions, see the SageMaker Python SDK documentation.

Customize access management for SageMaker Pipelines jobs

You can further customize your IAM policies so selected members in your organization can run any or all pipeline steps. For example, you can give certain users permission to create training jobs, and another group of users permission to create processing jobs, and all of your users permission to run the remaining steps. To use this feature, you select a custom string which prefixes your job name. Your admin prepends the permitted ARNs with the prefix while your data scientist includes this prefix in pipeline instantiations. Because the IAM policy for permitted users contains a job ARN with the specified prefix, subsequent jobs of your pipeline step have necessary permissions to proceed. Job prefixing is off by default—you must toggle on this option in your Pipeline class to use it.

For jobs with prefixing turned off, the job name is formatted as shown and is a concatenation of fields described in the following table:

pipelines-<executionId>-<stepNamePrefix>-<entityToken>-<failureCount>

In this case, no custom prefix is prepended to the job name, and the corresponding IAM policy must match this string.

For users who turn on job prefixing, the underlying job name takes the following form, with the custom prefix specified as MyBaseJobName:

<MyBaseJobName>-<executionId>-<entityToken>-<failureCount>

The custom prefix replaces the static pipelines string to help you narrow the selection of users who can run the SageMaker job as a part of a pipeline.

Prefix length restrictions

The job names have internal length constraints specific to individual pipeline steps. This constraint also limits the length of the allowed prefix. The prefix length requirements are as follows:

Apply job prefixes to an IAM policy

Your admin creates IAM policies allowing users of specific prefixes to create jobs. The following example policy permits data scientists to create training jobs if they use the MyBaseJobName prefix.

{
    "Action": "sagemaker:CreateTrainingJob",
    "Effect": "Allow",
    "Resource": [
        "arn:aws:sagemaker:region:account-id:*/MyBaseJobName-*"
    ]
}

Apply job prefixes to pipeline instantiations

You specify your prefix with the *base_job_name argument of the job instance class.

The following example shows how to specify a prefix for a new training job instance.

# Create a job instance
xgb_train = Estimator(
    image_uri=image_uri,
    instance_type="ml.m5.xlarge",
    instance_count=1,
    output_path=model_path,
    role=role,
    subnets=["subnet-0ab12c34567de89f0"],
    base_job_name="MyBaseJobName"
    security_group_ids=["sg-1a2bbcc3bd4444e55"],
    tags = [ ... ]
    encrypt_inter_container_traffic=True, 
)

# Attach your job instance to a pipeline step
step_train = TrainingStep(
    name="TestTrainingJob",
    estimator=xgb_train, 
    inputs={
        "train": TrainingInput(...), 
        "validation": TrainingInput(...) 
    }
)

Job prefixing is off by default. To opt into this feature, use the use_custom_job_prefix option of PipelineDefinitionConfig as shown in the following snippet:

from sagemaker.workflow.pipeline_definition_config import PipelineDefinitionConfig
        
# Create a definition configuration and toggle on custom prefixing
definition_config = PipelineDefinitionConfig(use_custom_job_prefix=True);

# Create a pipeline with a custom prefix
 pipeline = Pipeline(
     name="MyJobPrefixedPipeline",
     parameters=[...]
     steps=[...]
     pipeline_definition_config=definition_config
)

Create and run your pipeline. The following example creates and runs a pipeline, and also demonstrates how you can turn off job prefixing and rerun your pipeline.

pipeline.create(role_arn=sagemaker.get_execution_role())

# Optionally, call definition() to confirm your prefixed job names are in the built JSON
pipeline.definition()
pipeline.start()
      
# To run a pipeline without custom-prefixes, toggle off use_custom_job_prefix, update the pipeline 
# via upsert() or update(), and start a new run
definition_config = PipelineDefinitionConfig(use_custom_job_prefix=False)
pipeline.pipeline_definition_config = definition_config
pipeline.update()
execution = pipeline.start()

Similarly, you can toggle the feature on for existing pipelines and start a new run which uses job prefixes.

definition_config = PipelineDefinitionConfig(use_custom_job_prefix=True)
pipeline.pipeline_definition_config = definition_config
pipeline.update()
execution = pipeline.start()

Finally, you can view your custom-prefixed job by calling list_steps on the pipeline execution.

steps = execution.list_steps()

prefixed_training_job_name = steps['PipelineExecutionSteps'][0]['Metadata']['TrainingJob']['Arn']

Service Control Policies with Pipelines

Service control policies (SCPs) are a type of organization policy that you can use to manage permissions in your organization. SCPs offer central control over the maximum available permissions for all accounts in your organization. By using SageMaker Pipelines within your organization, you can ensure that data scientists manage your pipeline executions without having to interact with the Amazon console. 

If you're using a VPC with your SCP that restricts access to Amazon S3, you need to take steps to allow your pipeline to access other Amazon S3 resources.

To allow SageMaker Pipelines to access Amazon S3 outside of your VPC with the JsonGet function, update your organization's SCP to ensure that the role using SageMaker Pipelines can access Amazon S3. To do this, create an exception for roles that are being used by the SageMaker Pipelines executor via the pipeline execution role using a principal tag and condition key.