Configure your pipeline - Amazon SageMaker AI

Configure your pipeline

You are advised to use the SageMaker AI config file to set the defaults for the pipeline. For information about the SageMaker AI configuration file, see Configuring and using defaults with the SageMaker Python SDK. Any configuration added to the config file applies to all steps in the pipeline. If you want to override options for any of the steps, provide new values in the @step decorator arguments. The following topic describes how to set up a config file.

The @step decorator's configuration in the config file is identical to the @remote decorator's configuration. To set up the pipeline role ARN and pipeline tags in the config file, use the Pipeline section shown in the following snippet:

SchemaVersion: '1.0'
SageMaker:
  Pipeline:
    RoleArn: 'arn:aws:iam::555555555555:role/IMRole'
    Tags:
    - Key: 'tag_key'
      Value: 'tag_value'

For most of the defaults you can set in the configuration file you can also override by passing new values to the @step decorator. For example, you can override the instance type set in the config file for your preprocessing step, as shown in the following example:

@step(instance_type="ml.m5.large")
def preprocess(raw_data):
    df = pandas.read_csv(raw_data)
    ...
    return procesed_dataframe

A few arguments are not part of the @step decorator parameters list—these can be configured for the entire pipeline only through the SageMaker AI configuration file. They are listed as follows:

  • sagemaker_session (sagemaker.session.Session): The underlying SageMaker AI session to which SageMaker AI delegates service calls. If unspecified, a session is created using a default configuration as follows:

    SageMaker:
  PythonSDK:
    Modules:
      Session:
        DefaultS3Bucket: 'default_s3_bucket'
        DefaultS3ObjectKeyPrefix: 'key_prefix'

  • custom_file_filter (CustomFileFilter): A CustomFileFilter object that specifies the local directories and files to include in the pipeline step. If unspecified, this value defaults to None. For custom_file_filter to take effect, you must set IncludeLocalWorkdir to True. The following example shows a configuration that ignores all notebook files, and files and directories named data.

    SchemaVersion: '1.0'
SageMaker:
  PythonSDK:
    Modules:
      RemoteFunction:
        IncludeLocalWorkDir: true
        CustomFileFilter: 
          IgnoreNamePatterns: # files or directories to ignore
          - "*.ipynb" # all notebook files
          - "data" # folder or file named "data"

    For more details about how to use IncludeLocalWorkdir with CustomFileFilter, see Using modular code with the @remote decorator.

  • s3_root_uri (str): The root Amazon S3 folder to which SageMaker AI uploads the code archives and data. If unspecified, the default SageMaker AI bucket is used.

  • s3_kms_key (str): The key used to encrypt the input and output data. You can only configure this argument in the SageMaker AI config file and the argument applies to all steps defined in the pipeline. If unspecified, the value defaults to None. See the following snippet for an example S3 KMS key configuration:

    SchemaVersion: '1.0'
SageMaker:
  PythonSDK:
    Modules:
      RemoteFunction:
        S3KmsKeyId: 's3kmskeyid'
        S3RootUri: 's3://amzn-s3-demo-bucket/my-project