Transformer

class sagemaker.transformer.Transformer(model_name, instance_count, instance_type, strategy=None, assemble_with=None, output_path=None, output_kms_key=None, accept=None, max_concurrent_transforms=None, max_payload=None, tags=None, env=None, base_transform_job_name=None, sagemaker_session=None, volume_kms_key=None)

Bases: object

A class for handling creating and interacting with Amazon SageMaker transform jobs.

Initialize a Transformer.

Parameters:
  • model_name (str) – Name of the SageMaker model being used for the transform job.
  • instance_count (int) – Number of EC2 instances to use.
  • instance_type (str) – Type of EC2 instance to use, for example, ‘ml.c4.xlarge’.
  • strategy (str) – The strategy used to decide how to batch records in a single request (default: None). Valid values: ‘MultiRecord’ and ‘SingleRecord’.
  • assemble_with (str) – How the output is assembled (default: None). Valid values: ‘Line’ or ‘None’.
  • output_path (str) – S3 location for saving the transform result. If not specified, results are stored to a default bucket.
  • output_kms_key (str) – Optional. KMS key ID for encrypting the transform output (default: None).
  • accept (str) – The content type accepted by the endpoint deployed during the transform job.
  • max_concurrent_transforms (int) – The maximum number of HTTP requests to be made to each individual transform container at one time.
  • max_payload (int) – Maximum size of the payload in a single HTTP request to the container in MB.
  • env (dict) – Environment variables to be set for use during the transform job (default: None).
  • tags (list[dict]) – List of tags for labeling a transform job (default: None). For more, see https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.
  • base_transform_job_name (str) – Prefix for the transform job when the transform() method launches. If not specified, a default prefix will be generated based on the training image name that was used to train the model associated with the transform job.
  • sagemaker_session (sagemaker.session.Session) – Session object which manages interactions with Amazon SageMaker APIs and any other AWS services needed. If not specified, the estimator creates one using the default AWS configuration chain.
  • volume_kms_key (str) – Optional. KMS key ID for encrypting the volume attached to the ML compute instance (default: None).
transform(data, data_type='S3Prefix', content_type=None, compression_type=None, split_type=None, job_name=None)

Start a new transform job.

Parameters:
  • data (str) – Input data location in S3.
  • data_type (str) –

    What the S3 location defines (default: ‘S3Prefix’). Valid values:

    • ’S3Prefix’ - the S3 URI defines a key name prefix. All objects with this prefix will be used as
      inputs for the transform job.
    • ’ManifestFile’ - the S3 URI points to a single manifest file listing each S3 object to use as
      an input for the transform job.
  • content_type (str) – MIME type of the input data (default: None).
  • compression_type (str) – Compression type of the input data, if compressed (default: None). Valid values: ‘Gzip’, None.
  • split_type (str) – The record delimiter for the input object (default: ‘None’). Valid values: ‘None’, ‘Line’, ‘RecordIO’, and ‘TFRecord’.
  • job_name (str) – job name (default: None). If not specified, one will be generated.
delete_model()

Delete the corresponding SageMaker model for this Transformer.

wait()
classmethod attach(transform_job_name, sagemaker_session=None)

Attach an existing transform job to a new Transformer instance

Parameters:
  • transform_job_name (str) – Name for the transform job to be attached.
  • sagemaker_session (sagemaker.session.Session) – Session object which manages interactions with Amazon SageMaker APIs and any other AWS services needed. If not specified, one will be created using the default AWS configuration chain.
Returns:

The Transformer instance with the specified transform job attached.

Return type:

sagemaker.transformer.Transformer