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 accept header passed by the client to the inference endpoint. If it is supported by the endpoint, it will be the format of the batch transform output.

  • 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.

  • tags (list[dict]) – List of tags for labeling a transform job (default: None). For more, see the SageMaker API documentation for Tag.

  • env (dict) – Environment variables to be set for use during the transform job (default: None).

  • 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(**kwargs)
delete_model()

Delete the corresponding SageMaker model for this Transformer.

wait(logs=True)

Placeholder docstring

stop_transform_job(wait=True)

Stop latest running batch transform job.

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