class_docstr: Handles Amazon SageMaker processing tasks for jobs using HuggingFace containers.
class_docstr_tok: ["Handles","Amazon","SageMaker","processing","tasks","for","jobs","using","HuggingFace","containers","."]
docstr: This processor executes a Python script in a HuggingFace execution environment.
  
  Unless ``image_uri`` is specified, the environment is an Amazon-built Docker container
  that executes functions defined in the supplied ``code`` Python script.
  
  The arguments have the same meaning as in ``FrameworkProcessor``, with the following
  exceptions.
  
  Args:
      transformers_version (str): Transformers version you want to use for
          executing your model training code. Defaults to ``None``. Required unless
          ``image_uri`` is provided. The current supported version is ``4.4.2``.
      tensorflow_version (str): TensorFlow version you want to use for
          executing your model training code. Defaults to ``None``. Required unless
          ``pytorch_version`` is provided. The current supported version is ``2.4.1``.
      pytorch_version (str): PyTorch version you want to use for
          executing your model training code. Defaults to ``None``. Required unless
          ``tensorflow_version`` is provided. The current supported version is ``1.6.0``.
      py_version (str): Python version you want to use for executing your model training
          code. Defaults to ``None``. Required unless ``image_uri`` is provided.  If
          using PyTorch, the current supported version is ``py36``. If using TensorFlow,
          the current supported version is ``py37``.
  
  .. tip::
  
      You can find additional parameters for initializing this class at
      :class:`~sagemaker.processing.FrameworkProcessor`.
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code: def __init__(
      self,
      role: Optional[Union[str, PipelineVariable]] = None,
      instance_count: Union[int, PipelineVariable] = None,
      instance_type: Union[str, PipelineVariable] = None,
      transformers_version: Optional[str] = None,
      tensorflow_version: Optional[str] = None,
      pytorch_version: Optional[str] = None,
      py_version: str = "py36",
      image_uri: Optional[Union[str, PipelineVariable]] = None,
      command: Optional[List[str]] = None,
      volume_size_in_gb: Union[int, PipelineVariable] = 30,
      volume_kms_key: Optional[Union[str, PipelineVariable]] = None,
      output_kms_key: Optional[Union[str, PipelineVariable]] = None,
      code_location: Optional[str] = None,
      max_runtime_in_seconds: Optional[
          Union[int, PipelineVariable]
      ] = None,
      base_job_name: Optional[str] = None,
      sagemaker_session: Optional[Session] = None,
      env: Optional[Dict[str, Union[str, PipelineVariable]]] = None,
      tags: Optional[Tags] = None,
      network_config: Optional[NetworkConfig] = None,
  ):
      """This processor executes a Python script in a HuggingFace execution environment.
  
      Unless ``image_uri`` is specified, the environment is an Amazon-built Docker container
      that executes functions defined in the supplied ``code`` Python script.
  
      The arguments have the same meaning as in ``FrameworkProcessor``, with the following
      exceptions.
  
      Args:
          transformers_version (str): Transformers version you want to use for
              executing your model training code. Defaults to ``None``. Required unless
              ``image_uri`` is provided. The current supported version is ``4.4.2``.
          tensorflow_version (str): TensorFlow version you want to use for
              executing your model training code. Defaults to ``None``. Required unless
              ``pytorch_version`` is provided. The current supported version is ``2.4.1``.
          pytorch_version (str): PyTorch version you want to use for
              executing your model training code. Defaults to ``None``. Required unless
              ``tensorflow_version`` is provided. The current supported version is ``1.6.0``.
          py_version (str): Python version you want to use for executing your model training
              code. Defaults to ``None``. Required unless ``image_uri`` is provided.  If
              using PyTorch, the current supported version is ``py36``. If using TensorFlow,
              the current supported version is ``py37``.
  
      .. tip::
  
          You can find additional parameters for initializing this class at
          :class:`~sagemaker.processing.FrameworkProcessor`.
      """
      self.pytorch_version = pytorch_version
      self.tensorflow_version = tensorflow_version
      super().__init__(
          self.estimator_cls,
          transformers_version,
          role,
          instance_count,
          instance_type,
          py_version,
          image_uri,
          command,
          volume_size_in_gb,
          volume_kms_key,
          output_kms_key,
          code_location,
          max_runtime_in_seconds,
          base_job_name,
          sagemaker_session,
          env,
          format_tags(tags),
          network_config,
      )
  
file_path: sagemaker-python-sdk/src/sagemaker/huggingface/processing.py
imports: from __future__ import absolute_import
  from typing import Union, Optional, List, Dict
  from sagemaker.session import Session
  from sagemaker.network import NetworkConfig
  from sagemaker.processing import FrameworkProcessor
  from sagemaker.huggingface.estimator import HuggingFace
  from sagemaker.workflow.entities import PipelineVariable
  from sagemaker.utils import format_tags, Tags
label_desc: Use image node_id 1 to create a new HuggingFaceProcessor object from inherited base classes: FrameworkProcessor with example: obj = HuggingFaceProcessor(role, instance_count, instance_type, transformers_version, tensorflow_version, pytorch_version, py_version, image_uri, command, volume_size_in_gb, volume_kms_key, output_kms_key, code_location, max_runtime_in_seconds, base_job_name, sagemaker_session, env, tags, network_config)