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#
# Licensed under the Apache License, Version 2.0 (the "License"). You
# may not use this file except in compliance with the License. A copy of
# the License is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "license" file accompanying this file. This file is
# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
# ANY KIND, either express or implied. See the License for the specific
# language governing permissions and limitations under the License.
"""This module contains code related to HuggingFace Processors which are used for Processing jobs.
These jobs let customers perform data pre-processing, post-processing, feature engineering,
data validation, and model evaluation and interpretation on SageMaker.
"""
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
class HuggingFaceProcessor(FrameworkProcessor):
"""Handles Amazon SageMaker processing tasks for jobs using HuggingFace containers."""
estimator_cls = HuggingFace
def __init__(
self,
role: str,
instance_count: Union[int, PipelineVariable],
instance_type: Union[str, PipelineVariable],
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[List[Dict[str, Union[str, PipelineVariable]]]] = 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 ``1.6.0``.
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 ``2.4.1``.
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,
tags,
network_config,
)
def _create_estimator(
self,
entry_point="",
source_dir=None,
dependencies=None,
git_config=None,
):
"""Override default estimator factory function for HuggingFace's different parameters
HuggingFace estimators have 3 framework version parameters instead of one: The version for
Transformers, PyTorch, and TensorFlow.
"""
return self.estimator_cls(
transformers_version=self.framework_version,
tensorflow_version=self.tensorflow_version,
pytorch_version=self.pytorch_version,
py_version=self.py_version,
entry_point=entry_point,
source_dir=source_dir,
dependencies=dependencies,
git_config=git_config,
code_location=self.code_location,
enable_network_isolation=False,
image_uri=self.image_uri,
role=self.role,
instance_count=self.instance_count,
instance_type=self.instance_type,
sagemaker_session=self.sagemaker_session,
debugger_hook_config=False,
disable_profiler=True,
)
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