def test_renamed_kwargs():
kwargs, c = {"a": 1}, 2
val = renamed_kwargs("b", new_name="c", value=c, kwargs=kwargs)
assert val == 2
kwargs, c = {"a": 1, "c": 2}, 2
val = renamed_kwargs("b", new_name="c", value=c, kwargs=kwargs)
assert val == 2
with pytest.warns(DeprecationWarning):
kwargs, c = {"a": 1, "b": 3}, 2
val = renamed_kwargs("b", new_name="c", value=c, kwargs=kwargs)
assert val == 3
assert kwargs == {"a": 1, "b": 3, "c": 3}
def __init__(
self,
endpoint_name,
sagemaker_session=None,
serializer=IdentitySerializer(),
deserializer=BytesDeserializer(),
**kwargs,
):
"""Initialize a ``Predictor``.
Behavior for serialization of input data and deserialization of
result data can be configured through initializer arguments. If not
specified, a sequence of bytes is expected and the API sends it in the
request body without modifications. In response, the API returns the
sequence of bytes from the prediction result without any modifications.
Args:
endpoint_name (str): Name of the Amazon SageMaker endpoint to which
requests are sent.
sagemaker_session (sagemaker.session.Session): A SageMaker Session
object, used for SageMaker interactions (default: None). If not
specified, one is created using the default AWS configuration
chain.
serializer (:class:`~sagemaker.serializers.BaseSerializer`): A
serializer object, used to encode data for an inference endpoint
(default: :class:`~sagemaker.serializers.IdentitySerializer`).
deserializer (:class:`~sagemaker.deserializers.BaseDeserializer`): A
deserializer object, used to decode data from an inference
endpoint (default: :class:`~sagemaker.deserializers.BytesDeserializer`).
"""
removed_kwargs("content_type", kwargs)
removed_kwargs("accept", kwargs)
endpoint_name = renamed_kwargs("endpoint", "endpoint_name",
endpoint_name, kwargs)
self.endpoint_name = endpoint_name
self.sagemaker_session = sagemaker_session or Session()
self.serializer = serializer
self.deserializer = deserializer
self._endpoint_config_name = self._get_endpoint_config_name()
self._model_names = self._get_model_names()
self._context = None
def __init__(
self,
py_version,
entry_point,
transformers_version=None,
tensorflow_version=None,
pytorch_version=None,
source_dir=None,
hyperparameters=None,
image_uri=None,
distribution=None,
**kwargs
):
"""This ``Estimator`` executes a HuggingFace script in a managed execution environment.
The managed HuggingFace environment is an Amazon-built Docker container that executes
functions defined in the supplied ``entry_point`` Python script within a SageMaker
Training Job.
Training is started by calling
:meth:`~sagemaker.amazon.estimator.Framework.fit` on this Estimator.
Args:
py_version (str): Python version you want to use for executing your model training
code. Defaults to ``None``. Required unless ``image_uri`` is provided. List
of supported versions:
https://github.com/aws/sagemaker-python-sdk#huggingface-sagemaker-estimators
entry_point (str): Path (absolute or relative) to the Python source
file which should be executed as the entry point to training.
If ``source_dir`` is specified, then ``entry_point``
must point to a file located at the root of ``source_dir``.
transformers_version (str): Transformers version you want to use for
executing your model training code. Defaults to ``None``. Required unless
``image_uri`` is provided. List of supported versions:
https://github.com/aws/sagemaker-python-sdk#huggingface-sagemaker-estimators.
tensorflow_version (str): TensorFlow version you want to use for
executing your model training code. Defaults to ``None``. Required unless
``pytorch_version`` is provided. List of supported versions:
https://github.com/aws/sagemaker-python-sdk#huggingface-sagemaker-estimators.
pytorch_version (str): PyTorch version you want to use for
executing your model training code. Defaults to ``None``. Required unless
``tensorflow_version`` is provided. List of supported versions:
https://github.com/aws/sagemaker-python-sdk#huggingface-sagemaker-estimators.
source_dir (str): Path (absolute, relative or an S3 URI) to a directory
with any other training source code dependencies aside from the entry
point file (default: None). If ``source_dir`` is an S3 URI, it must
point to a tar.gz file. Structure within this directory are preserved
when training on Amazon SageMaker.
hyperparameters (dict): Hyperparameters that will be used for
training (default: None). The hyperparameters are made
accessible as a dict[str, str] to the training code on
SageMaker. For convenience, this accepts other types for keys
and values, but ``str()`` will be called to convert them before
training.
image_uri (str): If specified, the estimator will use this image
for training and hosting, instead of selecting the appropriate
SageMaker official image based on framework_version and
py_version. It can be an ECR url or dockerhub image and tag.
Examples:
* ``123412341234.dkr.ecr.us-west-2.amazonaws.com/my-custom-image:1.0``
* ``custom-image:latest``
If ``framework_version`` or ``py_version`` are ``None``, then
``image_uri`` is required. If also ``None``, then a ``ValueError``
will be raised.
distribution (dict): A dictionary with information on how to run distributed training
(default: None). Currently, the following are supported:
distributed training with parameter servers, SageMaker Distributed (SMD) Data
and Model Parallelism, and MPI. SMD Model Parallelism can only be used with MPI.
To enable parameter server use the following setup:
.. code:: python
{
"parameter_server": {
"enabled": True
}
}
To enable MPI:
.. code:: python
{
"mpi": {
"enabled": True
}
}
To enable SMDistributed Data Parallel or Model Parallel:
.. code:: python
{
"smdistributed": {
"dataparallel": {
"enabled": True
},
"modelparallel": {
"enabled": True,
"parameters": {}
}
}
}
**kwargs: Additional kwargs passed to the :class:`~sagemaker.estimator.Framework`
constructor.
.. tip::
You can find additional parameters for initializing this class at
:class:`~sagemaker.estimator.Framework` and
:class:`~sagemaker.estimator.EstimatorBase`.
"""
self.framework_version = transformers_version
self.py_version = py_version
self.tensorflow_version = tensorflow_version
self.pytorch_version = pytorch_version
self._validate_args(image_uri=image_uri)
if distribution is not None:
instance_type = renamed_kwargs(
"train_instance_type", "instance_type", kwargs.get("instance_type"), kwargs
)
base_framework_name = "tensorflow" if tensorflow_version is not None else "pytorch"
base_framework_version = (
tensorflow_version if tensorflow_version is not None else pytorch_version
)
validate_smdistributed(
instance_type=instance_type,
framework_name=base_framework_name,
framework_version=base_framework_version,
py_version=self.py_version,
distribution=distribution,
image_uri=image_uri,
)
warn_if_parameter_server_with_multi_gpu(
training_instance_type=instance_type, distribution=distribution
)
if "enable_sagemaker_metrics" not in kwargs:
kwargs["enable_sagemaker_metrics"] = True
super(HuggingFace, self).__init__(
entry_point, source_dir, hyperparameters, image_uri=image_uri, **kwargs
)
self.distribution = distribution or {}
def __init__(self,
py_version=None,
framework_version=None,
model_dir=None,
image_uri=None,
distribution=None,
**kwargs):
"""Initialize a ``TensorFlow`` estimator.
Args:
py_version (str): Python version you want to use for executing your model training
code. Defaults to ``None``. Required unless ``image_uri`` is provided.
framework_version (str): TensorFlow version you want to use for executing your model
training code. Defaults to ``None``. Required unless ``image_uri`` is provided.
List of supported versions:
https://github.com/aws/sagemaker-python-sdk#tensorflow-sagemaker-estimators.
model_dir (str): S3 location where the checkpoint data and models can be exported to
during training (default: None). It will be passed in the training script as one of
the command line arguments. If not specified, one is provided based on
your training configuration:
* *distributed training with SMDistributed or MPI with Horovod* - ``/opt/ml/model``
* *single-machine training or distributed training without MPI* - \
``s3://{output_path}/model``
* *Local Mode with local sources (file:// instead of s3://)* - \
``/opt/ml/shared/model``
To disable having ``model_dir`` passed to your training script,
set ``model_dir=False``.
image_uri (str): If specified, the estimator will use this image for training and
hosting, instead of selecting the appropriate SageMaker official image based on
framework_version and py_version. It can be an ECR url or dockerhub image and tag.
Examples:
123.dkr.ecr.us-west-2.amazonaws.com/my-custom-image:1.0
custom-image:latest.
If ``framework_version`` or ``py_version`` are ``None``, then
``image_uri`` is required. If also ``None``, then a ``ValueError``
will be raised.
distribution (dict): A dictionary with information on how to run distributed training
(default: None). Currently, the following are supported:
distributed training with parameter servers, SageMaker Distributed (SMD) Data
and Model Parallelism, and MPI. SMD Model Parallelism can only be used with MPI.
To enable parameter server use the following setup:
.. code:: python
{
"parameter_server": {
"enabled": True
}
}
To enable MPI:
.. code:: python
{
"mpi": {
"enabled": True
}
}
To enable SMDistributed Data Parallel or Model Parallel:
.. code:: python
{
"smdistributed": {
"dataparallel": {
"enabled": True
},
"modelparallel": {
"enabled": True,
"parameters": {}
}
}
}
**kwargs: Additional kwargs passed to the Framework constructor.
.. tip::
You can find additional parameters for initializing this class at
:class:`~sagemaker.estimator.Framework` and
:class:`~sagemaker.estimator.EstimatorBase`.
"""
distribution = renamed_kwargs("distributions", "distribution",
distribution, kwargs)
instance_type = renamed_kwargs("train_instance_type", "instance_type",
kwargs.get("instance_type"), kwargs)
fw.validate_version_or_image_args(framework_version, py_version,
image_uri)
if py_version == "py2":
logger.warning(
fw.python_deprecation_warning(self._framework_name,
defaults.LATEST_PY2_VERSION))
self.framework_version = framework_version
self.py_version = py_version
self.instance_type = instance_type
if distribution is not None:
fw.warn_if_parameter_server_with_multi_gpu(
training_instance_type=instance_type,
distribution=distribution)
fw.validate_smdistributed(
instance_type=instance_type,
framework_name=self._framework_name,
framework_version=framework_version,
py_version=py_version,
distribution=distribution,
image_uri=image_uri,
)
if "enable_sagemaker_metrics" not in kwargs:
# enable sagemaker metrics for TF v1.15 or greater:
if framework_version and version.Version(
framework_version) >= version.Version("1.15"):
kwargs["enable_sagemaker_metrics"] = True
super(TensorFlow, self).__init__(image_uri=image_uri, **kwargs)
self.model_dir = model_dir
self.distribution = distribution or {}
self._validate_args(py_version=py_version)
def __init__(self,
entry_point,
framework_version=None,
py_version=None,
source_dir=None,
hyperparameters=None,
image_uri=None,
distribution=None,
**kwargs):
"""This ``Estimator`` executes an PyTorch script in a managed PyTorch
execution environment, within a SageMaker Training Job. The managed
PyTorch environment is an Amazon-built Docker container that executes
functions defined in the supplied ``entry_point`` Python script.
Training is started by calling
:meth:`~sagemaker.amazon.estimator.Framework.fit` on this Estimator.
After training is complete, calling
:meth:`~sagemaker.amazon.estimator.Framework.deploy` creates a hosted
SageMaker endpoint and returns an
:class:`~sagemaker.amazon.pytorch.model.PyTorchPredictor` instance that
can be used to perform inference against the hosted model.
Technical documentation on preparing PyTorch scripts for SageMaker
training and using the PyTorch Estimator is available on the project
home-page: https://github.com/aws/sagemaker-python-sdk
Args:
entry_point (str): Path (absolute or relative) to the Python source
file which should be executed as the entry point to training.
If ``source_dir`` is specified, then ``entry_point``
must point to a file located at the root of ``source_dir``.
framework_version (str): PyTorch version you want to use for
executing your model training code. Defaults to ``None``. Required unless
``image_uri`` is provided. List of supported versions:
https://github.com/aws/sagemaker-python-sdk#pytorch-sagemaker-estimators.
py_version (str): Python version you want to use for executing your
model training code. One of 'py2' or 'py3'. Defaults to ``None``. Required
unless ``image_uri`` is provided.
source_dir (str): Path (absolute, relative or an S3 URI) to a directory
with any other training source code dependencies aside from the entry
point file (default: None). If ``source_dir`` is an S3 URI, it must
point to a tar.gz file. Structure within this directory are preserved
when training on Amazon SageMaker.
hyperparameters (dict): Hyperparameters that will be used for
training (default: None). The hyperparameters are made
accessible as a dict[str, str] to the training code on
SageMaker. For convenience, this accepts other types for keys
and values, but ``str()`` will be called to convert them before
training.
image_uri (str): If specified, the estimator will use this image
for training and hosting, instead of selecting the appropriate
SageMaker official image based on framework_version and
py_version. It can be an ECR url or dockerhub image and tag.
Examples:
* ``123412341234.dkr.ecr.us-west-2.amazonaws.com/my-custom-image:1.0``
* ``custom-image:latest``
If ``framework_version`` or ``py_version`` are ``None``, then
``image_uri`` is required. If also ``None``, then a ``ValueError``
will be raised.
distribution (dict): A dictionary with information on how to run distributed training
(default: None). Currently, the following are supported:
distributed training with parameter servers, SageMaker Distributed (SMD) Data
and Model Parallelism, and MPI. SMD Model Parallelism can only be used with MPI.
To enable parameter server use the following setup:
.. code:: python
{
"parameter_server": {
"enabled": True
}
}
To enable MPI:
.. code:: python
{
"mpi": {
"enabled": True
}
}
To enable SMDistributed Data Parallel or Model Parallel:
.. code:: python
{
"smdistributed": {
"dataparallel": {
"enabled": True
},
"modelparallel": {
"enabled": True,
"parameters": {}
}
}
}
**kwargs: Additional kwargs passed to the :class:`~sagemaker.estimator.Framework`
constructor.
.. tip::
You can find additional parameters for initializing this class at
:class:`~sagemaker.estimator.Framework` and
:class:`~sagemaker.estimator.EstimatorBase`.
"""
validate_version_or_image_args(framework_version, py_version,
image_uri)
if py_version == "py2":
logger.warning(
python_deprecation_warning(self._framework_name,
defaults.LATEST_PY2_VERSION))
self.framework_version = framework_version
self.py_version = py_version
if distribution is not None:
instance_type = renamed_kwargs("train_instance_type",
"instance_type",
kwargs.get("instance_type"), kwargs)
validate_smdistributed(
instance_type=instance_type,
framework_name=self._framework_name,
framework_version=framework_version,
py_version=py_version,
distribution=distribution,
image_uri=image_uri,
)
warn_if_parameter_server_with_multi_gpu(
training_instance_type=instance_type,
distribution=distribution)
if "enable_sagemaker_metrics" not in kwargs:
# enable sagemaker metrics for PT v1.3 or greater:
if self.framework_version and Version(
self.framework_version) >= Version("1.3"):
kwargs["enable_sagemaker_metrics"] = True
super(PyTorch, self).__init__(entry_point,
source_dir,
hyperparameters,
image_uri=image_uri,
**kwargs)
self.distribution = distribution or {}
def __init__(self,
entry_point,
framework_version=None,
py_version="py3",
source_dir=None,
hyperparameters=None,
image_uri=None,
**kwargs):
"""This ``Estimator`` executes an Scikit-learn script in a managed
Scikit-learn execution environment, within a SageMaker Training Job. The
managed Scikit-learn environment is an Amazon-built Docker container
that executes functions defined in the supplied ``entry_point`` Python
script.
Training is started by calling
:meth:`~sagemaker.amazon.estimator.Framework.fit` on this Estimator.
After training is complete, calling
:meth:`~sagemaker.amazon.estimator.Framework.deploy` creates a hosted
SageMaker endpoint and returns an
:class:`~sagemaker.amazon.sklearn.model.SKLearnPredictor` instance that
can be used to perform inference against the hosted model.
Technical documentation on preparing Scikit-learn scripts for
SageMaker training and using the Scikit-learn Estimator is available on
the project home-page: https://github.com/aws/sagemaker-python-sdk
Args:
entry_point (str): Path (absolute or relative) to the Python source
file which should be executed as the entry point to training.
If ``source_dir`` is specified, then ``entry_point``
must point to a file located at the root of ``source_dir``.
framework_version (str): Scikit-learn version you want to use for
executing your model training code. Defaults to ``None``. Required
unless ``image_uri`` is provided. List of supported versions:
https://github.com/aws/sagemaker-python-sdk#sklearn-sagemaker-estimators
py_version (str): Python version you want to use for executing your
model training code (default: 'py3'). Currently, 'py3' is the only
supported version. If ``None`` is passed in, ``image_uri`` must be
provided.
source_dir (str): Path (absolute, relative or an S3 URI) to a directory
with any other training source code dependencies aside from the entry
point file (default: None). If ``source_dir`` is an S3 URI, it must
point to a tar.gz file. Structure within this directory are preserved
when training on Amazon SageMaker.
hyperparameters (dict): Hyperparameters that will be used for
training (default: None). The hyperparameters are made
accessible as a dict[str, str] to the training code on
SageMaker. For convenience, this accepts other types for keys
and values, but ``str()`` will be called to convert them before
training.
image_uri (str): If specified, the estimator will use this image
for training and hosting, instead of selecting the appropriate
SageMaker official image based on framework_version and
py_version. It can be an ECR url or dockerhub image and tag.
Examples:
123.dkr.ecr.us-west-2.amazonaws.com/my-custom-image:1.0
custom-image:latest.
If ``framework_version`` or ``py_version`` are ``None``, then
``image_uri`` is required. If also ``None``, then a ``ValueError``
will be raised.
**kwargs: Additional kwargs passed to the
:class:`~sagemaker.estimator.Framework` constructor.
.. tip::
You can find additional parameters for initializing this class at
:class:`~sagemaker.estimator.Framework` and
:class:`~sagemaker.estimator.EstimatorBase`.
"""
instance_type = renamed_kwargs("train_instance_type", "instance_type",
kwargs.get("instance_type"), kwargs)
instance_count = renamed_kwargs("train_instance_count",
"instance_count",
kwargs.get("instance_count"), kwargs)
validate_version_or_image_args(framework_version, py_version,
image_uri)
if py_version and py_version != "py3":
raise AttributeError(
"Scikit-learn image only supports Python 3. Please use 'py3' for py_version."
)
self.framework_version = framework_version
self.py_version = py_version
# SciKit-Learn does not support distributed training or training on GPU instance types.
# Fail fast.
_validate_not_gpu_instance_type(instance_type)
if instance_count:
if instance_count != 1:
raise AttributeError(
"Scikit-Learn does not support distributed training. Please remove the "
"'instance_count' argument or set 'instance_count=1' when initializing SKLearn."
)
super(SKLearn, self).__init__(entry_point,
source_dir,
hyperparameters,
image_uri=image_uri,
**dict(kwargs, instance_count=1))
if image_uri is None:
self.image_uri = image_uris.retrieve(
SKLearn._framework_name,
self.sagemaker_session.boto_region_name,
version=self.framework_version,
py_version=self.py_version,
instance_type=instance_type,
)
def __init__(self,
entry_point,
framework_version,
source_dir=None,
hyperparameters=None,
py_version="py3",
image_uri=None,
**kwargs):
"""An estimator that executes an XGBoost-based SageMaker Training Job.
The managed XGBoost environment is an Amazon-built Docker container thatexecutes functions
defined in the supplied ``entry_point`` Python script.
Training is started by calling :meth:`~sagemaker.amazon.estimator.Framework.fit` on this
Estimator. After training is complete, calling
:meth:`~sagemaker.amazon.estimator.Framework.deploy` creates a hosted SageMaker endpoint
and returns an :class:`~sagemaker.amazon.xgboost.model.XGBoostPredictor` instance that
can be used to perform inference against the hosted model.
Technical documentation on preparing XGBoost scripts for SageMaker training and using the
XGBoost Estimator is available on the project home-page:
https://github.com/aws/sagemaker-python-sdk
Args:
entry_point (str): Path (absolute or relative) to the Python source file which should
be executed as the entry point to training. If ``source_dir`` is specified,
then ``entry_point`` must point to a file located at the root of ``source_dir``.
framework_version (str): XGBoost version you want to use for executing your model
training code.
source_dir (str): Path (absolute, relative or an S3 URI) to a directory
with any other training source code dependencies aside from the entry
point file (default: None). If ``source_dir`` is an S3 URI, it must
point to a tar.gz file. Structure within this directory are preserved
when training on Amazon SageMaker.
hyperparameters (dict): Hyperparameters that will be used for training (default: None).
The hyperparameters are made accessible as a dict[str, str] to the training code
on SageMaker. For convenience, this accepts other types for keys and values, but
``str()`` will be called to convert them before training.
py_version (str): Python version you want to use for executing your model
training code (default: 'py3').
image_uri (str): If specified, the estimator will use this image for training and
hosting, instead of selecting the appropriate SageMaker official image
based on framework_version and py_version. It can be an ECR url or
dockerhub image and tag.
Examples:
123.dkr.ecr.us-west-2.amazonaws.com/my-custom-image:1.0
custom-image:latest.
**kwargs: Additional kwargs passed to the
:class:`~sagemaker.estimator.Framework` constructor.
.. tip::
You can find additional parameters for initializing this class at
:class:`~sagemaker.estimator.Framework` and
:class:`~sagemaker.estimator.EstimatorBase`.
"""
instance_type = renamed_kwargs("train_instance_type", "instance_type",
kwargs.get("instance_type"), kwargs)
super(XGBoost, self).__init__(entry_point,
source_dir,
hyperparameters,
image_uri=image_uri,
**kwargs)
self.py_version = py_version
self.framework_version = framework_version
validate_py_version(py_version)
validate_framework_version(framework_version)
if image_uri is None:
self.image_uri = image_uris.retrieve(
self._framework_name,
self.sagemaker_session.boto_region_name,
version=framework_version,
py_version=self.py_version,
instance_type=instance_type,
image_scope="training",
)
def __init__(
self,
py_version,
entry_point,
transformers_version=None,
tensorflow_version=None,
pytorch_version=None,
source_dir=None,
hyperparameters=None,
image_uri=None,
distribution=None,
compiler_config=None,
**kwargs,
):
"""This estimator runs a Hugging Face training script in a SageMaker training environment.
The estimator initiates the SageMaker-managed Hugging Face environment
by using the pre-built Hugging Face Docker container and runs
the Hugging Face training script that user provides through
the ``entry_point`` argument.
After configuring the estimator class, use the class method
:meth:`~sagemaker.amazon.estimator.Framework.fit()` to start a training job.
Args:
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``.
entry_point (str): Path (absolute or relative) to the Python source
file which should be executed as the entry point to training.
If ``source_dir`` is specified, then ``entry_point``
must point to a file located at the root of ``source_dir``.
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 ``4.6.1``.
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 versions are ``1.7.1`` and ``1.6.0``.
source_dir (str): Path (absolute, relative or an S3 URI) to a directory
with any other training source code dependencies aside from the entry
point file (default: None). If ``source_dir`` is an S3 URI, it must
point to a tar.gz file. Structure within this directory are preserved
when training on Amazon SageMaker.
hyperparameters (dict): Hyperparameters that will be used for
training (default: None). The hyperparameters are made
accessible as a dict[str, str] to the training code on
SageMaker. For convenience, this accepts other types for keys
and values, but ``str()`` will be called to convert them before
training.
image_uri (str): If specified, the estimator will use this image
for training and hosting, instead of selecting the appropriate
SageMaker official image based on framework_version and
py_version. It can be an ECR url or dockerhub image and tag.
Examples:
* ``123412341234.dkr.ecr.us-west-2.amazonaws.com/my-custom-image:1.0``
* ``custom-image:latest``
If ``framework_version`` or ``py_version`` are ``None``, then
``image_uri`` is required. If also ``None``, then a ``ValueError``
will be raised.
distribution (dict): A dictionary with information on how to run distributed training
(default: None). Currently, the following are supported:
distributed training with parameter servers, SageMaker Distributed (SMD) Data
and Model Parallelism, and MPI. SMD Model Parallelism can only be used with MPI.
To enable parameter server use the following setup:
.. code:: python
{
"parameter_server": {
"enabled": True
}
}
To enable MPI:
.. code:: python
{
"mpi": {
"enabled": True
}
}
To enable SMDistributed Data Parallel or Model Parallel:
.. code:: python
{
"smdistributed": {
"dataparallel": {
"enabled": True
},
"modelparallel": {
"enabled": True,
"parameters": {}
}
}
}
compiler_config (:class:`~sagemaker.huggingface.TrainingCompilerConfig`):
Configures SageMaker Training Compiler to accelerate training.
**kwargs: Additional kwargs passed to the :class:`~sagemaker.estimator.Framework`
constructor.
.. tip::
You can find additional parameters for initializing this class at
:class:`~sagemaker.estimator.Framework` and
:class:`~sagemaker.estimator.EstimatorBase`.
"""
self.framework_version = transformers_version
self.py_version = py_version
self.tensorflow_version = tensorflow_version
self.pytorch_version = pytorch_version
self._validate_args(image_uri=image_uri)
instance_type = renamed_kwargs(
"train_instance_type", "instance_type", kwargs.get("instance_type"), kwargs
)
base_framework_name = "tensorflow" if tensorflow_version is not None else "pytorch"
base_framework_version = (
tensorflow_version if tensorflow_version is not None else pytorch_version
)
if distribution is not None:
validate_smdistributed(
instance_type=instance_type,
framework_name=base_framework_name,
framework_version=base_framework_version,
py_version=self.py_version,
distribution=distribution,
image_uri=image_uri,
)
warn_if_parameter_server_with_multi_gpu(
training_instance_type=instance_type, distribution=distribution
)
if "enable_sagemaker_metrics" not in kwargs:
kwargs["enable_sagemaker_metrics"] = True
kwargs["py_version"] = self.py_version
super(HuggingFace, self).__init__(
entry_point, source_dir, hyperparameters, image_uri=image_uri, **kwargs
)
if compiler_config is not None:
if not isinstance(compiler_config, TrainingCompilerConfig):
error_string = (
f"Expected instance of type {TrainingCompilerConfig}"
f"for argument compiler_config. "
f"Instead got {type(compiler_config)}"
)
raise ValueError(error_string)
if compiler_config:
compiler_config.validate(
image_uri=image_uri,
instance_type=instance_type,
distribution=distribution,
)
self.distribution = distribution or {}
self.compiler_config = compiler_config
