def __init__(self,
training_steps=None,
evaluation_steps=None,
checkpoint_path=None,
py_version=None,
framework_version=None,
model_dir=None,
requirements_file="",
image_name=None,
script_mode=False,
distributions=None,
**kwargs):
"""Initialize a ``TensorFlow`` estimator.
Args:
training_steps (int): Perform this many steps of training. `None`, the default means
train forever.
evaluation_steps (int): Perform this many steps of evaluation. `None`, the default
means that evaluation runs until input from eval_input_fn is exhausted (or another
exception is raised).
checkpoint_path (str): Identifies S3 location where checkpoint data during model
training can be saved (default: None). For distributed model training, this
parameter is required.
py_version (str): Python version you want to use for executing your model training
code (default: 'py2').
framework_version (str): TensorFlow version you want to use for executing your model
training code. If not specified, this will default to 1.11.
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 MPI* - ``/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``
requirements_file (str): Path to a ``requirements.txt`` file (default: ''). The path
should be within and relative to ``source_dir``. Details on the format can be
found in the Pip User Guide:
<https://pip.pypa.io/en/stable/reference/pip_install/#requirements-file-format>
image_name (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.
script_mode (bool): If set to True will the estimator will use the Script Mode
containers (default: False). This will be ignored if py_version is set to 'py3'.
distributions (dict): A dictionary with information on how to run distributed training
(default: None). Currently we support distributed training with parameter servers
and MPI.
To enable parameter server use the following setup:
.. code:: python
{
'parameter_server':
{
'enabled': True
}
}
To enable MPI:
.. code:: python
{
'mpi':
{
'enabled': True
}
}
**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`.
"""
if framework_version is None:
logger.warning(
fw.empty_framework_version_warning(defaults.TF_VERSION,
self.LATEST_VERSION))
self.framework_version = framework_version or defaults.TF_VERSION
if not py_version:
py_version = "py3" if self._only_python_3_supported() else "py2"
if py_version == "py2":
logger.warning(
fw.python_deprecation_warning(self.__framework_name__,
defaults.LATEST_PY2_VERSION))
if distributions is not None:
logger.warning(
fw.parameter_v2_rename_warning("distribution", distributions))
train_instance_type = kwargs.get("train_instance_type")
fw.warn_if_parameter_server_with_multi_gpu(
training_instance_type=train_instance_type,
distributions=distributions)
if "enable_sagemaker_metrics" not in kwargs:
# enable sagemaker metrics for TF v1.15 or greater:
if fw.is_version_equal_or_higher([1, 15], self.framework_version):
kwargs["enable_sagemaker_metrics"] = True
super(TensorFlow, self).__init__(image_name=image_name, **kwargs)
self.checkpoint_path = checkpoint_path
self.py_version = py_version
self.training_steps = training_steps
self.evaluation_steps = evaluation_steps
self.model_dir = model_dir
self.script_mode = script_mode
self.distributions = distributions or {}
self._validate_args(
py_version=py_version,
script_mode=script_mode,
framework_version=self.framework_version,
training_steps=training_steps,
evaluation_steps=evaluation_steps,
requirements_file=requirements_file,
checkpoint_path=checkpoint_path,
)
self._validate_requirements_file(requirements_file)
self.requirements_file = requirements_file
def __init__(
self,
entry_point,
source_dir=None,
hyperparameters=None,
py_version=defaults.PYTHON_VERSION,
framework_version=None,
image_name=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.
This should be compatible with either Python 2.7 or Python 3.5.
source_dir (str): Path (absolute or relative) to a directory with
any other training source code dependencies aside from the entry
point file (default: None). 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'). One of 'py2' or 'py3'.
framework_version (str): PyTorch version you want to use for
executing your model training code. List of supported versions
https://github.com/aws/sagemaker-python-sdk#pytorch-sagemaker-estimators.
If not specified, this will default to 0.4.
image_name (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``
**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`.
"""
if framework_version is None:
logger.warning(
empty_framework_version_warning(defaults.PYTORCH_VERSION, self.LATEST_VERSION)
)
self.framework_version = framework_version or defaults.PYTORCH_VERSION
if "enable_sagemaker_metrics" not in kwargs:
# enable sagemaker metrics for PT v1.3 or greater:
if is_version_equal_or_higher([1, 3], self.framework_version):
kwargs["enable_sagemaker_metrics"] = True
super(PyTorch, self).__init__(
entry_point, source_dir, hyperparameters, image_name=image_name, **kwargs
)
if py_version == "py2":
logger.warning(
python_deprecation_warning(self.__framework_name__, defaults.LATEST_PY2_VERSION)
)
self.py_version = py_version
def __init__(self,
entry_point,
source_dir=None,
hyperparameters=None,
py_version="py2",
framework_version=None,
image_name=None,
distributions=None,
**kwargs):
"""This ``Estimator`` executes an MXNet script in a managed MXNet
execution environment, within a SageMaker Training Job. The managed
MXNet 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.mxnet.model.MXNetPredictor` instance that can
be used to perform inference against the hosted model.
Technical documentation on preparing MXNet scripts for SageMaker
training and using the MXNet 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``.
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: 'py2'). One of 'py2' or 'py3'.
framework_version (str): MXNet version you want to use for executing
your model training code. List of supported versions
https://github.com/aws/sagemaker-python-sdk#mxnet-sagemaker-estimators.
If not specified, this will default to 1.2.1.
image_name (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``
distributions (dict): A dictionary with information on how to run distributed
training (default: None). To have parameter servers launched for training,
set this value to be ``{'parameter_server': {'enabled': True}}``.
**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`.
"""
if framework_version is None:
logger.warning(
empty_framework_version_warning(defaults.MXNET_VERSION,
self.LATEST_VERSION))
self.framework_version = framework_version or defaults.MXNET_VERSION
if "enable_sagemaker_metrics" not in kwargs:
# enable sagemaker metrics for MXNet v1.6 or greater:
if is_version_equal_or_higher([1, 6], self.framework_version):
kwargs["enable_sagemaker_metrics"] = True
super(MXNet, self).__init__(entry_point,
source_dir,
hyperparameters,
image_name=image_name,
**kwargs)
if py_version == "py2":
logger.warning(
python_deprecation_warning(self.__framework_name__,
defaults.LATEST_PY2_VERSION))
if distributions is not None:
logger.warning(
parameter_v2_rename_warning("distributions", "distribution"))
train_instance_type = kwargs.get("train_instance_type")
warn_if_parameter_server_with_multi_gpu(
training_instance_type=train_instance_type,
distributions=distributions)
self.py_version = py_version
self._configure_distribution(distributions)
