def __init__(self,
entry_point,
source_dir=None,
hyperparameters=None,
py_version=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 tne 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:
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.
"""
if framework_version is None:
logger.warning(
empty_framework_version_warning(PYTORCH_VERSION,
PYTORCH_VERSION))
self.framework_version = framework_version or PYTORCH_VERSION
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__))
self.py_version = py_version
def __init__(
self,
model_data,
role,
entry_point,
image=None,
py_version="py2",
framework_version=None,
predictor_cls=TensorFlowPredictor,
model_server_workers=None,
**kwargs
):
"""Initialize an TensorFlowModel.
Args:
model_data (str): The S3 location of a SageMaker model data
``.tar.gz`` file.
role (str): An AWS IAM role (either name or full ARN). The Amazon
SageMaker training jobs and APIs that create Amazon SageMaker
endpoints use this role to access training data and model
artifacts. After the endpoint is created, the inference code
might use the IAM role, if it needs to access an AWS resource.
entry_point (str): Path (absolute or relative) to the Python source
file which should be executed as the entry point to model
hosting. This should be compatible with either Python 2.7 or
Python 3.5.
image (str): A Docker image URI (default: None). If not specified, a
default image for TensorFlow will be used.
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.
predictor_cls (callable[str, sagemaker.session.Session]): A function
to call to create a predictor with an endpoint name and
SageMaker ``Session``. If specified, ``deploy()`` returns the
result of invoking this function on the created endpoint name.
model_server_workers (int): Optional. The number of worker processes
used by the inference server. If None, server will use one
worker per vCPU.
**kwargs: Keyword arguments passed to the ``FrameworkModel``
initializer.
.. tip::
You can find additional parameters for initializing this class at
:class:`~sagemaker.model.FrameworkModel` and
:class:`~sagemaker.model.Model`.
"""
super(TensorFlowModel, self).__init__(
model_data, image, role, entry_point, predictor_cls=predictor_cls, **kwargs
)
if py_version == "py2":
logger.warning(python_deprecation_warning(self.__framework_name__))
if framework_version is None:
logger.warning(empty_framework_version_warning(TF_VERSION, LATEST_VERSION))
self.py_version = py_version
self.framework_version = framework_version or TF_VERSION
self.model_server_workers = model_server_workers
def __init__(self,
entry_point,
use_mpi=None,
num_processes=None,
process_slots_per_host=None,
additional_mpi_options=None,
source_dir=None,
hyperparameters=None,
py_version="py3",
framework_version=None,
image_name=None,
**kwargs):
"""This ``Estimator`` executes an Chainer script in a managed Chainer
execution environment, within a SageMaker Training Job. The managed
Chainer 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.chainer.model.ChainerPredictor` instance that
can be used to perform inference against the hosted model.
Technical documentation on preparing Chainer scripts for SageMaker
training and using the Chainer 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.
use_mpi (bool): If true, entry point is run as an MPI script. By
default, the Chainer Framework runs the entry point with
'mpirun' if more than one instance is used.
num_processes (int): Total number of processes to run the entry
point with. By default, the Chainer Framework runs one process
per GPU (on GPU instances), or one process per host (on CPU
instances).
process_slots_per_host (int): The number of processes that can run
on each instance. By default, this is set to the number of GPUs
on the instance (on GPU instances), or one (on CPU instances).
additional_mpi_options (str): String of options to the 'mpirun'
command used to run the entry point. For example, '-X
NCCL_DEBUG=WARN' will pass that option string to the mpirun
command.
source_dir (str): Path (absolute or relative) to a directory with
any other training source code dependencies aside from tne 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: 'py2'). One of 'py2' or 'py3'.
framework_version (str): Chainer version you want to use for
executing your model training code. List of supported versions
https://github.com/aws/sagemaker-python-sdk#chainer-sagemaker-estimators.
If not specified, this will default to 4.1.
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``
**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(CHAINER_VERSION,
self.LATEST_VERSION))
self.framework_version = framework_version or CHAINER_VERSION
super(Chainer, self).__init__(entry_point,
source_dir,
hyperparameters,
image_name=image_name,
**kwargs)
if py_version == "py2":
logger.warning(python_deprecation_warning(self.__framework_name__))
self.py_version = py_version
self.use_mpi = use_mpi
self.num_processes = num_processes
self.process_slots_per_host = process_slots_per_host
self.additional_mpi_options = additional_mpi_options
def __init__(self,
model_data,
role,
entry_point,
framework_version=None,
py_version=None,
image_uri=None,
predictor_cls=MXNetPredictor,
model_server_workers=None,
**kwargs):
"""Initialize an MXNetModel.
Args:
model_data (str): The S3 location of a SageMaker model data
``.tar.gz`` file.
role (str): An AWS IAM role (either name or full ARN). The Amazon
SageMaker training jobs and APIs that create Amazon SageMaker
endpoints use this role to access training data and model
artifacts. After the endpoint is created, the inference code
might use the IAM role, if it needs to access an AWS resource.
entry_point (str): Path (absolute or relative) to the Python source
file which should be executed as the entry point to model
hosting. If ``source_dir`` is specified, then ``entry_point``
must point to a file located at the root of ``source_dir``.
framework_version (str): MXNet version you want to use for executing
your model training code. Defaults to ``None``. Required unless
``image_uri`` is provided.
py_version (str): Python version you want to use for executing your
model training code. Defaults to ``None``. Required unless
``image_uri`` is provided.
image_uri (str): A Docker image URI (default: None). If not specified, a
default image for MXNet will be used.
If ``framework_version`` or ``py_version`` are ``None``, then
``image_uri`` is required. If also ``None``, then a ``ValueError``
will be raised.
predictor_cls (callable[str, sagemaker.session.Session]): A function
to call to create a predictor with an endpoint name and
SageMaker ``Session``. If specified, ``deploy()`` returns the
result of invoking this function on the created endpoint name.
model_server_workers (int): Optional. The number of worker processes
used by the inference server. If None, server will use one
worker per vCPU.
**kwargs: Keyword arguments passed to the superclass
:class:`~sagemaker.model.FrameworkModel` and, subsequently, its
superclass :class:`~sagemaker.model.Model`.
.. tip::
You can find additional parameters for initializing this class at
:class:`~sagemaker.model.FrameworkModel` and
:class:`~sagemaker.model.Model`.
"""
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
super(MXNetModel, self).__init__(model_data,
image_uri,
role,
entry_point,
predictor_cls=predictor_cls,
**kwargs)
self.model_server_workers = model_server_workers
def __init__(self,
entry_point,
framework_version=None,
py_version=None,
source_dir=None,
hyperparameters=None,
image_uri=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.
**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 "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)
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,
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,
framework_version=defaults.SKLEARN_VERSION,
source_dir=None,
hyperparameters=None,
py_version="py3",
image_name=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. List of supported versions
https://github.com/aws/sagemaker-python-sdk#sklearn-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.
py_version (str): Python version you want to use for executing your
model training code (default: 'py3'). One of 'py2' or 'py3'.
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.
**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`.
"""
# SciKit-Learn does not support distributed training or training on GPU instance types.
# Fail fast.
train_instance_type = kwargs.get("train_instance_type")
_validate_not_gpu_instance_type(train_instance_type)
train_instance_count = kwargs.get("train_instance_count")
if train_instance_count:
if train_instance_count != 1:
raise AttributeError(
"Scikit-Learn does not support distributed training. "
"Please remove the 'train_instance_count' argument or set "
"'train_instance_count=1' when initializing SKLearn."
)
super(SKLearn, self).__init__(
entry_point,
source_dir,
hyperparameters,
image_name=image_name,
**dict(kwargs, train_instance_count=1)
)
if py_version == "py2":
logger.warning(
python_deprecation_warning(self.__framework_name__, defaults.LATEST_PY2_VERSION)
)
self.py_version = py_version
if framework_version is None:
logger.warning(
empty_framework_version_warning(defaults.SKLEARN_VERSION, defaults.SKLEARN_VERSION)
)
self.framework_version = framework_version or defaults.SKLEARN_VERSION
if image_name is None:
image_tag = "{}-{}-{}".format(framework_version, "cpu", py_version)
self.image_name = default_framework_uri(
SKLearn.__framework_name__, self.sagemaker_session.boto_region_name, image_tag
)
def __init__(self,
entry_point,
framework_version,
source_dir=None,
hyperparameters=None,
py_version="py3",
image_name=None,
**kwargs):
"""
This ``Estimator`` 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.
This should be compatible with either Python 2.7 or Python 3.5.
framework_version (str): XGBoost version you want to use for executing your model
training code. List of supported versions
https://github.com/aws/sagemaker-python-sdk#xgboost-sagemaker-estimators
source_dir (str): Path (absolute or relative) to a directory with any other training
source code dependencies aside from tne 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'.
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.
**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`.
"""
super(XGBoost, self).__init__(entry_point,
source_dir,
hyperparameters,
image_name=image_name,
**kwargs)
if py_version == "py2":
logger.warning(python_deprecation_warning(self.__framework_name__))
self.py_version = py_version
if framework_version in XGBOOST_SUPPORTED_VERSIONS:
self.framework_version = framework_version
else:
raise ValueError(
get_unsupported_framework_version_error(
self.__framework_name__, framework_version,
XGBOOST_SUPPORTED_VERSIONS))
if image_name is None:
self.image_name = get_xgboost_image_uri(
self.sagemaker_session.boto_region_name, framework_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.
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: '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:
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)
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 MXNet script in a managed MXNet execution environment.
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``.
framework_version (str): MXNet 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#mxnet-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 we support distributed training with
parameter server and MPI [Horovod].
To enable parameter server use the following setup:
.. code:: python
{
'parameter_server':
{
'enabled': True
}
}
To enable MPI:
.. code:: python
{
'mpi':
{
'enabled': True
}
}
Option parameters within ``mpi`` are ``processes_per_host``
and ``custom_mpi_options``.
.. code:: python
{
'mpi':
{
'enabled': True,
'processes_per_host': 2,
'custom_mpi_options': '-verbose --NCCL_DEBUG=INFO'
}
}
**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`.
"""
distribution = renamed_kwargs("distributions", "distribution", distribution, kwargs)
instance_type = renamed_kwargs(
"train_instance_type", "instance_type", kwargs.get("instance_type"), kwargs
)
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 "enable_sagemaker_metrics" not in kwargs:
# enable sagemaker metrics for MXNet v1.6 or greater:
if self.framework_version and Version(self.framework_version) >= Version("1.6"):
kwargs["enable_sagemaker_metrics"] = True
super(MXNet, self).__init__(
entry_point, source_dir, hyperparameters, image_uri=image_uri, **kwargs
)
if distribution is not None:
warn_if_parameter_server_with_multi_gpu(
training_instance_type=instance_type, distribution=distribution
)
self._configure_distribution(distribution)
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 a PyTorch script in a managed PyTorch execution environment.
The managed PyTorch 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.
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/deep-learning-containers/blob/master/available_images.md.
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 the SageMaker distributed data parallelism:**
.. code:: python
{ "smdistributed": { "dataparallel": { "enabled": True } } }
.. seealso::
To learn more, see :ref:`sdp_api_docs_toc`.
**To enable the SageMaker distributed model parallelism:**
.. code:: python
{
"smdistributed": {
"modelparallel": {
"enabled":True,
"parameters": {
"partitions": 2,
"microbatches": 4,
"placement_strategy": "spread",
"pipeline": "interleaved",
"optimize": "speed",
"ddp": True,
}
},
"mpi": {
"enabled" : True,
"processes_per_host" : 8,
}
}
.. note::
The SageMaker distributed model parallel library internally uses MPI.
In order to use model parallelism, MPI also must be enabled.
.. seealso::
To learn more, see :ref:`smp_api_docs_toc`.
.. seealso::
To find a complete list of parameters for SageMaker model parallelism,
see :ref:`sm-sdk-modelparallel-general`.
**To enable MPI:**
.. code:: python
{
"mpi": {
"enabled": True
}
}
To learn more, see `Training with Horovod
<https://sagemaker.readthedocs.io/en/stable/frameworks/tensorflow/using_tf.html#training-with-horovod>`_.
**To enable parameter server:**
.. code:: python
{
"parameter_server": {
"enabled": True
}
}
To learn more, see `Training with parameter servers
<https://sagemaker.readthedocs.io/en/stable/frameworks/tensorflow/using_tf.html#training-with-parameter-servers>`_.
**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 {}
