feature: add HuggingFace framework estimator

src/sagemaker/estimator.py

@@ -2331,6 +2331,22 @@ def training_image_uri(self):
             distribution = self.distribution  # pylint: disable=no-member
         else:
             distribution = None
+
+        if hasattr(self, "tensorflow_version") or hasattr(self, "pytorch_version"):
+            processor = image_uris._processor(self.instance_type, ["cpu", "gpu"])
+            container_version = "cu110-ubuntu18.04" if processor == "gpu" else None
+            if self.tensorflow_version is not None:  # pylint: disable=no-member
+                base_framework_version = (
+                    f"tensorflow{self.tensorflow_version}"  # pylint: disable=no-member
+                )
+            else:
+                base_framework_version = (
+                    f"pytorch{self.pytorch_version}"  # pylint: disable=no-member
+                )
+        else:
+            container_version = None
+            base_framework_version = None
+
         return image_uris.retrieve(
             self._framework_name,
             self.sagemaker_session.boto_region_name,
@@ -2339,6 +2355,8 @@ def training_image_uri(self):
             py_version=self.py_version,  # pylint: disable=no-member
             image_scope="training",
             distribution=distribution,
+            base_framework_version=base_framework_version,
+            container_version=container_version,
         )
 
     @classmethod

src/sagemaker/fw_utils.py

@@ -288,9 +288,10 @@ def framework_name_from_image(image_uri):
     # We must support both the legacy and current image name format.
     name_pattern = re.compile(
         r"""^(?:sagemaker(?:-rl)?-)?
-        (tensorflow|mxnet|chainer|pytorch|scikit-learn|xgboost)(?:-)?
+        (tensorflow|mxnet|chainer|pytorch|scikit-learn|xgboost
+        |huggingface-tensorflow|huggingface-pytorch)(?:-)?
         (scriptmode|training)?
-        :(.*)-(.*?)-(py2|py3[67]?)$""",
+        :(.*)-(.*?)-(py2|py3[67]?)(?:.*)$""",
         re.VERBOSE,
     )
     name_match = name_pattern.match(sagemaker_match.group(9))

src/sagemaker/huggingface/__init__.py

@@ -0,0 +1,16 @@
+# Copyright 2018-2020 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# 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.
+"""Placeholder docstring"""
+from __future__ import absolute_import
+
+from sagemaker.huggingface.estimator import HuggingFace  # noqa: F401

src/sagemaker/huggingface/estimator.py

@@ -0,0 +1,278 @@
+# Copyright 2019-2020 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# 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.
+"""Placeholder docstring"""
+from __future__ import absolute_import
+
+import logging
+import re
+
+from sagemaker.deprecations import renamed_kwargs
+from sagemaker.estimator import Framework
+from sagemaker.fw_utils import (
+    framework_name_from_image,
+    warn_if_parameter_server_with_multi_gpu,
+    validate_smdistributed,
+)
+from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
+
+logger = logging.getLogger("sagemaker")
+
+
+class HuggingFace(Framework):
+    """Handle training of custom HuggingFace code."""
+
+    _framework_name = "huggingface"
+
+    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 _validate_args(self, image_uri):
+        """Placeholder docstring"""
+        if image_uri is not None:
+            return
+        if self.framework_version is None and image_uri is None:
+            raise ValueError(
+                "transformers_version, and image_uri are both None. "
+                "Specify either transformers_version or image_uri"
+            )
+        if self.tensorflow_version is not None and self.pytorch_version is not None:
+            raise ValueError(
+                "tensorflow_version and pytorch_version are both not None. "
+                "Specify only tensorflow_version or pytorch_version."
+            )
+        if self.tensorflow_version is None and self.pytorch_version is None:
+            raise ValueError(
+                "tensorflow_version and pytorch_version are both None. "
+                "Specify either tensorflow_version or pytorch_version."
+            )
+
+    def hyperparameters(self):
+        """Return hyperparameters used by your custom PyTorch code during model training."""
+        hyperparameters = super(HuggingFace, self).hyperparameters()
+        additional_hyperparameters = self._distribution_configuration(
+            distribution=self.distribution
+        )
+        hyperparameters.update(Framework._json_encode_hyperparameters(additional_hyperparameters))
+        return hyperparameters
+
+    def create_model(
+        self,
+        model_server_workers=None,
+        role=None,
+        vpc_config_override=VPC_CONFIG_DEFAULT,
+        entry_point=None,
+        source_dir=None,
+        dependencies=None,
+        **kwargs
+    ):
+        """Placeholder docstring"""
+        raise NotImplementedError("Creating model with HuggingFace training job is not supported.")
+
+    @classmethod
+    def _prepare_init_params_from_job_description(cls, job_details, model_channel_name=None):
+        """Convert the job description to init params that can be handled by the class constructor.
+
+        Args:
+            job_details: The returned job details from a describe_training_job
+                API call.
+            model_channel_name (str): Name of the channel where pre-trained
+                model data will be downloaded.
+
+        Returns:
+            dictionary: The transformed init_params
+        """
+        init_params = super(HuggingFace, cls)._prepare_init_params_from_job_description(
+            job_details, model_channel_name
+        )
+        image_uri = init_params.pop("image_uri")
+        framework, py_version, tag, _ = framework_name_from_image(image_uri)
+
+        if tag is None:
+            framework_version = None
+        else:
+            framework, pt_or_tf = framework.split("-")
+            tag_pattern = re.compile("^(.*)-transformers(.*)-(cpu|gpu)-(py2|py3[67]?)$")
+            tag_match = tag_pattern.match(tag)
+            pt_or_tf_version = tag_match.group(1)
+            framework_version = tag_match.group(2)
+            if pt_or_tf == "pytorch":
+                init_params["pytorch_version"] = pt_or_tf_version
+            else:
+                init_params["tensorflow_version"] = pt_or_tf_version
+
+        init_params["transformers_version"] = framework_version
+        init_params["py_version"] = py_version
+
+        if not framework:
+            # If we were unable to parse the framework name from the image it is not one of our
+            # officially supported images, in this case just add the image to the init params.
+            init_params["image_uri"] = image_uri
+            return init_params
+
+        if framework != cls._framework_name:
+            raise ValueError(
+                "Training job: {} didn't use image for requested framework".format(
+                    job_details["TrainingJobName"]
+                )
+            )
+
+        return init_params