Merge branch 'master' into minor-updates-to-clarify-api-doc

Author: ahsan-z-khan

CHANGELOG.md

@@ -1,5 +1,31 @@
 # Changelog
 
+## v2.52.1 (2021-08-06)
+
+### Bug Fixes and Other Changes
+
+ * revert #2251 changes for sklearn processor
+
+## v2.52.0 (2021-08-05)
+
+### Features
+
+ * processors that support multiple Python files, requirements.txt, and dependencies.
+ * support step object in step depends on list
+
+### Bug Fixes and Other Changes
+
+ * enable isolation while creating model from job
+ * update `sagemaker.serverless` integration test
+ * Use correct boto model name for RegisterModelStep properties
+
+## v2.51.0 (2021-08-03)
+
+### Features
+
+ * add LambdaStep support for SageMaker Pipelines
+ * support JsonGet for all step types
+
 ## v2.50.1 (2021-08-02)
 
 ### Bug Fixes and Other Changes

VERSION

@@ -1 +1 @@
-2.50.2.dev0
+2.52.2.dev0

doc/api/inference/model.rst

@@ -15,3 +15,8 @@ Model
     :members:
     :undoc-members:
     :show-inheritance:
+
+.. autoclass:: sagemaker.serverless.model.LambdaModel
+    :members:
+    :undoc-members:
+    :show-inheritance:

doc/api/inference/predictors.rst

@@ -7,3 +7,8 @@ Make real-time predictions against SageMaker endpoints with Python objects
     :members:
     :undoc-members:
     :show-inheritance:
+
+.. autoclass:: sagemaker.serverless.predictor.LambdaPredictor
+    :members:
+    :undoc-members:
+    :show-inheritance:

doc/api/utility/lambda_helper.rst

@@ -0,0 +1,7 @@
+Lambda Utilities
+----------------
+
+.. automodule:: sagemaker.lambda_helper
+    :members:
+    :undoc-members:
+    :show-inheritance:

doc/overview.rst

@@ -1063,6 +1063,50 @@ You can also find these notebooks in the **Advanced Functionality** section of t
 For information about using sample notebooks in a SageMaker notebook instance, see `Use Example Notebooks <https://docs.aws.amazon.com/sagemaker/latest/dg/howitworks-nbexamples.html>`__
 in the AWS documentation.
 
+********************
+Serverless Inference
+********************
+
+You can use the SageMaker Python SDK to perform serverless inference on Lambda.
+
+To deploy models to Lambda, you must complete the following prerequisites:
+
+- `Package your model and inference code as a container image. <https://docs.aws.amazon.com/lambda/latest/dg/images-create.html>`_
+- `Create a role that lists Lambda as a trusted entity. <https://docs.aws.amazon.com/lambda/latest/dg/lambda-intro-execution-role.html#permissions-executionrole-console>`_
+
+After completing the prerequisites, you can deploy your model to Lambda using
+the `LambdaModel`_ class.
+
+.. code:: python
+
+   from sagemaker.serverless import LambdaModel
+
+   image_uri = "123456789012.dkr.ecr.us-west-2.amazonaws.com/my-lambda-repository:latest"
+   role = "arn:aws:iam::123456789012:role/MyLambdaExecutionRole"
+
+   model = LambdaModel(image_uri=image_uri, role=role)
+   predictor = model.deploy("my-lambda-function", timeout=20, memory_size=4092)
+
+The ``deploy`` method returns a `LambdaPredictor`_  instance. Use the
+`LambdaPredictor`_ ``predict`` method to perform inference on Lambda.
+
+.. code:: python
+
+   url = "https://example.com/cat.jpeg"
+   predictor.predict({"url": url})  # {'class': 'tabby'}
+
+Once you are done performing inference on Lambda, free the `LambdaModel`_ and
+`LambdaPredictor`_ resources using the ``delete_model`` and ``delete_predictor``
+methods.
+
+.. code:: python
+
+   model.delete_model()
+   predictor.delete_predictor()
+
+.. _LambdaModel : https://sagemaker.readthedocs.io/en/stable/api/inference/model.html#sagemaker.serverless.model.LambdaModel
+.. _LambdaPredictor : https://sagemaker.readthedocs.io/en/stable/api/inference/predictors.html#sagemaker.serverless.predictor.LambdaPredictor
+
 ******************
 SageMaker Workflow
 ******************

doc/workflows/pipelines/sagemaker.workflow.pipelines.rst

@@ -127,3 +127,5 @@ Steps
 .. autoclass:: sagemaker.workflow.callback_step.CallbackStep
 
 .. autoclass:: sagemaker.workflow.steps.CacheConfig
+
+.. autoclass:: sagemaker.workflow.lambda_step.LambdaStep

src/sagemaker/huggingface/__init__.py

@@ -15,3 +15,4 @@
 
 from sagemaker.huggingface.estimator import HuggingFace  # noqa: F401
 from sagemaker.huggingface.model import HuggingFaceModel, HuggingFacePredictor  # noqa: F401
+from sagemaker.huggingface.processing import HuggingFaceProcessor  # noqa:F401

src/sagemaker/huggingface/processing.py

@@ -0,0 +1,132 @@
+# Copyright 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.
+"""This module contains code related to HuggingFace Processors which are used for Processing jobs.
+
+These jobs let customers perform data pre-processing, post-processing, feature engineering,
+data validation, and model evaluation and interpretation on SageMaker.
+"""
+from __future__ import absolute_import
+
+from sagemaker.processing import FrameworkProcessor
+from sagemaker.huggingface.estimator import HuggingFace
+
+
+class HuggingFaceProcessor(FrameworkProcessor):
+    """Handles Amazon SageMaker processing tasks for jobs using HuggingFace containers."""
+
+    estimator_cls = HuggingFace
+
+    def __init__(
+        self,
+        role,
+        instance_count,
+        instance_type,
+        transformers_version=None,
+        tensorflow_version=None,
+        pytorch_version=None,
+        py_version="py36",
+        image_uri=None,
+        command=None,
+        volume_size_in_gb=30,
+        volume_kms_key=None,
+        output_kms_key=None,
+        code_location=None,
+        max_runtime_in_seconds=None,
+        base_job_name=None,
+        sagemaker_session=None,
+        env=None,
+        tags=None,
+        network_config=None,
+    ):
+        """This processor executes a Python script in a HuggingFace execution environment.
+
+        Unless ``image_uri`` is specified, the environment is an Amazon-built Docker container
+        that executes functions defined in the supplied ``code`` Python script.
+
+        The arguments have the same meaning as in ``FrameworkProcessor``, with the following
+        exceptions.
+
+        Args:
+            transformers_version (str): Transformers version you want to use for
+                executing your model training code. Defaults to ``None``. Required unless
+                ``image_uri`` is provided. The current supported version is ``4.4.2``.
+            tensorflow_version (str): TensorFlow version you want to use for
+                executing your model training code. Defaults to ``None``. Required unless
+                ``pytorch_version`` is provided. The current supported version is ``1.6.0``.
+            pytorch_version (str): PyTorch version you want to use for
+                executing your model training code. Defaults to ``None``. Required unless
+                ``tensorflow_version`` is provided. The current supported version is ``2.4.1``.
+            py_version (str): Python version you want to use for executing your model training
+                code. Defaults to ``None``. Required unless ``image_uri`` is provided.  If
+                using PyTorch, the current supported version is ``py36``. If using TensorFlow,
+                the current supported version is ``py37``.
+
+        .. tip::
+
+            You can find additional parameters for initializing this class at
+            :class:`~sagemaker.processing.FrameworkProcessor`.
+        """
+        self.pytorch_version = pytorch_version
+        self.tensorflow_version = tensorflow_version
+        super().__init__(
+            self.estimator_cls,
+            transformers_version,
+            role,
+            instance_count,
+            instance_type,
+            py_version,
+            image_uri,
+            command,
+            volume_size_in_gb,
+            volume_kms_key,
+            output_kms_key,
+            code_location,
+            max_runtime_in_seconds,
+            base_job_name,
+            sagemaker_session,
+            env,
+            tags,
+            network_config,
+        )
+
+    def _create_estimator(
+        self,
+        entry_point="",
+        source_dir=None,
+        dependencies=None,
+        git_config=None,
+    ):
+        """Override default estimator factory function for HuggingFace's different parameters
+
+        HuggingFace estimators have 3 framework version parameters instead of one: The version for
+        Transformers, PyTorch, and TensorFlow.
+        """
+        return self.estimator_cls(
+            transformers_version=self.framework_version,
+            tensorflow_version=self.tensorflow_version,
+            pytorch_version=self.pytorch_version,
+            py_version=self.py_version,
+            entry_point=entry_point,
+            source_dir=source_dir,
+            dependencies=dependencies,
+            git_config=git_config,
+            code_location=self.code_location,
+            enable_network_isolation=False,
+            image_uri=self.image_uri,
+            role=self.role,
+            instance_count=self.instance_count,
+            instance_type=self.instance_type,
+            sagemaker_session=self.sagemaker_session,
+            debugger_hook_config=False,
+            disable_profiler=True,
+        )