Merge branch 'master' into change/remove-setuptools-deprecation

Author: jjerphan

.gitignore

@@ -34,3 +34,4 @@ env/
 **/_repack_script_launcher.sh
 tests/data/**/_repack_model.py
 tests/data/experiment/sagemaker-dev-1.0.tar.gz
+src/sagemaker/serve/tmp_workspace

CHANGELOG.md

@@ -1,5 +1,61 @@
 # Changelog
 
+## v2.199.0 (2023-11-30)
+
+### Features
+
+ * Update boto3 version to 1.33.3
+ * Goldfinch InferenceComponent integration
+ * Add Pipeline step decorator, NotebookJobStep, and scheduler
+ * ModelBuilder for simplified model testing and deployment
+
+### Bug Fixes and Other Changes
+
+ * Skip failing integ tests
+ * chore: add jumpstart gated model integration tests
+ * disable integration tests for Inference Component based endpoint for non supported regions
+ * remove unnecessary whl file - Galactus
+ * refactor endpoint type enums, comments, docstrings, method names…
+ * Address SA feedback regarding deployment straight to Endpoint Mode - Galactus
+ * fix HuggingFace GEN2 model deployment arguments
+ * when customer role does not have permission to read logs from CW, default to standard logging - Galactus
+ * Add pagination for ListInferenceComponent API responses, address non-blocking comments
+ * Create CSVSerializerWrapper - Galactus
+ * Model builder Final Fixes
+ * remote function include_local_workdir default value
+ * use copy instead of move in bootstrap script
+ * WaiterError on failed pipeline execution. results()
+ * Add a unit test for consistency between step and remote decorator
+ * merge WorkdirConfig and custom_filter_filter parameters
+ * Add back mixed step type integ test
+ * do not delete temp folder generated by sdist
+ * make collect_parameters decorator as private
+ * HMAC signing for ModelBuilder Triton python backend
+
+### Documentation Changes
+
+ * update docstring for Generation two endpoint and validation message
+ * galactus doc updates
+ * CustomFileFilter doc updates
+ * NotebookJobSteps class parameter severity update
+
+## v2.198.0 (2023-11-27)
+
+### Features
+
+ * re:Invent 2023 keynote1
+
+## v2.197.0 (2023-11-07)
+
+### Features
+
+ * PT2.1 SM Training/Inference DLC Release
+
+### Bug Fixes and Other Changes
+
+ * Release HuggingFace PT Neuronx training image 1.13.1
+ * HuggingFace PT Neuronx release in SDK
+
 ## v2.196.0 (2023-10-27)
 
 ### Features

MANIFEST.in

@@ -1,6 +1,7 @@
 recursive-include src/sagemaker *.py
 
 include src/sagemaker/image_uri_config/*.json
+include src/sagemaker/serve/requirements.txt
 recursive-include requirements *
 
 include VERSION

VERSION

@@ -1 +1 @@
-2.196.1.dev0
+2.199.1.dev0

doc/api/inference/model_builder.rst

@@ -0,0 +1,16 @@
+Model Builder
+-------------
+
+This module contains classes related to Amazon Sagemaker Model Builder
+
+.. autoclass:: sagemaker.serve.builder.model_builder.ModelBuilder
+
+.. automethod:: sagemaker.serve.builder.model_builder.ModelBuilder.build
+
+.. automethod:: sagemaker.serve.builder.model_builder.ModelBuilder.save
+
+.. autoclass:: sagemaker.serve.spec.inference_spec.InferenceSpec
+
+.. autoclass:: sagemaker.serve.builder.schema_builder.SchemaBuilder
+
+.. autoclass:: sagemaker.serve.marshalling.custom_payload_translator.CustomPayloadTranslator

doc/frameworks/pytorch/using_pytorch.rst

@@ -132,14 +132,49 @@ Using third-party libraries
 ---------------------------
 
 When running your training script on SageMaker, it will have access to some pre-installed third-party libraries including ``torch``, ``torchvision``, and ``numpy``.
-For more information on the runtime environment, including specific package versions, see `SageMaker PyTorch Docker containers <https://github.com/aws/deep-learning-containers/tree/master/pytorch>`_.
 
 If there are other packages you want to use with your script, you can include a ``requirements.txt`` file in the same directory as your training script to install other dependencies at runtime. Both ``requirements.txt`` and your training script should be put in the same folder. You must specify this folder in ``source_dir`` argument when creating PyTorch estimator.
 
 The function of installing packages using ``requirements.txt`` is supported for all PyTorch versions during training. When serving a PyTorch model, support for this function varies with PyTorch versions. For PyTorch 1.3.1 or newer, ``requirements.txt`` must be under folder ``code``. The SageMaker PyTorch Estimator will automatically save ``code`` in ``model.tar.gz`` after training (assuming you set up your script and ``requirements.txt`` correctly as stipulated in the previous paragraph). In the case of bringing your own trained model for deployment, you must save ``requirements.txt`` under folder ``code`` in ``model.tar.gz`` yourself or specify it through ``dependencies``. For PyTorch 1.2.0, ``requirements.txt`` is not supported for inference. For PyTorch 0.4.0 to 1.1.0, ``requirements.txt`` must be in ``source_dir``.
 
 A ``requirements.txt`` file is a text file that contains a list of items that are installed by using ``pip install``. You can also specify the version of an item to install. For information about the format of a ``requirements.txt`` file, see `Requirements Files <https://pip.pypa.io/en/stable/user_guide/#requirements-files>`__ in the pip documentation.
 
+If you were to use your own custom Docker Image, the `SageMaker Python SDK` and the `SageMaker Training Toolkit <https://github.com/aws/sagemaker-training-toolkit/>`__ need to be installed.
+
+To do so, you can add the following lines to your ``requirements.txt`` file:
+
+.. code:: text
+
+    sagemaker
+    sagemaker-training
+
+Deep Learning Framework-Specific SageMaker Toolkits and Containers
+------------------------------------------------------------------
+
+Framework-specific Toolkits exist. You might want to use them in your applications for framework-specific features.
+
+For Training Toolkits, see:
+
+- `SageMaker PyTorch Training Toolkit <https://github.com/aws/sagemaker-pytorch-training-toolkit>`__
+- `SageMaker MXNet Training Toolkit <https://github.com/aws/sagemaker-mxnet-training-toolkit>`__
+- `SageMaker TensorFlow Training Toolkit <https://github.com/aws/sagemaker-tensorflow-training-toolkit>`__
+
+For Inference Toolkits, see:
+
+- `SageMaker PyTorch Inference Toolkit <https://github.com/aws/sagemaker-pytorch-inference-toolkit>`__
+- `SageMaker MXNet Inference Toolkit <https://github.com/aws/sagemaker-mxnet-training-toolkit>`__
+- `SageMaker TensorFlow Inference Toolkit <https://github.com/aws/sagemaker-tensorflow-inference-toolkit>`__
+- `SageMaker HuggingFace Inference Toolkit <https://github.com/aws/sagemaker-huggingface-inference-toolkit>`__
+
+Moreover, for more information on the container runtime environment, including specific framework versions and configurations,
+see `AWS Deep Learning Containers <https://github.com/aws/deep-learning-containers/>`_. More specifically, see:
+
+- `Images for PyTorch <https://github.com/aws/deep-learning-containers/tree/master/pytorch>`__
+- `Images for MXNet <https://github.com/aws/deep-learning-containers/tree/master/mxnet>`__
+- `Images for TensorFlow <https://github.com/aws/deep-learning-containers/tree/master/tensorflow>`__
+- `Images for HuggingFace <https://github.com/aws/deep-learning-containers/tree/master/huggingface>`__
+
+
 Create an Estimator
 ===================