Merge branch 'master' into estimator-hyperparam-security-note

Author: mchoi8739

.github/PULL_REQUEST_TEMPLATE.md

@@ -11,7 +11,7 @@ _Put an `x` in the boxes that apply. You can also fill these out after creating
 #### General
 
 - [ ] I have read the [CONTRIBUTING](https://github.com/aws/sagemaker-python-sdk/blob/master/CONTRIBUTING.md) doc
-- [ ] I certify that the changes I am introducing will be backword compatible, and I have discussed concerns about this, if any, with the Python SDK team
+- [ ] I certify that the changes I am introducing will be backward compatible, and I have discussed concerns about this, if any, with the Python SDK team
 - [ ] I used the commit message format described in [CONTRIBUTING](https://github.com/aws/sagemaker-python-sdk/blob/master/CONTRIBUTING.md#committing-your-change)
 - [ ] I have passed the region in to all S3 and STS clients that I've initialized as part of this change.
 - [ ] I have updated any necessary documentation, including [READMEs](https://github.com/aws/sagemaker-python-sdk/blob/master/README.rst) and [API docs](https://github.com/aws/sagemaker-python-sdk/tree/master/doc) (if appropriate)

CHANGELOG.md

@@ -1,5 +1,87 @@
 # Changelog
 
+## v2.112.2 (2022-10-11)
+
+### Bug Fixes and Other Changes
+
+ * Update Neo-TF2.x versions to TF2.9(.2)
+
+### Documentation Changes
+
+ * fix typo in PR template
+
+## v2.112.1 (2022-10-10)
+
+### Bug Fixes and Other Changes
+
+ * fix(local-mode): loosen docker requirement to allow 6.0.0
+ * CreateModelPackage API error for Scikit-learn and XGBoost frameworkss
+
+## v2.112.0 (2022-10-09)
+
+### Features
+
+ * added monitor batch transform step (pipeline)
+
+### Bug Fixes and Other Changes
+
+ * Add PipelineVariable annotation to framework estimators
+
+## v2.111.0 (2022-10-05)
+
+### Features
+
+ * Edit test file for supporting TF 2.10 training
+
+### Bug Fixes and Other Changes
+
+ * support kms key in processor pack local code
+ * security issue by bumping apache-airflow from 2.3.4 to 2.4.0
+ * instance count retrieval logic
+ * Add regex for short-form sagemaker-xgboost tags
+ * Upgrade attrs>=20.3.0,<23
+ * Add PipelineVariable annotation to Amazon estimators
+
+### Documentation Changes
+
+ * add context for pytorch
+
+## v2.110.0 (2022-09-27)
+
+### Features
+
+ * Support KeepAlivePeriodInSeconds for Training APIs
+ * added ANALYSIS_CONFIG_SCHEMA_V1_0 in clarify
+ * add model monitor image accounts for ap-southeast-3
+
+### Bug Fixes and Other Changes
+
+ * huggingface release test
+ * Fixing the logic to return instanceCount for heterogeneousClusters
+ * Disable type hints in doc signature and add PipelineVariable annotations in docstring
+ * estimator hyperparameters in script mode
+
+### Documentation Changes
+
+ * Added link to example notebook for Pipelines local mode
+
+## v2.109.0 (2022-09-09)
+
+### Features
+
+ * add search filters
+
+### Bug Fixes and Other Changes
+
+ * local pipeline step argument parsing bug
+ * support fail_on_violation flag for check steps
+ * fix links per app security scan
+ * Add PipelineVariable annotation for all processor subclasses
+
+### Documentation Changes
+
+ * the SageMaker model parallel library 1.11.0 release
+
 ## v2.108.0 (2022-09-02)
 
 ### Features

VERSION

@@ -1 +1 @@
-2.108.1.dev0
+2.112.3.dev0

doc/conf.py

@@ -96,6 +96,13 @@
 # Example configuration for intersphinx: refer to the Python standard library.
 intersphinx_mapping = {"http://docs.python.org/": None}
 
+# -- Options for autodoc ----------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/extensions/autodoc.html#configuration
+
+# Automatically extract typehints when specified and place them in
+# descriptions of the relevant function/method.
+autodoc_typehints = "description"
+
 # autosummary
 autosummary_generate = True
 

doc/frameworks/pytorch/using_pytorch.rst

@@ -415,20 +415,25 @@ Before a model can be served, it must be loaded. The SageMaker PyTorch model ser
 
 .. code:: python
 
-    def model_fn(model_dir)
+    def model_fn(model_dir, context)
+
+``context`` is an optional argument that contains additional serving information, such as the GPU ID and batch size.
+If specified in the function declaration, the context will be created and passed to the function by SageMaker.
+For more information about ``context``, see the `Serving Context class <https://github.com/pytorch/serve/blob/master/ts/context.py>`_.
 
 SageMaker will inject the directory where your model files and sub-directories, saved by ``save``, have been mounted.
 Your model function should return a model object that can be used for model serving.
 
 The following code-snippet shows an example ``model_fn`` implementation.
-It loads the model parameters from a ``model.pth`` file in the SageMaker model directory ``model_dir``.
+It loads the model parameters from a ``model.pth`` file in the SageMaker model directory ``model_dir``. As explained in the preceding example,
+``context`` is an optional argument that passes additional information.
 
 .. code:: python
 
     import torch
     import os
 
-    def model_fn(model_dir):
+    def model_fn(model_dir, context):
         model = Your_Model()
         with open(os.path.join(model_dir, 'model.pth'), 'rb') as f:
             model.load_state_dict(torch.load(f))
@@ -482,13 +487,13 @@ function in the chain. Inside the SageMaker PyTorch model server, the process lo
 .. code:: python
 
     # Deserialize the Invoke request body into an object we can perform prediction on
-    input_object = input_fn(request_body, request_content_type)
+    input_object = input_fn(request_body, request_content_type, context)
 
     # Perform prediction on the deserialized object, with the loaded model
-    prediction = predict_fn(input_object, model)
+    prediction = predict_fn(input_object, model, context)
 
     # Serialize the prediction result into the desired response content type
-    output = output_fn(prediction, response_content_type)
+    output = output_fn(prediction, response_content_type, context)
 
 The above code sample shows the three function definitions:
 
@@ -536,9 +541,13 @@ it should return an object that can be passed to ``predict_fn`` and have the fol
 
 .. code:: python
 
-    def input_fn(request_body, request_content_type)
+    def input_fn(request_body, request_content_type, context)
 
-Where ``request_body`` is a byte buffer and ``request_content_type`` is a Python string
+Where ``request_body`` is a byte buffer and ``request_content_type`` is a Python string.
+
+``context`` is an optional argument that contains additional serving information, such as the GPU ID and batch size.
+If specified in the function declaration, the context will be created and passed to the function by SageMaker.
+For more information about ``context``, see the `Serving Context class <https://github.com/pytorch/serve/blob/master/ts/context.py>`_.
 
 The SageMaker PyTorch model server provides a default implementation of ``input_fn``.
 This function deserializes JSON, CSV, or NPY encoded data into a torch.Tensor.
@@ -586,16 +595,19 @@ The ``predict_fn`` function has the following signature:
 
 .. code:: python
 
-    def predict_fn(input_object, model)
+    def predict_fn(input_object, model, context)
 
 Where ``input_object`` is the object returned from ``input_fn`` and
 ``model`` is the model loaded by ``model_fn``.
+If you are using multiple GPUs, then specify the ``context`` argument, which contains information such as the GPU ID for a dynamically-selected GPU and the batch size.
+One of the examples below demonstrates how to configure ``predict_fn`` with the ``context`` argument to handle multiple GPUs. For more information about ``context``, see the `Serving Context class <https://github.com/pytorch/serve/blob/master/ts/context.py>`_.
+If you are using CPUs or a single GPU, then you do not need to specify the ``context`` argument.
 
 The default implementation of ``predict_fn`` invokes the loaded model's ``__call__`` function on ``input_object``,
 and returns the resulting value. The return-type should be a torch.Tensor to be compatible with the default
 ``output_fn``.
 
-The example below shows an overridden ``predict_fn``:
+The following example shows an overridden ``predict_fn``:
 
 .. code:: python
 
@@ -609,6 +621,20 @@ The example below shows an overridden ``predict_fn``:
         with torch.no_grad():
             return model(input_data.to(device))
 
+The following example is for use cases with multiple GPUs and shows an overridden ``predict_fn`` that uses the ``context`` argument to dynamically select a GPU device for making predictions:
+
+.. code:: python
+
+    import torch
+    import numpy as np
+
+    def predict_fn(input_data, model):
+        device = torch.device("cuda:" + str(context.system_properties.get("gpu_id")) if torch.cuda.is_available() else "cpu")
+        model.to(device)
+        model.eval()
+        with torch.no_grad():
+            return model(input_data.to(device))
+
 If you implement your own prediction function, you should take care to ensure that:
 
 -  The first argument is expected to be the return value from input_fn.
@@ -664,11 +690,14 @@ The ``output_fn`` has the following signature:
 
 .. code:: python
 
-    def output_fn(prediction, content_type)
+    def output_fn(prediction, content_type, context)
 
 Where ``prediction`` is the result of invoking ``predict_fn`` and
-the content type for the response, as specified by the InvokeEndpoint request.
-The function should return a byte array of data serialized to content_type.
+the content type for the response, as specified by the InvokeEndpoint request. The function should return a byte array of data serialized to ``content_type``.
+
+``context`` is an optional argument that contains additional serving information, such as the GPU ID and batch size.
+If specified in the function declaration, the context will be created and passed to the function by SageMaker.
+For more information about ``context``, see the `Serving Context class <https://github.com/pytorch/serve/blob/master/ts/context.py>`_.
 
 The default implementation expects ``prediction`` to be a torch.Tensor and can serialize the result to JSON, CSV, or NPY.
 It accepts response content types of "application/json", "text/csv", and "application/x-npy".

doc/overview.rst

@@ -1565,6 +1565,8 @@ For detailed examples of running Docker in local mode, see:
 - `TensorFlow local mode example notebook <https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker-python-sdk/tensorflow_script_mode_using_shell_commands/tensorflow_script_mode_using_shell_commands.ipynb>`__.
 - `MXNet local mode example notebook <https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker-python-sdk/mxnet_gluon_mnist/mxnet_mnist_with_gluon_local_mode.ipynb>`__.
 - `PyTorch local mode example notebook <https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker-python-sdk/pytorch_cnn_cifar10/pytorch_local_mode_cifar10.ipynb>`__.
+- `Pipelines local mode example notebook <https://github.com/aws/amazon-sagemaker-examples/blob/main/sagemaker-pipelines/tabular/local-mode/sagemaker-pipelines-local-mode.ipynb>`__.
+
 
 You can also find these notebooks in the **SageMaker Python SDK** section of the **SageMaker Examples** section in a notebook instance.
 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>`__

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

@@ -46,6 +46,8 @@ Entities
 
 .. autoclass:: sagemaker.workflow.entities.Expression
 
+.. autoclass:: sagemaker.workflow.entities.PipelineVariable
+
 Execution Variables
 -------------------
 

requirements/extras/local_requirements.txt

@@ -1,4 +1,4 @@
 urllib3==1.26.8
 docker-compose==1.29.2
-docker~=5.0.0
+docker>=5.0.2,<7.0.0
 PyYAML==5.4.1

requirements/extras/test_requirements.txt

@@ -11,9 +11,9 @@ contextlib2==21.6.0
 awslogs==0.14.0
 black==22.3.0
 stopit==1.1.2
-apache-airflow==2.3.4
+apache-airflow==2.4.0
 apache-airflow-providers-amazon==4.0.0
-attrs==20.3.0
+attrs==22.1.0
 fabric==2.6.0
 requests==2.27.1
 sagemaker-experiments==0.1.35

setup.py

@@ -47,7 +47,7 @@ def read_requirements(filename):
 
 # Declare minimal set for installation
 required_packages = [
-    "attrs>=20.3.0,<22",
+    "attrs>=20.3.0,<23",
     "boto3>=1.20.21,<2.0",
     "google-pasta",
     "numpy>=1.9.0,<2.0",
@@ -58,6 +58,7 @@ def read_requirements(filename):
     "packaging>=20.0",
     "pandas",
     "pathos",
+    "schema",
 ]
 
 # Specific use case dependencies