Merge branch 'master' into cv_s3_upload

Author: Satish615

CHANGELOG.md

@@ -1,5 +1,24 @@
 # Changelog
 
+## v2.144.0 (2023-04-05)
+
+### Features
+
+ * support create Clarify explainer enabled endpoint for Clarify Online Explainability
+ * Combined inference and training script artifact
+ * jumpstart instance types
+ * Deprecation warning for framework profiling for TF 2.12 and on, PT 2.0 and on
+
+### Bug Fixes and Other Changes
+
+ * always delete temporary directory even during exception
+ * Fixes the completion_criteria_config dict in the to_input_req method
+ * Update CHANGELOG.md
+
+### Documentation Changes
+
+ * Update SageMaker Debugger doc
+
 ## v2.143.0 (2023-03-29)
 
 ### Features

VERSION

@@ -1 +1 @@
-2.143.1.dev0
+2.144.1.dev0

doc/amazon_sagemaker_debugger.rst

@@ -4,6 +4,12 @@ Amazon SageMaker Debugger
 #########################
 
 
+.. warning::
+
+  This page is no longer supported for maintenence. The live documentation is at `Debug and Profile Training Jobs Using Amazon SageMaker Debugger <https://docs.aws.amazon.com/sagemaker/latest/dg/train-debugger.html>`_
+  and `Debugger API <https://sagemaker.readthedocs.io/en/stable/api/training/debugger.html>`_.
+
+
 Amazon SageMaker Debugger allows you to detect anomalies while training your machine learning model by emitting relevant data during training, storing the data and then analyzing it.
 
 .. contents::

doc/api/inference/explainer.rst

@@ -0,0 +1,16 @@
+Online Explainability
+---------------------
+
+This module contains classes related to Amazon Sagemaker Clarify Online Explainability
+
+.. automodule:: sagemaker.explainer.explainer_config
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+.. automodule:: sagemaker.explainer.clarify_explainer_config
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+

doc/api/training/debugger.rst

@@ -10,8 +10,13 @@ Configure the Debugger-specific parameters when constructing
 a SageMaker estimator to gain visibility and insights
 into your training job.
 
+.. contents::
+
 .. currentmodule:: sagemaker.debugger
 
+Debugger Rule APIs
+~~~~~~~~~~~~~~~~~~
+
 .. autoclass:: get_rule_container_image_uri
     :show-inheritance:
 
@@ -44,6 +49,9 @@ into your training job.
     :show-inheritance:
     :inherited-members:
 
+Debugger Configuration APIs
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
 .. autoclass:: CollectionConfig
     :show-inheritance:
 
@@ -56,6 +64,21 @@ into your training job.
 .. autoclass:: ProfilerConfig
     :show-inheritance:
 
+Debugger Configuration APIs for Framework Profiling (Deprecated)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. warning::
+
+  SageMaker Debugger deprecates the framework profiling feature starting from TensorFlow 2.11 and PyTorch 2.0. You can still use the feature in the previous versions of the frameworks and SDKs as follows.
+
+  * SageMaker Python SDK <= v2.130.0
+  * PyTorch >= v1.6.0, < v2.0
+  * TensorFlow >= v2.3.1, < v2.11
+
+  With the deprecation, SageMaker Debugger discontinues support for the APIs below this note.
+
+  See also `Amazon SageMaker Debugger Release Notes: March 16, 2023 <https://docs.aws.amazon.com/sagemaker/latest/dg/debugger-release-notes.html#debugger-release-notes-20230315>`_.
+
 .. autoclass:: FrameworkProfile
     :show-inheritance:
 

src/sagemaker/estimator.py

@@ -1378,6 +1378,7 @@ def deploy(
         model_data_download_timeout=None,
         container_startup_health_check_timeout=None,
         inference_recommendation_id=None,
+        explainer_config=None,
         **kwargs,
     ):
         """Deploy the trained model to an Amazon SageMaker endpoint.
@@ -1458,6 +1459,8 @@ def deploy(
             inference_recommendation_id (str): The recommendation id which specifies the
                 recommendation you picked from inference recommendation job results and
                 would like to deploy the model and endpoint with recommended parameters.
+            explainer_config (sagemaker.explainer.ExplainerConfig): Specifies online explainability
+                configuration for use with Amazon SageMaker Clarify. (default: None)
             **kwargs: Passed to invocation of ``create_model()``.
                 Implementations may customize ``create_model()`` to accept
                 ``**kwargs`` to customize model creation during deploy.
@@ -1516,6 +1519,7 @@ def deploy(
             data_capture_config=data_capture_config,
             serverless_inference_config=serverless_inference_config,
             async_inference_config=async_inference_config,
+            explainer_config=explainer_config,
             volume_size=volume_size,
             model_data_download_timeout=model_data_download_timeout,
             container_startup_health_check_timeout=container_startup_health_check_timeout,

src/sagemaker/explainer/__init__.py

@@ -0,0 +1,24 @@
+# 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.
+"""Imports the classes in this module to simplify customer imports"""
+
+from __future__ import absolute_import
+
+from sagemaker.explainer.explainer_config import ExplainerConfig  # noqa: F401
+from sagemaker.explainer.clarify_explainer_config import (  # noqa: F401
+    ClarifyExplainerConfig,
+    ClarifyInferenceConfig,
+    ClarifyShapConfig,
+    ClarifyShapBaselineConfig,
+    ClarifyTextConfig,
+)