Merge branch 'aws:master' into model_config

Author: oyangz

CHANGELOG.md

@@ -1,5 +1,55 @@
 # Changelog
 
+## v2.131.1 (2023-02-03)
+
+### Bug Fixes and Other Changes
+
+ * test dub gpu integs with p3
+ * fix(experiments/run.py): Stop duplication of RUN_TC_TAG on Consecutive Experiment Runs
+ * Enable load_run without name args in Transform env
+ * Remove confusing log line emitted during feature group ingestion
+ * Enable Experiment integ test on beta clients
+ * Make test_processor_with_role_as_pipeline_parameter more concrete
+
+### Documentation Changes
+
+ * add security note for the estimator hyperparameter arg
+ * SageMaker distributed - model parallism library release note
+ * Add a deprecation note for DetailedProfileConfig
+
+## v2.131.0 (2023-01-31)
+
+### Features
+
+ * Display file diff on black-check
+ * Support for environment variables in the HPO
+ * Support role as PipelineParameter in Processor class
+ * Add TrainingImageConfig support for SageMaker training jobs
+
+### Bug Fixes and Other Changes
+
+ * use FeatureGroup's Session in nonconcurrency ingestion
+ * Update feature_group.py ingest() description
+ * Do not use print function. User logger instead
+ * Add batch_get_record and search API for FeatureStore
+ * hashing problem for framework processors with identical source dirs
+
+## v2.130.0 (2023-01-26)
+
+### Features
+
+ * Add PyTorch 1.13.1 to SDK
+ * Adding image_uri config for DJL containers
+ * Support specifying env-vars when creating model from model package
+ * local download dir for Model and Estimator classes
+
+### Bug Fixes and Other Changes
+
+ * increase creation time slack minutes
+ * Enable load_run auto pass in experiment config
+ * Add us-isob-east-1 accounts and configs
+ * Clean up Pipeline unit tests
+
 ## v2.129.0 (2023-01-19)
 
 ### Features

VERSION

@@ -1 +1 @@
-2.129.1.dev0
+2.131.2.dev0

doc/api/training/smd_model_parallel_release_notes/smd_model_parallel_change_log.rst

@@ -6,9 +6,47 @@ New features, bug fixes, and improvements are regularly made to the SageMaker
 distributed model parallel library.
 
 
-SageMaker Distributed Model Parallel 1.13.0 Release Notes
+SageMaker Distributed Model Parallel 1.14.0 Release Notes
 =========================================================
 
+*Date: Jan. 30. 2023*
+
+**Currency Updates**
+
+* Added support for PyTorch v1.13.1
+
+**Improvements**
+
+* Upgraded the flash-attention (https://github.com/HazyResearch/flash-attention) library to  v0.2.6.post1
+
+**Migration to AWS Deep Learning Containers**
+
+This version passed benchmark testing and is migrated to the following AWS Deep Learning Containers (DLC):
+
+- SageMaker training container for PyTorch v1.13.1
+
+  .. code::
+
+    763104351884.dkr.ecr.<region>.amazonaws.com/pytorch-training:1.13.1-gpu-py39-cu117-ubuntu20.04-sagemaker
+
+
+Binary file of this version of the library for `custom container
+<https://docs.aws.amazon.com/sagemaker/latest/dg/model-parallel-sm-sdk.html#model-parallel-bring-your-own-container>`_ users:
+
+- For PyTorch 1.13.1
+
+  .. code::
+
+    https://sagemaker-distributed-model-parallel.s3.us-west-2.amazonaws.com/pytorch-1.13.1/build-artifacts/2023-01-19-18-35/smdistributed_modelparallel-1.14.0-cp39-cp39-linux_x86_64.whl
+
+----
+
+Release History
+===============
+
+SageMaker Distributed Model Parallel 1.13.0 Release Notes
+---------------------------------------------------------
+
 *Date: Dec. 15. 2022*
 
 **New Features**
@@ -46,16 +84,12 @@ This version passed benchmark testing and is migrated to the following AWS Deep
 Binary file of this version of the library for `custom container
 <https://docs.aws.amazon.com/sagemaker/latest/dg/model-parallel-sm-sdk.html#model-parallel-bring-your-own-container>`_ users:
 
-- For PyTorch 1.12.0
+- For PyTorch 1.12.1
 
   .. code::
 
     https://sagemaker-distributed-model-parallel.s3.us-west-2.amazonaws.com/pytorch-1.12.1/build-artifacts/2022-12-08-21-34/smdistributed_modelparallel-1.13.0-cp38-cp38-linux_x86_64.whl
 
-----
-
-Release History
-===============
 
 SageMaker Distributed Model Parallel 1.11.0 Release Notes
 ---------------------------------------------------------
@@ -92,7 +126,7 @@ Binary file of this version of the library for `custom container
 
   .. code::
 
-    https://sagemaker-distribu
+    https://sagemaker-distributed-model-parallel.s3.us-west-2.amazonaws.com/pytorch-1.12.0/build-artifacts/2022-08-12-16-58/smdistributed_modelparallel-1.11.0-cp38-cp38-linux_x86_64.whl
 
 SageMaker Distributed Model Parallel 1.10.1 Release Notes
 ---------------------------------------------------------

doc/api/training/smp_versions/latest.rst

@@ -10,8 +10,8 @@ depending on which version of the library you need to use.
 To use the library, reference the
 **Common API** documentation alongside the framework specific API documentation.
 
-Version 1.11.0, 1.13.0 (Latest)
-===============================
+Version 1.11.0, 1.13.0, 1.14.0 (Latest)
+=======================================
 
 To use the library, reference the Common API documentation alongside the framework specific API documentation.
 

src/sagemaker/debugger/framework_profile.py

@@ -143,6 +143,12 @@ def __init__(
                 profiling. Configure it using the
                 :class:`~sagemaker.debugger.metrics_config.DetailedProfilingConfig` class.
                 Pass ``DetailedProfilingConfig()`` to use the default configuration.
+
+                .. warning::
+                    This detailed framework profiling feature discontinues support for TensorFlow v2.11
+                    and later. To use the detailed profiling feature, use previous versions of
+                    TensorFlow between v2.3.1 and v2.10.0.
+
             dataloader_profiling_config (DataloaderProfilingConfig): The configuration for
                 dataloader metrics profiling. Configure it using the
                 :class:`~sagemaker.debugger.metrics_config.DataloaderProfilingConfig` class.

src/sagemaker/debugger/metrics_config.py

@@ -203,8 +203,7 @@ def __init__(
     ):
         """Specify target steps or a target duration to profile.
 
-        By default, it profiles step 5
-        of training.
+        By default, it profiles step 5 of the training job.
 
         If **profile_default_steps** is set to `True` and none of the other
         range parameters is specified,
@@ -224,6 +223,11 @@ def __init__(
             if one of the two pairs is used. If both pairs are specified, a
             conflict error occurs.
 
+        .. warning::
+            This detailed framework profiling feature discontinues support for TensorFlow v2.11
+            and later. To use the detailed profiling feature, use previous versions of
+            TensorFlow between v2.3.1 and v2.10.0.
+
         """
         assert isinstance(
             profile_default_steps, bool