feature: Add business details and hyper parameters fields and update test_model_card.py

Author: Haonian Wang

CHANGELOG.md

@@ -1,5 +1,22 @@
 # 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

VERSION

@@ -1 +1 @@
-2.131.1.dev0
+2.131.2.dev0

doc/api/governance/model_card.rst

@@ -41,3 +41,9 @@ see `Amazon SageMaker Model Cards <https://docs.aws.amazon.com/sagemaker/latest/
 
 .. autoclass:: TrainingJobDetails
     :show-inheritance:
+
+.. autoclass:: BusinessDetails
+    :show-inheritance:
+
+.. autoclass:: HyperParameter
+    :show-inheritance:

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/estimator.py

@@ -423,6 +423,13 @@ def __init__(
             hyperparameters (dict[str, str] or dict[str, PipelineVariable]):
                 A dictionary containing the hyperparameters to
                 initialize this estimator with. (Default: None).
+
+                .. caution::
+                    You must not include any security-sensitive information, such as
+                    account access IDs, secrets, and tokens, in the dictionary for configuring
+                    hyperparameters. SageMaker rejects the training job request and returns an
+                    validation error for detected credentials, if such user input is found.
+
             container_log_level (int or PipelineVariable): The log level to use within the container
                 (default: logging.INFO). Valid values are defined in the Python
                 logging module.
@@ -1335,6 +1342,7 @@ def deploy(
         volume_size=None,
         model_data_download_timeout=None,
         container_startup_health_check_timeout=None,
+        inference_recommendation_id=None,
         **kwargs,
     ):
         """Deploy the trained model to an Amazon SageMaker endpoint.
@@ -1412,6 +1420,9 @@ def deploy(
                 inference container to pass health check by SageMaker Hosting. For more information
                 about health check see:
                 https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-inference-code.html#your-algorithms-inference-algo-ping-requests
+            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.
             **kwargs: Passed to invocation of ``create_model()``.
                 Implementations may customize ``create_model()`` to accept
                 ``**kwargs`` to customize model creation during deploy.
@@ -1476,6 +1487,7 @@ def deploy(
             volume_size=volume_size,
             model_data_download_timeout=model_data_download_timeout,
             container_startup_health_check_timeout=container_startup_health_check_timeout,
+            inference_recommendation_id=inference_recommendation_id,
         )
 
     def register(
@@ -2462,6 +2474,13 @@ def __init__(
                 using the default AWS configuration chain.
             hyperparameters (dict[str, str] or dict[str, PipelineVariable]):
                 Dictionary containing the hyperparameters to initialize this estimator with.
+
+                .. caution::
+                    You must not include any security-sensitive information, such as
+                    account access IDs, secrets, and tokens, in the dictionary for configuring
+                    hyperparameters. SageMaker rejects the training job request and returns an
+                    validation error for detected credentials, if such user input is found.
+
             tags (list[dict[str, str] or list[dict[str, PipelineVariable]]): List of tags for
                 labeling a training job. For more, see
                 https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.
@@ -2939,6 +2958,13 @@ def __init__(
                 SageMaker. For convenience, this accepts other types for keys
                 and values, but ``str()`` will be called to convert them before
                 training.
+
+                .. caution::
+                    You must not include any security-sensitive information, such as
+                    account access IDs, secrets, and tokens, in the dictionary for configuring
+                    hyperparameters. SageMaker rejects the training job request and returns an
+                    validation error for detected credentials, if such user input is found.
+
             container_log_level (int or PipelineVariable): Log level to use within the container
                 (default: logging.INFO). Valid values are defined in the Python
                 logging module.

src/sagemaker/huggingface/model.py

@@ -209,6 +209,7 @@ def deploy(
         volume_size=None,
         model_data_download_timeout=None,
         container_startup_health_check_timeout=None,
+        inference_recommendation_id=None,
         **kwargs,
     ):
         """Deploy this ``Model`` to an ``Endpoint`` and optionally return a ``Predictor``.
@@ -282,6 +283,9 @@ def deploy(
                 inference container to pass health check by SageMaker Hosting. For more information
                 about health check see:
                 https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-inference-code.html#your-algorithms-inference-algo-ping-requests
+            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.
         Raises:
              ValueError: If arguments combination check failed in these circumstances:
                 - If no role is specified or
@@ -317,6 +321,7 @@ def deploy(
             volume_size=volume_size,
             model_data_download_timeout=model_data_download_timeout,
             container_startup_health_check_timeout=container_startup_health_check_timeout,
+            inference_recommendation_id=inference_recommendation_id,
         )
 
     def register(