Merge branch 'master' into feature-3563-add-lambda-func-parameters

Author: dipanjank

CHANGELOG.md

@@ -1,5 +1,65 @@
 # Changelog
 
+## v2.138.0 (2023-03-13)
+
+### Features
+
+ * Jumpstart training metrics
+
+### Bug Fixes and Other Changes
+
+ * Add new region support for MX, PT, TF on SM Training
+
+## v2.137.0 (2023-03-10)
+
+### Features
+
+ * support JSON for input dataset and model output
+
+### Bug Fixes and Other Changes
+
+ * Wait on describe for tag propagation
+ * Extracted profile_name directly from sagemaker.Session if None
+ * Avoid double encoding to JSON in InferenceRecommenderMixin
+ * RepackStep must use the same KMS key as the Model
+
+## v2.136.0 (2023-03-09)
+
+### Features
+
+ * with_feature_group [feature_store]
+ * Djl Large Model Support
+ * Decouple model.right_size() from model registry
+
+### Bug Fixes and Other Changes
+
+ * Fix integration test error in test_default_right_size_and_deploy_unregistered_base_model
+ * Add djl 0.21.0 dlc images
+
+### Documentation Changes
+
+ * Torchrun gpu support documentation change
+
+## v2.135.1.post0 (2023-03-02)
+
+### Documentation Changes
+
+ * update feature store dataset builder docs
+
+## v2.135.1 (2023-03-01)
+
+### Bug Fixes and Other Changes
+
+ * Revert back to stable apache-airflow-providers-amazon from 7.2.1 to 4.0.0.
+ * Typo in graviton algos
+ * build(deps): bump apache-airflow-providers-amazon from 4.0.0 to 7.2.1 in /requirements/extras
+ * Support cloning private repo using ssh key
+ * Create a default SageMaker Session inside FeatureGroup class
+
+### Documentation Changes
+
+ * fix typo in README
+
 ## v2.135.0 (2023-02-23)
 
 ### Features

README.rst

@@ -126,7 +126,7 @@ To run the unit tests with tox, run:
 
     tox tests/unit
 
-**Integrations tests**
+**Integration tests**
 
 To run the integration tests, the following prerequisites must be met
 

VERSION

@@ -1 +1 @@
-2.135.1.dev0
+2.138.1.dev0

doc/amazon_sagemaker_featurestore.rst

@@ -380,6 +380,102 @@ location for the data set to be saved there.
 From here you can train a model using this data set and then perform
 inference.
 
+.. rubric:: Using the Offline Store SDK: Getting Started
+   :name: bCe9CA61b79
+
+The Feature Store Offline SDK provides the ability to quickly and easily
+build ML-ready datasets for use by ML model training or pre-processing.
+The SDK makes it easy to build datasets from SQL join, point-in-time accurate
+join, and event range time frames, all without the need to write any SQL code.
+This functionality is accessed via the DatasetBuilder class which is the
+primary entry point for the SDK functionality.
+
+.. code:: python
+
+   from sagemaker.feature_store.feature_store import FeatureStore
+
+   feature_store = FeatureStore(sagemaker_session=feature_store_session)
+
+.. code:: python
+
+   base_feature_group = identity_feature_group
+   target_feature_group = transaction_feature_group
+
+You can create dataset using `create_dataset` of feature store API.
+`base` can either be a feature group or a pandas dataframe.
+
+.. code:: python
+
+   result_df, query = feature_store.create_dataset(
+      base=base_feature_group,
+      output_path=f"s3://{s3_bucket_name}"
+   ).to_dataframe()
+
+If you want to join other feature group, you can specify extra
+feature group using `with_feature_group` method.
+
+.. code:: python
+
+   dataset_builder = feature_store.create_dataset(
+      base=base_feature_group,
+      output_path=f"s3://{s3_bucket_name}"
+   ).with_feature_group(target_feature_group, record_identifier_name)
+
+   result_df, query = dataset_builder.to_dataframe()
+
+.. rubric:: Using the Offline Store SDK: Configuring the DatasetBuilder
+   :name: bCe9CA61b80
+
+How the DatasetBuilder produces the resulting dataframe can be configured
+in various ways.
+
+By default the Python SDK will exclude all deleted and duplicate records.
+However if you need either of them in returned dataset, you can call
+`include_duplicated_records` or `include_deleted_records` when creating
+dataset builder.
+
+.. code:: python
+
+   dataset_builder.include_duplicated_records()
+   dataset_builder.include_deleted_records()
+
+The DatasetBuilder provides `with_number_of_records_from_query_results` and
+`with_number_of_recent_records_by_record_identifier` methods to limit the
+number of records returned for the offline snapshot.
+
+`with_number_of_records_from_query_results` will limit the number of records
+in the output. For example, when N = 100, only 100 records are going to be
+returned in either the csv or dataframe.
+
+.. code:: python
+
+   dataset_builder.with_number_of_records_from_query_results(number_of_records=N)
+
+On the other hand, `with_number_of_recent_records_by_record_identifier` is
+used to deal with records which have the same identifier. They are going
+to be sorted according to `event_time` and return at most N recent records
+in the output.
+
+.. code:: python
+
+   dataset_builder.with_number_of_recent_records_by_record_identifier(number_of_recent_records=N)
+
+Since these functions return the dataset builder, these functions can
+be chained.
+
+.. code:: python
+
+   dataset_builder
+      .with_number_of_records_from_query_results(number_of_records=N)
+      .include_duplicated_records()
+      .with_number_of_recent_records_by_record_identifier(number_of_recent_records=N)
+      .to_dataframe()
+
+There are additional configurations that can be made for various use cases,
+such as time travel and point-in-time join. These are outlined in the
+Feature Store `DatasetBuilder API Reference
+<https://sagemaker.readthedocs.io/en/stable/api/prep_data/feature_store.html#dataset-builder>`__.
+
 .. rubric:: Delete a feature group
    :name: bCe9CA61b78
 
@@ -395,3 +491,4 @@ The following code example is from the fraud detection example.
 
    identity_feature_group.delete()
    transaction_feature_group.delete()
+

doc/frameworks/djl/index.rst

@@ -0,0 +1,16 @@
+########################
+Deep Java Library (DJL)
+########################
+
+A managed environment for inference using Deep Java Library (DJL) on Amazon SageMaker.
+For general information about using the SageMaker Python SDK, see :ref:`overview:Using the SageMaker Python SDK`.
+
+.. toctree::
+    :maxdepth: 1
+
+    using_djl
+
+.. toctree::
+    :maxdepth: 2
+
+    sagemaker.djl_inference

doc/frameworks/djl/sagemaker.djl_inference.rst

@@ -0,0 +1,35 @@
+DJL Classes
+=================
+
+
+DJLModel
+---------------------------
+
+.. autoclass:: sagemaker.djl_inference.model.DJLModel
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+DeepSpeedModel
+---------------------------
+
+.. autoclass:: sagemaker.djl_inference.model.DeepSpeedModel
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+HuggingFaceAccelerateModel
+---------------------------
+
+.. autoclass:: sagemaker.djl_inference.model.HuggingFaceAccelerateModel
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+DJLPredictor
+---------------------------
+
+.. autoclass:: sagemaker.djl_inference.model.DJLPredictor
+    :members:
+    :undoc-members:
+    :show-inheritance: