Merge branch 'master' into master-safer-configs

Author: benieric

CHANGELOG.md

@@ -1,5 +1,42 @@
 # Changelog
 
+## v2.239.3 (2025-02-19)
+
+### Bug Fixes and Other Changes
+
+ * added ap-southeast-7 and mx-central-1 for Jumpstart
+ * update image_uri_configs  02-19-2025 06:18:15 PST
+
+## v2.239.2 (2025-02-18)
+
+### Bug Fixes and Other Changes
+
+ * Add warning about not supporting torch.nn.SyncBatchNorm
+ * pass in inference_ami_version to model_based endpoint type
+ * Fix hyperparameter strategy docs
+ * Add framework_version to all TensorFlowModel examples
+ * Move RecordSerializer and RecordDeserializer to sagemaker.serializers and sagemaker.deserialzers
+
+## v2.239.1 (2025-02-14)
+
+### Bug Fixes and Other Changes
+
+ * keep sagemaker_session from being overridden to None
+ * Fix all type hint and docstrings for callable
+ * Fix the workshop link for Step Functions
+ * Fix Tensorflow doc link
+ * Fix FeatureGroup docstring
+ * Add type hint for ProcessingOutput
+ * Fix sourcedir.tar.gz filenames in docstrings
+ * Fix documentation for local mode
+ * bug in get latest version was getting the max sorted alphabetically
+ * Add cleanup logic to model builder integ tests for endpoints
+ * Fixed pagination failing while listing collections
+ * fix ValueError when updating a data quality monitoring schedule
+ * Add docstring for image_uris.retrieve
+ * Create GitHub action to trigger canaries
+ * update image_uri_configs  02-04-2025 06:18:00 PST
+
 ## v2.239.0 (2025-02-01)
 
 ### Features

CONTRIBUTING.md

@@ -61,6 +61,10 @@ Before sending us a pull request, please ensure that:
    1. Follow the instructions at [Modifying an EBS Volume Using Elastic Volumes (Console)](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/requesting-ebs-volume-modifications.html#modify-ebs-volume) to increase the EBS volume size associated with the newly created EC2 instance.
    1. Wait 5-10min for the new EBS volume increase to finalize.
    1. Allow EC2 to claim the additional space by stopping and then starting your EC2 host.
+2. Set up a venv to manage dependencies:
+   1. `python -m venv ~/.venv/myproject-env` to create the venv
+   2. `source ~/.venv/myproject-env/bin/activate` to activate the venv
+   3. `deactivate` to exit the venv
 
 
 ### Pull Down the Code
@@ -74,8 +78,8 @@ Before sending us a pull request, please ensure that:
 ### Run the Unit Tests
 
 1. Install tox using `pip install tox`
-1. Install coverage using `pip install .[test]`
-1. cd into the sagemaker-python-sdk folder: `cd sagemaker-python-sdk` or `cd /environment/sagemaker-python-sdk`
+1. cd into the github project sagemaker-python-sdk folder: `cd sagemaker-python-sdk` or `cd /environment/sagemaker-python-sdk`
+1. Install coverage using `pip install '.[test]'`
 1. Run the following tox command and verify that all code checks and unit tests pass: `tox tests/unit`
 1. You can also run a single test with the following command: `tox -e py310 -- -s -vv <path_to_file><file_name>::<test_function_name>`
 1. You can run coverage via runcvoerage env : `tox -e runcoverage -- tests/unit` or `tox -e py310 -- tests/unit --cov=sagemaker --cov-append --cov-report xml`

VERSION

@@ -1 +1 @@
-2.239.1.dev0
+2.239.4.dev0

doc/frameworks/pytorch/using_pytorch.rst

@@ -375,6 +375,9 @@ To initialize distributed training in your script, call
 `torch.distributed.init_process_group
 <https://pytorch.org/docs/master/distributed.html#torch.distributed.init_process_group>`_
 with the desired backend and the rank of the current host.
+Warning: Some torch features, such as (and likely not limited to) ``torch.nn.SyncBatchNorm``
+is not supported and its existence in ``init_process_group`` will cause an exception during
+distributed training.
 
 .. code:: python
 

doc/frameworks/tensorflow/deploying_tensorflow_serving.rst

@@ -64,7 +64,7 @@ If you already have existing model artifacts in S3, you can skip training and de
 
   from sagemaker.tensorflow import TensorFlowModel
 
-  model = TensorFlowModel(model_data='s3://mybucket/model.tar.gz', role='MySageMakerRole')
+  model = TensorFlowModel(model_data='s3://mybucket/model.tar.gz', role='MySageMakerRole', framework_version='x.x.x')
 
   predictor = model.deploy(initial_instance_count=1, instance_type='ml.c5.xlarge')
 
@@ -74,7 +74,7 @@ Python-based TensorFlow serving on SageMaker has support for `Elastic Inference
 
     from sagemaker.tensorflow import TensorFlowModel
 
-    model = TensorFlowModel(model_data='s3://mybucket/model.tar.gz', role='MySageMakerRole')
+    model = TensorFlowModel(model_data='s3://mybucket/model.tar.gz', role='MySageMakerRole', framework_version='x.x.x')
 
     predictor = model.deploy(initial_instance_count=1, instance_type='ml.c5.xlarge', accelerator_type='ml.eia1.medium')
 

doc/frameworks/tensorflow/using_tf.rst

@@ -468,7 +468,7 @@ If you already have existing model artifacts in S3, you can skip training and de
 
   from sagemaker.tensorflow import TensorFlowModel
 
-  model = TensorFlowModel(model_data='s3://mybucket/model.tar.gz', role='MySageMakerRole')
+  model = TensorFlowModel(model_data='s3://mybucket/model.tar.gz', role='MySageMakerRole', framework_version='x.x.x')
 
   predictor = model.deploy(initial_instance_count=1, instance_type='ml.c5.xlarge')
 
@@ -478,7 +478,7 @@ Python-based TensorFlow serving on SageMaker has support for `Elastic Inference
 
     from sagemaker.tensorflow import TensorFlowModel
 
-    model = TensorFlowModel(model_data='s3://mybucket/model.tar.gz', role='MySageMakerRole')
+    model = TensorFlowModel(model_data='s3://mybucket/model.tar.gz', role='MySageMakerRole', framework_version='x.x.x')
 
     predictor = model.deploy(initial_instance_count=1, instance_type='ml.c5.xlarge', accelerator_type='ml.eia1.medium')
 
@@ -767,7 +767,8 @@ This customized Python code must be named ``inference.py`` and is specified thro
 
     model = TensorFlowModel(entry_point='inference.py',
                             model_data='s3://mybucket/model.tar.gz',
-                            role='MySageMakerRole')
+                            role='MySageMakerRole',
+                            framework_version='x.x.x')
 
 In the example above, ``inference.py`` is assumed to be a file inside ``model.tar.gz``. If you want to use a local file instead, you must add the ``source_dir`` argument. See the documentation on `TensorFlowModel <https://sagemaker.readthedocs.io/en/stable/frameworks/tensorflow/sagemaker.tensorflow.html#sagemaker.tensorflow.model.TensorFlowModel>`_.
 
@@ -923,7 +924,8 @@ processing. There are 2 ways to do this:
     model = TensorFlowModel(entry_point='inference.py',
                             dependencies=['requirements.txt'],
                             model_data='s3://mybucket/model.tar.gz',
-                            role='MySageMakerRole')
+                            role='MySageMakerRole',
+                            framework_version='x.x.x')
 
 
 2. If you are working in a network-isolation situation or if you don't
@@ -941,7 +943,8 @@ processing. There are 2 ways to do this:
     model = TensorFlowModel(entry_point='inference.py',
                            dependencies=['/path/to/folder/named/lib'],
                            model_data='s3://mybucket/model.tar.gz',
-                           role='MySageMakerRole')
+                           role='MySageMakerRole',
+                           framework_version='x.x.x')
 
 For more information, see: https://github.com/aws/sagemaker-tensorflow-serving-container#prepost-processing
 

doc/v2.rst

@@ -324,9 +324,9 @@ The follow serializer/deserializer classes have been renamed and/or moved:
 +--------------------------------------------------------+-------------------------------------------------------+
 | ``sagemaker.predictor._NPYSerializer``                 | ``sagemaker.serializers.NumpySerializer``             |
 +--------------------------------------------------------+-------------------------------------------------------+
-| ``sagemaker.amazon.common.numpy_to_record_serializer`` | ``sagemaker.amazon.common.RecordSerializer``          |
+| ``sagemaker.amazon.common.numpy_to_record_serializer`` | ``sagemaker.serializers.RecordSerializer``            |
 +--------------------------------------------------------+-------------------------------------------------------+
-| ``sagemaker.amazon.common.record_deserializer``        | ``sagemaker.amazon.common.RecordDeserializer``        |
+| ``sagemaker.amazon.common.record_deserializer``        | ``sagemaker.deserializers.RecordDeserializer``        |
 +--------------------------------------------------------+-------------------------------------------------------+
 | ``sagemaker.predictor._JsonDeserializer``              | ``sagemaker.deserializers.JSONDeserializer``          |
 +--------------------------------------------------------+-------------------------------------------------------+