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‎CHANGELOG.md

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# Changelog
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## v2.190.0 (2023-10-04)
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### Features
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* Add support for in-memory feature groups and collection type features in Feature Store.
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### Bug Fixes and Other Changes
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* chore: xfail resource in use failure for specific test
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* Add missing API docs for processors
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### Documentation Changes
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* Bring back (de)serializers documentation
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* Add missing AirFlow operators + link to airflow documentation
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## v2.189.0 (2023-10-03)
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### Features
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* add feature processor APIs to public doc
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* s3 prefix model data for JumpStartModel
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* Model Package support for updating approval
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### Bug Fixes and Other Changes
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* Add bucket owner check
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* transform step unit test
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* Release TGI 1.1.0 Image
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## v2.188.0 (2023-09-26)
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### Features

‎VERSION

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2.188.1.dev0
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2.190.1.dev0

‎doc/api/inference/deserializers.rst

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Deserializers
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-------------
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.. automodule:: sagemaker.deserializers
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.. automodule:: sagemaker.base_deserializers
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:members:
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:undoc-members:
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:show-inheritance:
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.. automodule:: sagemaker.deserializers
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:members:
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:undoc-members:
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:show-inheritance:

‎doc/api/inference/serializers.rst

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Serializers
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-----------
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.. automodule:: sagemaker.serializers
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.. automodule:: sagemaker.base_serializers
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:members:
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:undoc-members:
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:show-inheritance:
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.. automodule:: sagemaker.serializers
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:members:
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:undoc-members:
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:show-inheritance:

‎doc/api/prep_data/feature_store.rst

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:members:
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:show-inheritance:
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.. autoclass:: sagemaker.feature_store.feature_definition.CollectionTypeEnum
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:members:
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:show-inheritance:
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.. autoclass:: sagemaker.feature_store.feature_definition.CollectionType
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:members:
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:show-inheritance:
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.. autoclass:: sagemaker.feature_store.feature_definition.ListCollectionType
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:members:
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:show-inheritance:
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.. autoclass:: sagemaker.feature_store.feature_definition.SetCollectionType
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:members:
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:show-inheritance:
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.. autoclass:: sagemaker.feature_store.feature_definition.VectorCollectionType
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:members:
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:show-inheritance:
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Inputs
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******
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:show-inheritance:
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.. autoclass:: sagemaker.feature_store.inputs.OnlineStoreStorageTypeEnum
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:members:
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:show-inheritance:
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.. autoclass:: sagemaker.feature_store.inputs.ResourceEnum
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:members:
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:show-inheritance:
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.. autoclass:: sagemaker.feature_store.feature_store.FeatureStore
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:members:
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:show-inheritance:
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@feature_processor Decorator
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****************************
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.. autodecorator:: sagemaker.feature_store.feature_processor.feature_processor
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Feature Processor Data Source
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*****************************
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.. autoclass:: sagemaker.feature_store.feature_processor.FeatureGroupDataSource
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:members:
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:show-inheritance:
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.. autoclass:: sagemaker.feature_store.feature_processor.CSVDataSource
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:members:
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:show-inheritance:
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.. autoclass:: sagemaker.feature_store.feature_processor.ParquetDataSource
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:members:
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:show-inheritance:
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Feature Processor Scheduler
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***************************
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.. automethod:: sagemaker.feature_store.feature_processor.to_pipeline
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.. automethod:: sagemaker.feature_store.feature_processor.schedule
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.. automethod:: sagemaker.feature_store.feature_processor.execute
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.. automethod:: sagemaker.feature_store.feature_processor.delete_schedule
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.. automethod:: sagemaker.feature_store.feature_processor.describe
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.. automethod:: sagemaker.feature_store.feature_processor.list_pipelines

‎doc/conf.py

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# autosectionlabel
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autosectionlabel_prefix_document = True
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autodoc_mock_imports = ["pyspark", "feature_store_pyspark", "py4j"]
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def setup(app):
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sys.stdout.write("Generating JumpStart model table...")

‎doc/frameworks/huggingface/sagemaker.huggingface.rst

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:undoc-members:
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:show-inheritance:
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Hugging Face Processor
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----------------------
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.. autoclass:: sagemaker.huggingface.processing.HuggingFaceProcessor
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:members:
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:undoc-members:
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:show-inheritance:

‎doc/frameworks/mxnet/sagemaker.mxnet.rst

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MXNet Estimator
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---------------------------
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---------------
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.. autoclass:: sagemaker.mxnet.estimator.MXNet
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MXNet Model
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-----------
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.. autoclass:: sagemaker.mxnet.model.MXNetModel
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MXNet Predictor
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---------------
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.. autoclass:: sagemaker.mxnet.model.MXNetPredictor
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:members:
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:show-inheritance:
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MXNet Processor
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---------------
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.. autoclass:: sagemaker.mxnet.processing.MXNetProcessor
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:members:
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:undoc-members:
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:show-inheritance:

‎doc/frameworks/pytorch/sagemaker.pytorch.rst

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PyTorch Processor
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-----------------
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.. autoclass:: sagemaker.pytorch.processing.PyTorchProcessor
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:members:
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:undoc-members:
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:show-inheritance:

‎doc/frameworks/tensorflow/sagemaker.tensorflow.rst

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TensorFlow Processor
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--------------------
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.. autoclass:: sagemaker.tensorflow.processing.TensorFlowProcessor
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:members:
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:show-inheritance:

‎doc/workflows/airflow/using_workflow.rst

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* ``SageMakerTransformOperator``
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* ``SageMakerEndpointConfigOperator``
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* ``SageMakerEndpointOperator``
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* ``SageMakerProcessingOperator``
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* ``SageMakerDeleteModelOperator``
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* ``SageMakerStartPipelineOperator``
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* ``SageMakerStopPipelineOperator``
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* ``SageMakerRegisterModelVersionOperator``
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* ``SageMakerAutoMLOperator``
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* ``SageMakerCreateExperimentOperator``
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You can look into `the Apache Airflow documentation <https://airflow.apache.org/docs/apache-airflow-providers-amazon/stable/operators/sagemaker.html>`_ for the full
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list of operators with examples.
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2. PythonOperator: Airflow built-in operator that executes Python callables. You can use the PythonOperator to execute
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operations in the SageMaker Python SDK to create a SageMaker workflow.

‎src/sagemaker/chainer/model.py

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def register(
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self,
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content_types: List[Union[str, PipelineVariable]],
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content_types: List[Union[str, PipelineVariable]] = None,
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response_types: List[Union[str, PipelineVariable]] = None,
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inference_instances: Optional[List[Union[str, PipelineVariable]]] = None,
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transform_instances: Optional[List[Union[str, PipelineVariable]]] = None,
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model_package_name: Optional[Union[str, PipelineVariable]] = None,

‎src/sagemaker/estimator.py

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def register(
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content_types,
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response_types,
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content_types=None,
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response_types=None,
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inference_instances=None,
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transform_instances=None,
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image_uri=None,

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