doc: update documentation with v2.0.0.rc1 changes and bump version (#…

…1686)

CHANGELOG.md

@@ -1,5 +1,62 @@
 # Changelog
 
+## v2.0.0.rc1 (2020-07-08)
+
+### Breaking Changes
+
+ * Move StreamDeserializer to sagemaker.deserializers
+ * Move StringDeserializer to sagemaker.deserializers
+ * rename record_deserializer to RecordDeserializer
+ * remove "train_" where redundant in parameter/variable names
+ * Add BytesDeserializer
+ * rename image to image_uri
+ * rename image_name to image_uri
+ * create new inference resources during model.deploy() and model.transformer()
+ * rename session parameter to sagemaker_session in S3 utility classes
+ * rename distributions to distribution in TF/MXNet estimators
+ * deprecate update_endpoint arg in deploy()
+ * create new inference resources during estimator.deploy() or estimator.transformer()
+ * deprecate delete_endpoint() for estimators and HyperparameterTuner
+ * refactor Predictor attribute endpoint to endpoint_name
+ * make instance_type optional for Airflow model configs
+ * refactor name of RealTimePredictor to Predictor
+ * remove check for Python 2 string in sagemaker.predictor._is_sequence_like()
+ * deprecate sagemaker.utils.to_str()
+ * drop Python 2 support
+
+### Features
+
+ * add BaseSerializer and BaseDeserializer
+ * add Predictor.update_endpoint()
+
+### Bug Fixes and Other Changes
+
+ * handle "train_*" renames in v2 migration tool
+ * handle image_uri rename for Session methods in v2 migration tool
+ * Update BytesDeserializer accept header
+ * handle image_uri rename for estimators and models in v2 migration tool
+ * handle image_uri rename in Airflow model config functions in v2 migration tool
+ * update migration tool for S3 utility functions
+ * set _current_job_name and base_tuning_job_name in HyperparameterTuner.attach()
+ * infer base name from job name in estimator.attach()
+ * ensure generated names are < 63 characters when deploying compiled models
+ * add TF migration documentation to error message
+
+### Documentation Changes
+
+ * update documentation with v2.0.0.rc1 changes
+ * remove 'train_*' prefix from estimator parameters
+ * update documentation for image_name/image --> image_uri
+
+### Testing and Release Infrastructure
+
+ * refactor matching logic in v2 migration tool
+ * add cli modifier for RealTimePredictor and derived classes
+ * change coverage settings to reduce intermittent errors
+ * clean up pickle.load logic in integ tests
+ * use fixture for Python version in framework integ tests
+ * remove assumption of Python 2 unit test runs
+
 ## v1.68.0 (2020-07-07)
 
 ### Features

VERSION

@@ -1 +1 @@
-2.0.0.rc0
+2.0.0.rc1

doc/v2.rst

@@ -21,7 +21,7 @@ To install the latest release candidate:
 
 .. code:: bash
 
-    pip install git+git@github.com:aws/sagemaker-python-sdk.git@v2.0.0.rc0
+    pip install git+git@github.com:aws/sagemaker-python-sdk.git@v2.0.0.rc1
 
 To install the latest version of v2:
 
@@ -38,6 +38,38 @@ Changes
 This section is for major changes that may require updates to your SageMaker Python SDK code.
 You can also see what changes have been made in the `CHANGELOG <https://github.com/aws/sagemaker-python-sdk/blob/zwei/CHANGELOG.md>`_.
 
+Deprecations
+============
+
+Python 2 Support
+----------------
+
+This library is no longer supported for Python 2.
+Please upgrade to Python 3 if you haven't already.
+
+Deprecate Legacy TensorFlow
+---------------------------
+
+TensorFlow versions 1.4-1.10 and some variations of versions 1.11-1.12
+(see `What Constitutes "Legacy TensorFlow Support" <frameworks/tensorflow/upgrade_from_legacy.html#what-constitutes-legacy-tensorflow-support>`_)
+are no longer natively supported by the SageMaker Python SDK.
+
+To use those versions of TensorFlow, you must specify the Docker image URI explicitly,
+and configure settings via hyperparameters or environment variables rather than using SDK parameters.
+For more information, see `Upgrade from Legacy TensorFlow Support <frameworks/tensorflow/upgrade_from_legacy.html>`_.
+
+``delete_endpoint()`` for Estimators and ``HyperparameterTuner``
+----------------------------------------------------------------
+
+The ``delete_endpoint()`` method for estimators and ``HyperparameterTuner`` has been deprecated.
+Please use :func:`sagemaker.predictor.Predictor.delete_endpoint` instead.
+
+``update_endpoint`` in ``deploy()``
+-----------------------------------
+
+The ``update_endpoint`` argument in ``deploy()`` methods for estimators and models has been deprecated.
+Please use :func:`sagemaker.predictor.Predictor.update_endpoint` instead.
+
 Require ``framework_version`` and ``py_version`` for Frameworks
 ===============================================================
 
@@ -69,19 +101,51 @@ For example:
         py_version="py3",  # now required
     )
 
-Deprecate Legacy TensorFlow
-===========================
+Parameter and Class Name Changes
+================================
 
-TensorFlow versions 1.4-1.10 and some variations of versions 1.11-1.12
-(see `What Constitutes "Legacy TensorFlow Support" <frameworks/tensorflow/upgrade_from_legacy.html#what-constitutes-legacy-tensorflow-support>`_)
-are no longer natively supported by the SageMaker Python SDK.
+Estimators
+----------
 
-To use those versions of TensorFlow, you must specify the Docker image URI explicitly,
-and configure settings via hyperparameters or environment variables rather than using SDK parameters.
-For more information, see `Upgrade from Legacy TensorFlow Support <frameworks/tensorflow/upgrade_from_legacy.html>`_.
+Renamed Estimator Parameters
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The following estimator parameters have been renamed:
+
++------------------------------+------------------------+
+| v1.x                         | v2.0 and later         |
++==============================+========================+
+| ``train_instance_count``     | ``instance_count``     |
++------------------------------+------------------------+
+| ``train_instance_type``      | ``instance_type``      |
++------------------------------+------------------------+
+| ``train_max_run``            | ``max_run``            |
++------------------------------+------------------------+
+| ``train_use_spot_instances`` | ``use_spot_instances`` |
++------------------------------+------------------------+
+| ``train_max_run_wait``       | ``max_run_wait``       |
++------------------------------+------------------------+
+| ``train_volume_size``        | ``volume_size``        |
++------------------------------+------------------------+
+| ``train_volume_kms_key``     | ``volume_kms_key``     |
++------------------------------+------------------------+
+
+``distributions``
+~~~~~~~~~~~~~~~~~
+
+For TensorFlow and MXNet estimators, ``distributions`` has been renamed to ``distribution``.
 
-Parameter Changes for ``sagemaker.model.Model``
-===============================================
+Specify Custom Training Images
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The ``image_name`` parameter has been renamed to ``image_uri`` for specifying a custom Docker image URI to use with training.
+
+
+Models
+------
+
+``sagemaker.model.Model`` Parameter Order
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 The parameter order for :class:`sagemaker.model.Model` changed: instead of ``model_data`` being first, ``image_uri`` (formerly ``image``) is first.
 As a result, ``model_data`` has been made into an optional parameter.
@@ -96,6 +160,29 @@ If you are using the :class:`sagemaker.model.Model` class, your code should be c
     # v2.0 and later
     Model("my-image:latest", model_data="s3://bucket/path/model.tar.gz")
 
+Specify Custom Serving Image
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The ``image`` parameter has been renamed to ``image_uri`` for specifying a custom Docker image URI to use with inference.
+
+Predictors
+----------
+
+``sagemaker.predictor.RealTimePredictor`` has been renamed to :class:`sagemaker.predictor.Predictor`.
+
+In addition, for :class:`sagemaker.predictor.Predictor`, :class:`sagemaker.sparkml.model.SparkMLPredictor`,
+and predictors for Amazon algorithm (e.g. Factorization Machines, Linear Learner, etc.),
+the ``endpoint`` attribute has been renamed to ``endpoint_name``.
+
+Airflow
+-------
+
+For :func:`sagemaker.workflow.airflow.model_config` and :func:`sagemaker.workflow.airflow.model_config_from_estimator`,
+``instance_type`` is no longer the first positional argument and is now an optional keyword argument.
+
+For :func:`sagemaker.workflow.airflow.model_config`, :func:`sagemaker.workflow.airflow.model_config_from_estimator`, and
+:func:`sagemaker.workflow.airflow.transform_config_from_estimator`, the ``image`` argument has been renamed to ``image_uri``.
+
 Dependency Changes
 ==================
 
@@ -170,3 +257,12 @@ If you are using the ``sagemaker.tensorflow.serving.Model`` class, the tool does
 -------------------------
 
 If you are using the :class:`sagemaker.model.Model` class, the tool does not take care of switching the order between ``model_data`` and ``image_uri`` (formerly ``image``).
+
+``update_endpoint`` and ``delete_endpoint``
+-------------------------------------------
+
+The tool does not take care of removing the ``update_endpoint`` argument from a ``deploy`` call.
+If you are using that argument, please modify your code to use :func:`sagemaker.predictor.Predictor.update_endpoint` instead.
+
+The tool also does not handle ``delete_endpoint`` calls on estimators or ``HyperparameterTuner``.
+If you are using that method, please modify your code to use  :func:`sagemaker.predictor.Predictor.delete_endpoint` instead.