Merge branch 'master' into more-docstyle-improvements

Author: ajaykarpur

CHANGELOG.md

@@ -1,5 +1,74 @@
 # Changelog
 
+## v2.14.0 (2020-10-05)
+
+### Features
+
+ * upgrade Neo MxNet to 1.7
+
+### Bug Fixes and Other Changes
+
+ * add a condition to retrieve correct image URI for xgboost
+
+## v2.13.0 (2020-09-30)
+
+### Features
+
+ * add xgboost framework version 1.2-1
+
+### Bug Fixes and Other Changes
+
+ * revert "feature: upgrade Neo MxNet to 1.7 (#1928)"
+
+## v2.12.0 (2020-09-29)
+
+### Features
+
+ * upgrade Neo MxNet to 1.7
+
+## v2.11.0 (2020-09-28)
+
+### Features
+
+ * Add SDK support for SparkML Serving Container version 2.4
+
+### Bug Fixes and Other Changes
+
+ * pin pytest version <6.1.0 to avoid pytest-rerunfailures breaking changes
+ * temporarily skip the MxNet Neo test until we fix them
+
+### Documentation Changes
+
+ * fix conda setup for docs
+
+## v2.10.0 (2020-09-23)
+
+### Features
+
+ * add inferentia pytorch inference container config
+
+## v2.9.2 (2020-09-21)
+
+### Bug Fixes and Other Changes
+
+ * allow kms encryption upload for processing
+
+## v2.9.1 (2020-09-17)
+
+### Bug Fixes and Other Changes
+
+ * update spark image_uri config with eu-north-1 account
+
+## v2.9.0 (2020-09-17)
+
+### Features
+
+ * add MXNet 1.7.0 images
+
+### Documentation Changes
+
+ * removed Kubernetes workflow content
+
 ## v2.8.0 (2020-09-16)
 
 ### Features

README.rst

@@ -53,7 +53,6 @@ Table of Contents
 #. `Secure Training and Inference with VPC <https://sagemaker.readthedocs.io/en/stable/overview.html#secure-training-and-inference-with-vpc>`__
 #. `BYO Model <https://sagemaker.readthedocs.io/en/stable/overview.html#byo-model>`__
 #. `Inference Pipelines <https://sagemaker.readthedocs.io/en/stable/overview.html#inference-pipelines>`__
-#. `Amazon SageMaker Operators for Kubernetes <https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_operators_for_kubernetes.html>`__
 #. `Amazon SageMaker Operators in Apache Airflow <https://sagemaker.readthedocs.io/en/stable/using_workflow.html>`__
 #. `SageMaker Autopilot <src/sagemaker/automl/README.rst>`__
 #. `Model Monitoring <https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_model_monitoring.html>`__
@@ -165,7 +164,7 @@ Setup a Python environment, and install the dependencies listed in ``doc/require
     # conda
     conda create -n sagemaker python=3.7
     conda activate sagemaker
-    conda install --file doc/requirements.txt
+    conda install sphinx=3.1.1 sphinx_rtd_theme=0.5.0
 
     # pip
     pip install -r doc/requirements.txt
@@ -202,7 +201,7 @@ In order to host a SparkML model in SageMaker, it should be serialized with ``ML
 
 For more information on MLeap, see https://github.com/combust/mleap .
 
-Supported major version of Spark: 2.2 (MLeap version - 0.9.6)
+Supported major version of Spark: 2.4 (MLeap version - 0.9.6)
 
 Here is an example on how to create an instance of  ``SparkMLModel`` class and use ``deploy()`` method to create an
 endpoint which can be used to perform prediction against your trained SparkML Model.

VERSION

@@ -1 +1 @@
-2.8.1.dev0
+2.14.1.dev0

doc/amazon_sagemaker_processing.rst

@@ -69,15 +69,15 @@ For an in-depth look, please see the `Scikit-learn Data Processing and Model Eva
 
 Data Processing with Spark
 ============================================
-SageMaker provides two classes for customers to run Spark applications: :class:`sagemaker.processing.PySparkProcessor` and :class:`sagemaker.processing.SparkJarProcessor`
+SageMaker provides two classes for customers to run Spark applications: :class:`sagemaker.spark.processing.PySparkProcessor` and :class:`sagemaker.spark.processing.SparkJarProcessor`
 
 
 PySparkProcessor
 ---------------------
 
-You can use the :class:`sagemaker.processing.PySparkProcessor` class to run PySpark scripts as processing jobs.
+You can use the :class:`sagemaker.spark.processing.PySparkProcessor` class to run PySpark scripts as processing jobs.
 
-This example shows how you can take an existing PySpark script and run a processing job with the :class:`sagemaker.processing.PySparkProcessor` class and the pre-built SageMaker Spark container.
+This example shows how you can take an existing PySpark script and run a processing job with the :class:`sagemaker.spark.processing.PySparkProcessor` class and the pre-built SageMaker Spark container.
 
 First you need to create a :class:`PySparkProcessor` object
 
@@ -230,8 +230,8 @@ Processing class documentation
 - :class:`sagemaker.processing.Processor`
 - :class:`sagemaker.processing.ScriptProcessor`
 - :class:`sagemaker.sklearn.processing.SKLearnProcessor`
-- :class:`sagemaker.sklearn.processing.PySparkProcessor`
-- :class:`sagemaker.sklearn.processing.SparkJarProcessor`
+- :class:`sagemaker.spark.processing.PySparkProcessor`
+- :class:`sagemaker.spark.processing.SparkJarProcessor`
 - :class:`sagemaker.processing.ProcessingInput`
 - :class:`sagemaker.processing.ProcessingOutput`
 - :class:`sagemaker.processing.ProcessingJob`

doc/api/training/processing.rst

@@ -5,3 +5,8 @@ Processing
     :members:
     :undoc-members:
     :show-inheritance:
+
+.. automodule:: sagemaker.spark.processing
+    :members:
+    :undoc-members:
+    :show-inheritance:

doc/overview.rst

@@ -672,7 +672,7 @@ You can install all necessary for this feature dependencies using pip:
 For more detailed examples of running hyperparameter tuning jobs, see:
 
 - `Using the TensorFlow estimator with hyperparameter tuning <https://github.com/awslabs/amazon-sagemaker-examples/blob/master/hyperparameter_tuning/tensorflow_mnist/hpo_tensorflow_mnist.ipynb>`__
-- `Bringing your own estimator for hyperparameter tuning <https://github.com/awslabs/amazon-sagemaker-examples/blob/master/hyperparameter_tuning/r_bring_your_own/hpo_r_bring_your_own.ipynb>`__
+- `Bringing your own estimator for hyperparameter tuning <https://github.com/awslabs/amazon-sagemaker-examples/blob/master/hyperparameter_tuning/r_bring_your_own/tune_r_bring_your_own.ipynb>`__
 - `Analyzing results <https://github.com/awslabs/amazon-sagemaker-examples/blob/master/hyperparameter_tuning/analyze_results/HPO_Analyze_TuningJob_Results.ipynb>`__
 
 You can also find these notebooks in the **Hyperprameter Tuning** section of the **SageMaker Examples** section in a notebook instance.

doc/v2.rst

@@ -231,7 +231,7 @@ The following estimator parameters have been renamed:
 +------------------------------+------------------------+
 | ``train_use_spot_instances`` | ``use_spot_instances`` |
 +------------------------------+------------------------+
-| ``train_max_run_wait``       | ``max_wait``           |
+| ``train_max_wait``           | ``max_wait``           |
 +------------------------------+------------------------+
 | ``train_volume_size``        | ``volume_size``        |
 +------------------------------+------------------------+

doc/workflows/index.rst

@@ -8,5 +8,4 @@ The SageMaker Python SDK supports managed training and inference for a variety o
     :maxdepth: 2
 
     airflow/index
-    kubernetes/index
     step_functions/index