Merge branch 'master' into ioFollowup2

Author: mvsusp

.gitignore

@@ -25,4 +25,4 @@ venv/
 *~
 .pytest_cache/
 *.swp
-tests/data/local_mode_lock
+.docker/

CHANGELOG.md

@@ -1,5 +1,22 @@
 # Changelog
 
+## v1.28.3 (2019-06-20)
+
+### Bug fixes and other changes
+
+ * update Sagemaker Neo regions and instance families
+
+### Documentation changes
+
+ * fix punctuation in MXNet version list
+ * clean up MXNet and TF documentation
+
+## v1.28.2 (2019-06-19)
+
+### Bug fixes and other changes
+
+ * prevent race condition in vpc tests
+
 ## v1.28.1 (2019-06-17)
 
 ### Bug fixes and other changes

README.rst

@@ -84,7 +84,7 @@ As a managed service, Amazon SageMaker performs operations on your behalf on the
 Amazon SageMaker can perform only operations that the user permits.
 You can read more about which permissions are necessary in the `AWS Documentation <https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html>`__.
 
-The SageMaker Python SDK should not require any additional permissions.
+The SageMaker Python SDK should not require any additional permissions aside from what is required for using SageMaker.
 However, if you are using an IAM role with a path in it, you should grant permission for ``iam:GetRole``.
 
 Licensing
@@ -117,27 +117,28 @@ To run the unit tests with tox, run:
 To run the integration tests, the following prerequisites must be met
 
 1. AWS account credentials are available in the environment for the boto3 client to use.
-2. The AWS account has an IAM role named :code:`SageMakerRole` with the AmazonSageMakerFullAccess policy attached.
+2. The AWS account has an IAM role named :code:`SageMakerRole`.
+   It should have the AmazonSageMakerFullAccess policy attached as well as a policy with `the necessary permissions to use Elastic Inference <https://docs.aws.amazon.com/sagemaker/latest/dg/ei-setup.html>`__.
 
 We recommend selectively running just those integration tests you'd like to run. You can filter by individual test function names with:
 
 ::
 
-    pytest -k 'test_i_care_about'
+    tox -- -k 'test_i_care_about'
 
 
 You can also run all of the integration tests by running the following command, which runs them in sequence, which may take a while:
 
 ::
 
-    pytest tests/integ
+    tox -- tests/integ
 
 
 You can also run them in parallel:
 
 ::
 
-    pytest -n auto tests/integ
+    tox -- -n auto tests/integ
 
 
 Building Sphinx docs
@@ -149,16 +150,16 @@ Building Sphinx docs
 
     make html
 
-You can edit the templates for any of the pages in the docs by editing the .rst files in the "doc" directory and then running "``make html``" again.
+You can edit the templates for any of the pages in the docs by editing the .rst files in the ``doc`` directory and then running ``make html`` again.
 
 MXNet SageMaker Estimators
 --------------------------
 
-By using MXNet SageMaker ``Estimators``, you can train and host MXNet models on Amazon SageMaker.
+By using MXNet SageMaker Estimators, you can train and host MXNet models on Amazon SageMaker.
 
-Supported versions of MXNet: ``1.3.0``, ``1.2.1``, ``1.1.0``, ``1.0.0``, ``0.12.1``.
+Supported versions of MXNet: ``0.12.1``, ``1.0.0``, ``1.1.0``, ``1.2.1``, ``1.3.0``, ``1.4.0``.
 
-Supported versions of MXNet for Elastic Inference: ``1.3.0``
+Supported versions of MXNet for Elastic Inference: ``1.3.0``, ``1.4.0``.
 
 We recommend that you use the latest supported version, because that's where we focus most of our development efforts.
 
@@ -170,7 +171,7 @@ For more information, see `Using MXNet with the SageMaker Python SDK`_.
 TensorFlow SageMaker Estimators
 -------------------------------
 
-By using TensorFlow SageMaker ``Estimators``, you can train and host TensorFlow models on Amazon SageMaker.
+By using TensorFlow SageMaker Estimators, you can train and host TensorFlow models on Amazon SageMaker.
 
 Supported versions of TensorFlow: ``1.4.1``, ``1.5.0``, ``1.6.0``, ``1.7.0``, ``1.8.0``, ``1.9.0``, ``1.10.0``, ``1.11.0``, ``1.12.0``.
 
@@ -186,47 +187,47 @@ For more information, see `Using TensorFlow with the SageMaker Python SDK`_.
 Chainer SageMaker Estimators
 ----------------------------
 
-By using Chainer SageMaker ``Estimators``, you can train and host Chainer models on Amazon SageMaker.
+By using Chainer SageMaker Estimators, you can train and host Chainer models on Amazon SageMaker.
 
 Supported versions of Chainer: ``4.0.0``, ``4.1.0``, ``5.0.0``.
 
 We recommend that you use the latest supported version, because that's where we focus most of our development efforts.
 
 For more information about Chainer, see https://github.com/chainer/chainer.
 
-For more information about  Chainer SageMaker ``Estimators``, see `Using Chainer with the SageMaker Python SDK`_.
+For more information about Chainer SageMaker Estimators, see `Using Chainer with the SageMaker Python SDK`_.
 
 .. _Using Chainer with the SageMaker Python SDK: https://sagemaker.readthedocs.io/en/stable/using_chainer.html
 
 
 PyTorch SageMaker Estimators
 ----------------------------
 
-With PyTorch SageMaker ``Estimators``, you can train and host PyTorch models on Amazon SageMaker.
+With PyTorch SageMaker Estimators, you can train and host PyTorch models on Amazon SageMaker.
 
 Supported versions of PyTorch: ``0.4.0``, ``1.0.0``.
 
 We recommend that you use the latest supported version, because that's where we focus most of our development efforts.
 
 For more information about PyTorch, see https://github.com/pytorch/pytorch.
 
-For more information about PyTorch SageMaker ``Estimators``, see `Using PyTorch with the SageMaker Python SDK`_.
+For more information about PyTorch SageMaker Estimators, see `Using PyTorch with the SageMaker Python SDK`_.
 
 .. _Using PyTorch with the SageMaker Python SDK: https://sagemaker.readthedocs.io/en/stable/using_pytorch.html
 
 
 Scikit-learn SageMaker Estimators
 ---------------------------------
 
-With Scikit-learn SageMaker ``Estimators``, you can train and host Scikit-learn models on Amazon SageMaker.
+With Scikit-learn SageMaker Estimators, you can train and host Scikit-learn models on Amazon SageMaker.
 
 Supported versions of Scikit-learn: ``0.20.0``.
 
 We recommend that you use the latest supported version, because that's where we focus most of our development efforts.
 
 For more information about Scikit-learn, see https://scikit-learn.org/stable/
 
-For more information about Scikit-learn SageMaker ``Estimators``, see `Using Scikit-learn with the SageMaker Python SDK`_.
+For more information about Scikit-learn SageMaker Estimators, see `Using Scikit-learn with the SageMaker Python SDK`_.
 
 .. _Using Scikit-learn with the SageMaker Python SDK: https://sagemaker.readthedocs.io/en/stable/using_sklearn.html
 
@@ -236,13 +237,13 @@ SageMaker Reinforcement Learning Estimators
 
 With Reinforcement Learning (RL) Estimators, you can use reinforcement learning to train models on Amazon SageMaker.
 
-Supported versions of Coach: ``0.10.1`` with TensorFlow, ``0.11.0`` with TensorFlow or MXNet.
+Supported versions of Coach: ``0.10.1``, ``0.11.1`` with TensorFlow, ``0.11.0`` with TensorFlow or MXNet.
 For more information about Coach, see https://github.com/NervanaSystems/coach
 
-Supported versions of Ray: ``0.5.3`` with TensorFlow.
+Supported versions of Ray: ``0.5.3``, ``0.6.5`` with TensorFlow.
 For more information about Ray, see https://github.com/ray-project/ray
 
-For more information about SageMaker RL ``Estimators``, see `SageMaker Reinforcement Learning Estimators`_.
+For more information about SageMaker RL Estimators, see `SageMaker Reinforcement Learning Estimators`_.
 
 .. _SageMaker Reinforcement Learning Estimators: src/sagemaker/rl/README.rst
 

VERSION

@@ -1 +1 @@
-1.28.2.dev0
+1.28.4.dev0

doc/overview.rst

@@ -559,6 +559,33 @@ Likewise, when you create ``Transformer`` from the ``Estimator`` using ``transfo
     # Transform Job container instances will run in your VPC
     mxnet_vpc_transformer.transform('s3://my-bucket/batch-transform-input')
 
+Secure Training with Network Isolation (Internet-Free) Mode
+-------------------------------------------------------------------------
+You can enable network isolation mode when running training and inference on Amazon SageMaker.
+
+For more information about Amazon SageMaker network isolation mode, see the `SageMaker documentation on network isolation or internet-free mode <https://docs.aws.amazon.com/sagemaker/latest/dg/mkt-algo-model-internet-free.html>`__.
+
+To train a model in network isolation mode, set the optional parameter ``enable_network_isolation`` to ``True`` in any network isolation supported Framework Estimator.
+
+.. code:: python
+
+    # set the enable_network_isolation parameter to True
+    sklearn_estimator = SKLearn('sklearn-train.py',
+                                train_instance_type='ml.m4.xlarge',
+                                framework_version='0.20.0',
+                                hyperparameters = {'epochs': 20, 'batch-size': 64, 'learning-rate': 0.1},
+                                enable_network_isolation=True)
+
+    # SageMaker Training Job will in the container without   any inbound or outbound network calls during runtime
+    sklearn_estimator.fit({'train': 's3://my-data-bucket/path/to/my/training/data',
+                            'test': 's3://my-data-bucket/path/to/my/test/data'})
+
+When this training job is created, the SageMaker Python SDK will upload the files in ``entry_point``, ``source_dir``, and ``dependencies`` to S3 as a compressed ``sourcedir.tar.gz`` file (``'s3://mybucket/sourcedir.tar.gz'``).
+
+A new training job channel, named ``code``, will be added with that S3 URI.  Before the training docker container is initialized, the ``sourcedir.tar.gz`` will be downloaded from S3 to the ML storage volume like any other offline input channel.
+
+Once the training job begins, the training container will look at the offline input ``code`` channel to install dependencies and run the entry script. This isolates the training container, so no inbound or outbound network calls can be made.
+
 
 FAQ
 ---

doc/using_mxnet.rst

@@ -35,13 +35,10 @@ In the following sections, we'll discuss how to prepare a training script for ex
 Preparing the MXNet training script
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-+----------------------------------------------------------------------------------------------------------------------------------------------------------+
-| WARNING                                                                                                                                                  |
-+==========================================================================================================================================================+
-| The structure for training scripts changed starting at MXNet version 1.3.                                                                                |
-| Make sure you refer to the correct section of this README when you prepare your script.                                                                  |
-| For information on how to upgrade an old script to the new format, see `"Updating your MXNet training script" <#updating-your-mxnet-training-script>`__. |
-+----------------------------------------------------------------------------------------------------------------------------------------------------------+
+.. warning::
+    The structure for training scripts changed starting at MXNet version 1.3.
+    Make sure you refer to the correct section of this README when you prepare your script.
+    For information on how to upgrade an old script to the new format, see `"Updating your MXNet training script" <#updating-your-mxnet-training-script>`__.
 
 For versions 1.3 and higher
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -142,7 +139,7 @@ You don't have to use all the arguments, arguments you don't care about can be i
     def train(hyperparameters, num_gpus, **kwargs):
         pass
 
-**Note: Writing a training script that imports correctly**
+**Note: Writing a training script that imports correctly:**
 When SageMaker runs your training script, it imports it as a Python module and then invokes ``train`` on the imported module. Consequently, you should not include any statements that won't execute successfully in SageMaker when your module is imported. For example, don't attempt to open any local files in top-level statements in your training script.
 
 If you want to run your training script locally via the Python interpreter, look at using a ``___name__ == '__main__'`` guard, discussed in more detail here: https://stackoverflow.com/questions/419163/what-does-if-name-main-do .
@@ -701,12 +698,12 @@ This one function should handle processing the input, performing a prediction, a
 The return object should be one of the following:
 
 For versions 1.4 and higher:
-----------------------------
+
 - a tuple with two items: the response data and ``accept_type`` (the content type of the response data), or
-- the response data: (the content type of the response will be set to either the accept header in the initial request or default to application/json)
+- the response data: (the content type of the response will be set to either the accept header in the initial request or default to "application/json")
 
 For versions 1.3 and lower:
----------------------------
+
 - a tuple with two items: the response data and ``accept_type`` (the content type of the response data), or
 - a Flask response object: http://flask.pocoo.org/docs/1.0/api/#response-objects
 
@@ -828,5 +825,7 @@ The Docker images extend Ubuntu 16.04.
 You can select version of MXNet by passing a ``framework_version`` keyword arg to the MXNet Estimator constructor. Currently supported versions are listed in the above table. You can also set ``framework_version`` to only specify major and minor version, e.g ``1.2``, which will cause your training script to be run on the latest supported patch version of that minor version, which in this example would be 1.2.1.
 Alternatively, you can build your own image by following the instructions in the SageMaker MXNet containers repository, and passing ``image_name`` to the MXNet Estimator constructor.
 
-You can visit the SageMaker MXNet training containers repository here: https://github.com/aws/sagemaker-mxnet-container
-You can visit the SageMaker MXNet serving containers repository here: https://github.com/aws/sagemaker-mxnet-serving-container
+You can visit the SageMaker MXNet container repositories here:
+
+- training: https://github.com/aws/sagemaker-mxnet-container
+- serving: https://github.com/aws/sagemaker-mxnet-serving-container

doc/using_tf.rst

@@ -8,6 +8,7 @@ models on SageMaker Hosting.
 
 **Note:** This topic describes how to use script mode for TensorFlow versions 1.11 and later.
 For Documentation of the previous Legacy Mode versions, see:
+
 * `1.4.1 <https://github.com/aws/sagemaker-python-sdk/tree/v1.0.0#tensorflow-sagemaker-estimators>`_
 * `1.5.0 <https://github.com/aws/sagemaker-python-sdk/tree/v1.1.0#tensorflow-sagemaker-estimators>`_
 * `1.6.0 <https://github.com/aws/sagemaker-python-sdk/blob/v1.5.0/src/sagemaker/tensorflow/README.rst#tensorflow-sagemaker-estimators-and-models>`_
@@ -16,18 +17,15 @@ For Documentation of the previous Legacy Mode versions, see:
 * `1.9.0 <https://github.com/aws/sagemaker-python-sdk/blob/v1.9.2/src/sagemaker/tensorflow/README.rst#tensorflow-sagemaker-estimators-and-models>`_
 * `1.10.0 <https://github.com/aws/sagemaker-python-sdk/blob/v1.10.0/src/sagemaker/tensorflow/README.rst#tensorflow-sagemaker-estimators-and-models>`_
 
-+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| WARNING                                                                                                                                                                     |
-+=============================================================================================================================================================================+
-| We have added a new format of your TensorFlow training script with TensorFlow version 1.11.                                                                                 |
-| This new way gives the user script more flexibility.                                                                                                                        |
-| This new format is called Script Mode, as opposed to Legacy Mode, which is what we support with TensorFlow 1.11 and older versions.                                         |
-| In addition we are adding Python 3 support with Script Mode.                                                                                                                |
-| Last supported version of Legacy Mode will be TensorFlow 1.12.                                                                                                              |
-| Script Mode is available with TensorFlow version 1.11 and newer.                                                                                                            |
-| Make sure you refer to the correct version of this README when you prepare your script.                                                                                     |
-| You can find the Legacy Mode README `here <https://github.com/aws/sagemaker-python-sdk/tree/v1.12.0/src/sagemaker/tensorflow#tensorflow-sagemaker-estimators-and-models>`_. |
-+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+.. warning::
+    We have added a new format of your TensorFlow training script with TensorFlow version 1.11.
+    This new way gives the user script more flexibility.
+    This new format is called Script Mode, as opposed to Legacy Mode, which is what we support with TensorFlow 1.11 and older versions.
+    In addition we are adding Python 3 support with Script Mode.
+    Last supported version of Legacy Mode will be TensorFlow 1.12.
+    Script Mode is available with TensorFlow version 1.11 and newer.
+    Make sure you refer to the correct version of this README when you prepare your script.
+    You can find the Legacy Mode README `here <https://github.com/aws/sagemaker-python-sdk/tree/v1.12.0/src/sagemaker/tensorflow#tensorflow-sagemaker-estimators-and-models>`_.
 
 .. contents::
 
@@ -507,4 +505,7 @@ You can select version of TensorFlow by passing a ``framework_version`` keyword
 Alternatively, you can build your own image by following the instructions in the SageMaker TensorFlow containers
 repository, and passing ``image_name`` to the TensorFlow Estimator constructor.
 
-For more information on the contents of the images, see the SageMaker TensorFlow containers repository here: https://github.com/aws/sagemaker-tensorflow-containers/
+For more information on the contents of the images, see the SageMaker TensorFlow containers repositories here:
+
+- training: https://github.com/aws/sagemaker-tensorflow-container
+- serving: https://github.com/aws/sagemaker-tensorflow-serving-container