Support tensorflow 1.9 and bump version to 1.9.2 (#365)

Author: yangaws

CHANGELOG.rst

@@ -2,6 +2,11 @@
 CHANGELOG
 =========
 
+1.9.2
+=====
+
+* feature: add support for TensorFlow 1.9
+
 1.9.1
 =====
 

README.rst

@@ -244,6 +244,8 @@ By using MXNet SageMaker ``Estimators``, you can train and host MXNet models on
 
 Supported versions of MXNet: ``1.2.1``, ``1.1.0``, ``1.0.0``, ``0.12.1``.
 
+We recommend that you use the latest supported version, because that's where we focus most of our development efforts.
+
 For more information, see `MXNet SageMaker Estimators and Models`_.
 
 .. _MXNet SageMaker Estimators and Models: src/sagemaker/mxnet/README.rst
@@ -254,7 +256,9 @@ TensorFlow SageMaker Estimators
 
 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``.
+Supported versions of TensorFlow: ``1.4.1``, ``1.5.0``, ``1.6.0``, ``1.7.0``, ``1.8.0``, ``1.9.0``.
+
+We recommend that you use the latest supported version, because that's where we focus most of our development efforts.
 
 For more information, see `TensorFlow SageMaker Estimators and Models`_.
 
@@ -268,6 +272,8 @@ By using Chainer SageMaker ``Estimators``, you can train and host Chainer models
 
 Supported versions of Chainer: ``4.0.0``, ``4.1.0``.
 
+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 `Chainer SageMaker Estimators and Models`_.
@@ -280,7 +286,9 @@ PyTorch SageMaker Estimators
 
 With PyTorch SageMaker ``Estimators``, you can train and host PyTorch models on Amazon SageMaker.
 
-Supported versions of PyTorch: ``0.4.0``
+Supported versions of PyTorch: ``0.4.0``.
+
+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.
 

setup.py

@@ -23,7 +23,7 @@ def read(fname):
 
 
 setup(name="sagemaker",
-      version="1.9.1",
+      version="1.9.2",
       description="Open source library for training and deploying models on Amazon SageMaker.",
       packages=find_packages('src'),
       package_dir={'': 'src'},

src/sagemaker/tensorflow/README.rst

@@ -6,7 +6,7 @@ TensorFlow SageMaker Estimators allow you to run your own TensorFlow
 training algorithms on SageMaker Learner, and to host your own TensorFlow
 models on SageMaker Hosting.
 
-Supported versions of TensorFlow: ``1.4.1``, ``1.5.0``, ``1.6.0``, ``1.7.0``, ``1.8.0``.
+Supported versions of TensorFlow: ``1.4.1``, ``1.5.0``, ``1.6.0``, ``1.7.0``, ``1.8.0``, ``1.9.0``.
 
 Training with TensorFlow
 ~~~~~~~~~~~~~~~~~~~~~~~~
@@ -833,7 +833,7 @@ SageMaker TensorFlow CPU images use TensorFlow built with Intel® MKL-DNN optimi
 In certain cases you might be able to get a better performance by disabling this optimization
 (`for example when using small models <https://github.com/awslabs/amazon-sagemaker-examples/blob/d88d1c19861fb7733941969f5a68821d9da2982e/sagemaker-python-sdk/tensorflow_iris_dnn_classifier_using_estimators/iris_dnn_classifier.py#L7-L9>`_)
 
-You can disable MKL-DNN optimization for TensorFlow ``1.8.0`` by setting two following environment variables:
+You can disable MKL-DNN optimization for TensorFlow ``1.8.0`` and above by setting two following environment variables:
 
 .. code:: python
 

src/sagemaker/tensorflow/defaults.py

@@ -12,4 +12,4 @@
 # language governing permissions and limitations under the License.
 from __future__ import absolute_import
 
-TF_VERSION = '1.8'
+TF_VERSION = '1.9'

tests/conftest.py

@@ -76,7 +76,7 @@ def sagemaker_local_session(boto_config):
 
 
 @pytest.fixture(scope='module', params=['1.4', '1.4.1', '1.5', '1.5.0', '1.6', '1.6.0',
-                                        '1.7', '1.7.0', '1.8', '1.8.0'])
+                                        '1.7', '1.7.0', '1.8', '1.8.0', '1.9', '1.9.0'])
 def tf_version(request):
     return request.param