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Aug 23, 2018
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Support tensorflow 1.9 and bump version to 1.9.2 #365

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6ae2d0a
Support tensorflow 1.9 and bump version to 1.9.2
yangaws Aug 23, 2018
fc1c7ba
Update README
yangaws Aug 23, 2018
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5 changes: 5 additions & 0 deletions CHANGELOG.rst
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Expand Up @@ -2,6 +2,11 @@
CHANGELOG
=========

1.9.2
=====

* feature: add support for TensorFlow 1.9

1.9.1
=====

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8 changes: 4 additions & 4 deletions README.rst
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Expand Up @@ -242,7 +242,7 @@ MXNet SageMaker Estimators

By using MXNet SageMaker ``Estimators``, you can train and host MXNet models on Amazon SageMaker.

Supported versions of MXNet: ``1.2.1``, ``1.1.0``, ``1.0.0``, ``0.12.1``.
Supported versions of MXNet: ``1.2.1``, ``1.1.0``, ``1.0.0``, ``0.12.1``. We recommend to use the latest version and our integration test will only test the latest version.
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I would add the sentence in a new line after the list of versions and change it to something like "We recommend you use the latest version as that's where we spend most of our development efforts". Maybe @djarpin or @eslesar-aws has a better wording suggestion.

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"We recommend that you use the latest supported version, because that's where we focus most of our development efforts."

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Thanks!


For more information, see `MXNet SageMaker Estimators and Models`_.

Expand All @@ -254,7 +254,7 @@ 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 to use the latest version and our integration test will only test the latest version.

For more information, see `TensorFlow SageMaker Estimators and Models`_.

Expand All @@ -266,7 +266,7 @@ Chainer SageMaker Estimators

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``.
Supported versions of Chainer: ``4.0.0``, ``4.1.0``. We recommend to use the latest version and our integration test will only test the latest version.

For more information about Chainer, see https://github.com/chainer/chainer.

Expand All @@ -280,7 +280,7 @@ 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 to use the latest version and our integration test will only test the latest version.

For more information about PyTorch, see https://github.com/pytorch/pytorch.

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2 changes: 1 addition & 1 deletion setup.py
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Expand Up @@ -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'},
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4 changes: 2 additions & 2 deletions src/sagemaker/tensorflow/README.rst
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Expand Up @@ -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
~~~~~~~~~~~~~~~~~~~~~~~~
Expand Down Expand Up @@ -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

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2 changes: 1 addition & 1 deletion src/sagemaker/tensorflow/defaults.py
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Expand Up @@ -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'
2 changes: 1 addition & 1 deletion tests/conftest.py
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Expand Up @@ -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

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