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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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12 changes: 10 additions & 2 deletions README.rst
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Expand Up @@ -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
Expand All @@ -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`_.

Expand All @@ -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`_.
Expand All @@ -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.

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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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