| commit | f9e899854cc96db28564fa65f22d32a647268fc1 | [log] [tgz] |
|---|---|---|
| author | Reed Wanderman-Milne <reedwm@google.com> | Mon Jan 27 16:23:55 2020 -0800 |
| committer | TensorFlower Gardener <gardener@tensorflow.org> | Mon Jan 27 16:27:40 2020 -0800 |
| tree | a4dd3e42de617a91d9ebcc98a7f1a50c4dff8214 | |
| parent | e8376142f50982e2bc22fae2d62f8fcfc6e88df7 [diff] |
Fix crash when float64 or mixed precision used in certain layers. Also add many more tests to layer_corectness_test.py, so that most Keras layers are tested. Also do not test distribution strategy without mixed precision in layer_corectness_test.py. This previuosly was only tested for ease of debugging if a test failed. But the distribution strategy tests take a very long time, so it's not worth testing with distribution strategy without mixed precision. I made the test a py_test instead of a cuda_py_test to save a bit of GPU resources. Fixes #35817 and fixes #35883. PiperOrigin-RevId: 291825015 Change-Id: I8ece6de5b9d549f0de06b643774686f56775781e
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TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications.
TensorFlow was originally developed by researchers and engineers working on the Google Brain team within Google's Machine Intelligence Research organization to conduct machine learning and deep neural networks research. The system is general enough to be applicable in a wide variety of other domains, as well.
TensorFlow provides stable Python and C++ APIs, as well as non-guaranteed backward compatible API for other languages.
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See the TensorFlow install guide for the pip package, to enable GPU support, use a Docker container, and build from source.
To install the current release, which includes support for CUDA-enabled GPU cards (Ubuntu and Windows):
$ pip install tensorflow
A smaller CPU-only package is also available:
$ pip install tensorflow-cpu
To update TensorFlow to the latest version, add --upgrade flag to the above commands.
Nightly binaries are available for testing using the tf-nightly and tf-nightly-cpu packages on PyPi.
$ python
>>> import tensorflow as tf >>> tf.add(1, 2).numpy() 3 >>> hello = tf.constant('Hello, TensorFlow!') >>> hello.numpy() 'Hello, TensorFlow!'
For more examples, see the TensorFlow tutorials.
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| Build Type | Status | Artifacts |
|---|---|---|
| Linux CPU |  | PyPI |
| Linux GPU |  | PyPI |
| Linux XLA |  | TBA |
| macOS |  | PyPI |
| Windows CPU |  | PyPI |
| Windows GPU |  | PyPI |
| Android |  |  |
| Raspberry Pi 0 and 1 |   | Py2 Py3 |
| Raspberry Pi 2 and 3 |   | Py2 Py3 |
| Build Type | Status | Artifacts |
|---|---|---|
| Linux AMD ROCm GPU Nightly | Nightly | |
| Linux AMD ROCm GPU Stable Release | Release 1.15 / 2.x | |
| Linux s390x Nightly | Nightly | |
| Linux s390x CPU Stable Release | Release | |
| Linux ppc64le CPU Nightly | Nightly | |
| Linux ppc64le CPU Stable Release | Release 1.15 / 2.x | |
| Linux ppc64le GPU Nightly | Nightly | |
| Linux ppc64le GPU Stable Release | Release 1.15 / 2.x | |
| Linux CPU with Intel® MKL-DNN Nightly | Nightly | |
| Linux CPU with Intel® MKL-DNN Stable Release | Release 1.15 / 2.x | |
| Red Hat® Enterprise Linux® 7.6 CPU & GPU Python 2.7, 3.6 | 1.13.1 PyPI |
Learn more about the TensorFlow community and how to contribute.