commit | 32b7183acd12766103fcacdd55d826a823c85d58 | [log] [tgz] |
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author | Deven Desai <deven.desai.amd@gmail.com> | Tue Dec 31 17:01:07 2019 +0000 |
committer | Deven Desai <deven.desai.amd@gmail.com> | Mon Mar 16 15:54:51 2020 +0000 |
tree | ff5a38a64be729278f5ccdc2d971543c3c43e956 | |
parent | 6072451f8b17d05336407aeeead6c5455a7dfc6a [diff] |
[ROCm] Unit-test updates for the ROCm platform. This commit mostly either enables or disables unittests on the ROCM platform, details listed below * adding/removing no_rocm tag for tests in the //tensorflow/compiler/mlir dir * enabling / disabling subtests within //tensorflow/core/grappler/optimizers:constant_folding_test for the ROCm platform * disabling a subtest within //tensorflow/core/distributed_runtime:collective_param_resolver_distributed_test fir the ROCm platform * adding no_rocm tag to tests that are failing on the ROCm platform * minor bug fix to ensure that the //tensorflow/compiler/mlir/tensorflow:error_util_test passes on a consistent basis
Documentation |
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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() b'Hello, TensorFlow!'
For more examples, see the TensorFlow tutorials.
If you want to contribute to TensorFlow, be sure to review the contribution guidelines. This project adheres to TensorFlow's code of conduct. By participating, you are expected to uphold this code.
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Build Type | Status | Artifacts |
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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 |
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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.