| commit | b7ab9b9e5fcff44588763a2c97103b1ae6468e06 | [log] [tgz] |
|---|---|---|
| author | Wen-Heng (Jack) Chung <whchung@gmail.com> | Fri Aug 09 17:36:19 2019 +0000 |
| committer | Wen-Heng (Jack) Chung <whchung@gmail.com> | Mon Sep 09 15:06:31 2019 -0500 |
| tree | f92a5d086822feec5451dee2e96969b10a71ff96 | |
| parent | de64ee8b7ef128f4da3ef61d764a55935c84f1d6 [diff] |
Enable XLA JIT compiler on ROCm. - bazel changes to enable xla_gpu_device and xla_gpu_jit on ROCm. - Disable cusolver_context on ROCm. It has source code dependency to CUDA API. - Disable dependency to cholesky_thunk on ROCm. It has source code dependency to CUDA API. - Remove cudnn_conv_algorithm_picker from gpu_compiler_impl dependency list. It is conditionally dependent when CUDA is enabled. - Remove CUDA-specific header inclusions in collective_permute_thunk and custom_call_thunk. These 2 thunks actually work on ROCm. - Partially enable ptxas_utils.h to make things build on ROCm. Full-fledged solution is on PR #30884.
Documentation |
|---|
 |
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 for the purposes of conducting 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 backwards compatible API for other languages.
Keep up-to-date with release announcements and security updates by subscribing to announce@tensorflow.org. See all the mailing lists.
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 for CPU-only:
$ pip install tensorflow
Use the GPU package for CUDA-enabled GPU cards:
$ pip install tensorflow-gpu
Nightly binaries are available for testing using the tf-nightly and tf-nightly-gpu packages on PyPi.
$ python
>>> import tensorflow as tf >>> tf.enable_eager_execution() >>> tf.add(1, 2).numpy() 3 >>> hello = tf.constant('Hello, TensorFlow!') >>> hello.numpy() '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.
We use GitHub issues for tracking requests and bugs, please see TensorFlow Discuss for general questions and discussion, and please direct specific questions to Stack Overflow.
The TensorFlow project strives to abide by generally accepted best practices in open-source software development:
| 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 | |
| Linux s390x Nightly | Nightly | |
| Linux s390x CPU Stable Release | Release | |
| Linux ppc64le CPU Nightly | Nightly | |
| Linux ppc64le CPU Stable Release | Release | |
| Linux ppc64le GPU Nightly | Nightly | |
| Linux ppc64le GPU Stable Release | Release | |
| Linux CPU with Intel® MKL-DNN Nightly | Nightly | |
| Linux CPU with Intel® MKL-DNN Supports Python 2.7, 3.4, 3.5, and 3.6 | 1.13.1 pypi | |
| 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.