| commit | 17c4db8be11f949c3a59f6beb75300f7b519bc00 | [log] [tgz] |
|---|---|---|
| author | Sergei Lebedev <slebedev@google.com> | Thu Oct 17 07:18:04 2019 -0700 |
| committer | TensorFlower Gardener <gardener@tensorflow.org> | Thu Oct 17 07:22:24 2019 -0700 |
| tree | 22ba674ee1802d86efee0a4276e85dd8adbd9fcc | |
| parent | 2542bbea51c5a76b6c35ba3ad108122e95d54b6d [diff] |
ConvertToEagerTensor now allocates on GPU if one is available and no device is specified This allows to get rid of redundant H->D transfers. For example tf.gather(tf.constant([42.0]), 0) would previously allocate both [42.0] and 0 on CPU, and then transfer both to GPU to compute Gather. This could potentially hurt ops with inputs pinned to host e.g. Range. PiperOrigin-RevId: 275252442 Change-Id: I7d85d3314b9701e7b9df76acea12c2fcfdf2960e
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 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.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, 3.6 and 3.7 | 1.14.0 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.