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一个面向所有人的开源机器学习框架
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TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem oftools,libraries, andcommunity 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 Pythonand C++ APIs, as well as non-guaranteed backward compatible API forother languages.
Keep up-to-date with release announcements and security updates by subscribing toannounce@tensorflow.org. See all the mailing lists.
See the TensorFlow install guide for thepip package, toenable GPU support, use aDocker container, andbuild from source.
To install the current release, which includes support forCUDA-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 thetf-nightly andtf-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 theTensorFlow tutorials.
If you want to contribute to TensorFlow, be sure to review thecontribution guidelines. This project adheres to TensorFlow'scode of conduct. By participating, you are expected to uphold this code.
We use GitHub issues for tracking requests and bugs, please seeTensorFlow Discussfor general questions and discussion, and please direct specific questions toStack 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 |  | Py3 |
| Raspberry Pi 2 and 3 |  | Py3 |
| Libtensorflow MacOS CPU |  | Nightly GCS Official GCS |
| Libtensorflow Linux CPU |  | Nightly GCS Official GCS |
| Libtensorflow Linux GPU |  | Nightly GCS Official GCS |
| Libtensorflow Windows CPU |  | Nightly GCS Official GCS |
| Libtensorflow Windows GPU |  | Nightly GCS Official GCS |
| 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 aarch64 CPU Nightly (Linaro) | Nightly | |
| Linux aarch64 CPU Stable Release (Linaro) | Release 1.x & 2.x | |
| Linux aarch64 CPU Nightly (OpenLab) Python 3.6 | Nightly | |
| Linux aarch64 CPU Stable Release (OpenLab) | Release 1.15 / 2.x | |
| Linux CPU with Intel oneAPI Deep Neural Network Library (oneDNN) Nightly | Nightly | |
| Linux CPU with Intel oneAPI Deep Neural Network Library (oneDNN) Stable Release | Release 1.15 / 2.x | |
| Red Hat® Enterprise Linux® 7.6 CPU & GPU Python 2.7, 3.6 | 1.13.1 PyPI |
| Container Type | Status | Artifacts |
|---|---|---|
| TensorFlow aarch64 Neoverse-N1 CPU Stable (Linaro) Debian | Static | Release 2.3 |
Learn more about theTensorFlow community and how tocontribute.
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