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<!-- WEHUB_ZH_README -->
> [!NOTE]
> 本文档由 WeHub 基于上游 README 翻译整理，属于社区翻译，非官方中文文档。
> [English](./README.en.md) · [原始项目](https://github.com/tensorflow/tensorflow) · [上游 README](https://github.com/tensorflow/tensorflow/blob/HEAD/README.md)
> 原作者、版权与许可证归属以原始项目及本仓库 LICENSE 文件为准。
<div align="center">
<img src="https://www.tensorflow.org/images/tf_logo_horizontal.png">
</div>
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**`Documentation`** |
------------------- |
[![Documentation](https://img.shields.io/badge/api-reference-blue.svg)](https://www.tensorflow.org/api_docs/) |
[TensorFlow](https://www.tensorflow.org/) 是一个端到端的开源机器学习平台。它拥有全面且灵活的生态系统，涵盖[工具](https://www.tensorflow.org/resources/tools),、[库](https://www.tensorflow.org/resources/libraries-extensions),和[社区](https://www.tensorflow.org/community)资源，使研究人员能够推动机器学习（ML）的最前沿发展，也让开发者能够轻松构建和部署基于 ML 的应用。
TensorFlow 最初由 Google Brain 旗下机器智能团队的研究人员和工程师开发，旨在开展机器学习和神经网络研究。不过，该框架功能广泛，足以应用于其他领域。
TensorFlow 提供稳定的 [Python](https://www.tensorflow.org/api_docs/python) 和 [C++](https://www.tensorflow.org/api_docs/cc) API，以及不保证向后兼容的[其他语言](https://www.tensorflow.org/api_docs). API。
订阅 [announce@tensorflow.org](https://groups.google.com/a/tensorflow.org/forum/#!forum/announce). 以获取最新的发布公告和安全更新。查看所有[邮件列表](https://www.tensorflow.org/community/forums).。
## 安装
请参阅 [TensorFlow 安装指南](https://www.tensorflow.org/install)，了解如何使用 [pip 包](https://www.tensorflow.org/install/pip),、[启用 GPU 支持](https://www.tensorflow.org/install/gpu),、使用 [Docker 容器](https://www.tensorflow.org/install/docker),以及[从源码构建](https://www.tensorflow.org/install/source).。
安装当前版本（包含对[支持 CUDA 的 GPU 显卡](https://www.tensorflow.org/install/gpu)的支持 *（Ubuntu 和 Windows）*）：
```
pip install tensorflow
```
其他设备（DirectX 和 MacOS-metal）可通过[设备插件](https://www.tensorflow.org/install/gpu_plugins#available_devices).获得支持。
另外也提供了一个更小的仅支持 CPU 的 TensorFlow 包：
```
pip install tensorflow-cpu
```
要将 TensorFlow 更新到最新版本，请在上面的命令中添加 `--upgrade` 标志。
*用于测试的每日构建版本可通过 PyPI 上的 [tf-nightly](https://pypi.python.org/pypi/tf-nightly) 和 [tf-nightly-cpu](https://pypi.python.org/pypi/tf-nightly-cpu) 包获取。*
#### *尝试你的第一个 TensorFlow 程序*
```shell
$ python
```
```python
>>> import tensorflow as tf
>>> tf.add(1, 2).numpy()
3
>>> hello = tf.constant('Hello, TensorFlow!')
>>> hello.numpy()
b'Hello, TensorFlow!'
```
更多示例请参阅 [TensorFlow 教程](https://www.tensorflow.org/tutorials/).。
## 贡献指南
**如果你想为 TensorFlow 做贡献，请务必阅读[贡献指南](CONTRIBUTING.md)。本项目遵循 TensorFlow 的[行为准则](CODE_OF_CONDUCT.md)。参与即表示你同意遵守该准则。**
**我们使用 [GitHub Issues](https://github.com/tensorflow/tensorflow/issues) 来跟踪需求和错误，请访问 [TensorFlow 论坛](https://discuss.tensorflow.org/) 进行一般性讨论和问答，具体问题请到 [Stack Overflow](https://stackoverflow.com/questions/tagged/tensorflow).** 上提问。**
TensorFlow 项目致力于遵循开源软件开发中公认的最佳实践。
## 补丁指南
按照以下步骤为特定版本的 TensorFlow 打补丁，例如修复错误或安全漏洞：
* 克隆 TensorFlow 仓库并切换到所需版本的相应分支——例如，版本 2.8 使用 `r2.8` 分支。
* 应用所需的更改（即 cherry-pick），并解决所有代码冲突。
* 运行 TensorFlow 测试并确保通过。
* 从源码[构建](https://www.tensorflow.org/install/source) TensorFlow pip 包。
## 持续构建状态
你可以在 [TensorFlow SIG Build 社区构建表](https://github.com/tensorflow/build#community-supported-tensorflow-builds). 中找到更多社区支持的平台和配置。
### 官方构建
构建类型 | 状态 | 制品
----------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------
**Linux CPU** | [![Status](https://storage.googleapis.com/tensorflow-kokoro-build-badges/ubuntu-cc.svg)](https://storage.googleapis.com/tensorflow-kokoro-build-badges/ubuntu-cc.html) | [PyPI](https://pypi.org/project/tf-nightly/)
**Linux GPU** | [![Status](https://storage.googleapis.com/tensorflow-kokoro-build-badges/ubuntu-gpu-py3.svg)](https://storage.googleapis.com/tensorflow-kokoro-build-badges/ubuntu-gpu-py3.html) | [PyPI](https://pypi.org/project/tf-nightly-gpu/)
**Linux XLA** | [![Status](https://storage.googleapis.com/tensorflow-kokoro-build-badges/ubuntu-xla.svg)](https://storage.googleapis.com/tensorflow-kokoro-build-badges/ubuntu-xla.html) | 待定
**macOS** | [![Status](https://storage.googleapis.com/tensorflow-kokoro-build-badges/macos-py2-cc.svg)](https://storage.googleapis.com/tensorflow-kokoro-build-badges/macos-py2-cc.html) | [PyPI](https://pypi.org/project/tf-nightly/)
**Windows CPU** | [![Status](https://storage.googleapis.com/tensorflow-kokoro-build-badges/windows-cpu.svg)](https://storage.googleapis.com/tensorflow-kokoro-build-badges/windows-cpu.html) | [PyPI](https://pypi.org/project/tf-nightly/)
**Windows GPU** | [![Status](https://storage.googleapis.com/tensorflow-kokoro-build-badges/windows-gpu.svg)](https://storage.googleapis.com/tensorflow-kokoro-build-badges/windows-gpu.html) | [PyPI](https://pypi.org/project/tf-nightly-gpu/)
**Android** | [![Status](https://storage.googleapis.com/tensorflow-kokoro-build-badges/android.svg)](https://storage.googleapis.com/tensorflow-kokoro-build-badges/android.html) | [下载](https://bintray.com/google/tensorflow/tensorflow/_latestVersion)
**Raspberry Pi 0 和 1** | [![Status](https://storage.googleapis.com/tensorflow-kokoro-build-badges/rpi01-py3.svg)](https://storage.googleapis.com/tensorflow-kokoro-build-badges/rpi01-py3.html) | [Py3](https://storage.googleapis.com/tensorflow-nightly/tensorflow-1.10.0-cp34-none-linux_armv6l.whl)
**Raspberry Pi 2 和 3** | [![Status](https://storage.googleapis.com/tensorflow-kokoro-build-badges/rpi23-py3.svg)](https://storage.googleapis.com/tensorflow-kokoro-build-badges/rpi23-py3.html) | [Py3](https://storage.googleapis.com/tensorflow-nightly/tensorflow-1.10.0-cp34-none-linux_armv7l.whl)
## 资源
* [TensorFlow.org](https://www.tensorflow.org))
* [TensorFlow 教程](https://www.tensorflow.org/tutorials/))
* [TensorFlow 官方模型](https://github.com/tensorflow/models/tree/master/official))
* [TensorFlow 示例](https://github.com/tensorflow/examples))
* [TensorFlow 编程实练](https://codelabs.developers.google.com/?cat=TensorFlow))
* [TensorFlow 博客](https://blog.tensorflow.org))
* [用 TensorFlow 学习机器学习](https://www.tensorflow.org/resources/learn-ml))
* [TensorFlow Twitter](https://twitter.com/tensorflow))
* [TensorFlow YouTube](https://www.youtube.com/channel/UC0rqucBdTuFTjJiefW5t-IQ))
* [TensorFlow 模型优化路线图](https://www.tensorflow.org/model_optimization/guide/roadmap))
* [TensorFlow 白皮书](https://www.tensorflow.org/about/bib))
* [TensorBoard 可视化工具包](https://github.com/tensorflow/tensorboard))
* [TensorFlow 代码搜索](https://cs.opensource.google/tensorflow/tensorflow))
了解更多关于 [TensorFlow 社区](https://www.tensorflow.org/community)) 的信息，以及如何 [贡献代码](https://www.tensorflow.org/community/contribute).)。
## 课程
* [Coursera](https://www.coursera.org/search?query=TensorFlow))
* [Udacity](https://www.udacity.com/courses/all?search=TensorFlow))
* [Edx](https://www.edx.org/search?q=TensorFlow))
## 许可
[Apache License 2.0](LICENSE)
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