# tensorflow/tensorflow

## 📖 项目介绍

**`Documentation`**

[TensorFlow](https://www.tensorflow.org/) is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of [tools](https://www.tensorflow.org/resources/tools), [libraries](https://www.tensorflow.org/resources/libraries-extensions), and [community](https://www.tensorflow.org/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 within the Machine Intelligence team at Google Brain to conduct research in machine learning and neural networks. However, the framework is versatile enough to be used in other areas as well.

TensorFlow provides stable [Python](https://www.tensorflow.org/api_docs/python) and [C++](https://www.tensorflow.org/api_docs/cc) APIs, as well as a non-guaranteed backward compatible API for [other languages](https://www.tensorflow.org/api_docs).

Keep up-to-date with release announcements and security updates by subscribing to [announce@tensorflow.org](https://groups.google.com/a/tensorflow.org/forum/#!forum/announce).

## Install

See the [TensorFlow install guide](https://www.tensorflow.org/install) for the [pip package](https://www.tensorflow.org/install/pip), to [enable GPU support](https://www.tensorflow.org/install/gpu), use a [Docker container](https://www.tensorflow.org/install/docker), and [build from source](https://www.tensorflow.org/install/source).

To install the current release, which includes support for [CUDA-enabled GPU cards](https://www.tensorflow.org/install/gpu) *(Ubuntu and Windows)*:

```
 pip install tensorflow
```

Other devices (DirectX and MacOS-metal) are supported using [Device Plugins](https://www.tensorflow.org/install/gpu_plugins#available_devices).

A smaller CPU-only TensorFlow package is also available:

```
 pip install tensorflow-cpu
```

To update TensorFlow to the latest version, add the `--upgrade` flag to the commands above.

*Nightly binaries are available for testing using the [tf-nightly](https://pypi.python.org/pypi/tf-nightly) and [tf-nightly-cpu](https://pypi.python.org/pypi/tf-nightly-cpu) packages on PyPI.*

#### _Try your first TensorFlow program_

```
shell
$ python
```

```
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](https://www.tensorflow.org/tutorials/).

## Contribution guidelines

If you want to contribute to TensorFlow, be sure to review the [Contribution Guidelines](/content/projects/CONTRIBUTING.md). This project adheres to TensorFlow's [Code of Conduct](/content/projects/CODE_OF_CONDUCT.md). By participating, you are expected to uphold this code.

We use [GitHub Issues](https://github.com/tensorflow/tensorflow/issues) for tracking requests and bugs, please see [TensorFlow Forum](https://discuss.tensorflow.org/) for general questions and discussion, and please direct specific questions to [Stack Overflow](https://stackoverflow.com/questions/tagged/tensorflow).

The TensorFlow project strives to abide by generally accepted best practices in open-source software development.

## Patching guidelines

Follow these steps to patch a specific version of TensorFlow, for example, to apply fixes to bugs or security vulnerabilities:

* Clone the TensorFlow repository and switch to the appropriate branch for your desired version—for example, `r2.8` for version 2.8.
* Apply the desired changes (i.e., cherry-pick them) and resolve any code conflicts.
* Run TensorFlow tests and ensure they pass.
* [Build](https://www.tensorflow.org/install/source) the TensorFlow pip package from source.

## Continuous build status

You can find more community-supported platforms and configurations in the [TensorFlow SIG Build Community Builds Table](https://github.com/tensorflow/build#community-supported-tensorflow-builds).

### Official Builds

Build Type | Status | Artifacts
--- | --- | ---
**Linux CPU** |  | [PyPI](https://pypi.org/project/tf-nightly/)
**Linux GPU** |  | [PyPI](https://pypi.org/project/tf-nightly-gpu/)
**Linux XLA** |  | TBA
**macOS** |  | [PyPI](https://pypi.org/project/tf-nightly/)
**Windows CPU** |  | [PyPI](https://pypi.org/project/tf-nightly/)
**Windows GPU** |  | [PyPI](https://pypi.org/project/tf-nightly-gpu/)
**Android** |  | [Download](https://bintray.com/google/tensorflow/tensorflow/_latestVersion)
**Raspberry Pi 0 and 1** |  | [Py3](https://storage.googleapis.com/tensorflow-nightly/tensorflow-1.10.0-cp34-none-linux_armv6l.whl)
**Raspberry Pi 2 and 3** |  | [Py3](https://storage.googleapis.com/tensorflow-nightly/tensorflow-1.10.0-cp34-none-linux_armv7l.whl)

## Resources

* [TensorFlow.org](https://www.tensorflow.org/)
* [TensorFlow Tutorials](https://www.tensorflow.org/tutorials/)
* [TensorFlow Official Models](https://github.com/tensorflow/models/tree/master/official)
* [TensorFlow Examples](https://github.com/tensorflow/examples)
* [TensorFlow Codelabs](https://codelabs.developers.google.com/?cat=TensorFlow)
* [TensorFlow Blog](https://blog.tensorflow.org/)
* [Learn ML with TensorFlow](https://www.tensorflow.org/resources/learn-ml)
* [TensorFlow Community](https://www.tensorflow.org/community) and how to [Contribute](https://www.tensorflow.org/community/contribute).

## License

[Apache License 2.0](/content/projects/LICENSE/index.html)

### 🔗 项目信息

**创建时间:** 2015/11/7

**最后更新:** 2026/6/15

**开源协议:** Apache License 2.0

**项目大小:** 1333.2k KB

### 📊 项目统计

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