<div align="center">
<img src="https://www.tensorflow.org/images/tf_logo_transp.png"><br><br>
</div>
-----------------
| **`Documentation`** |
|-----------------|
| [](https://www.tensorflow.org/api_docs/) |
**TensorFlow** is an open source software library for numerical computation
using data flow graphs. The graph nodes represent mathematical operations, while
the graph edges represent the multidimensional data arrays (tensors) that flow
between them. This flexible architecture enables you to deploy computation to
one or more CPUs or GPUs in a desktop, server, or mobile device without
rewriting code. TensorFlow also includes
[TensorBoard](https://github.com/tensorflow/tensorboard), a data visualization
toolkit.
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's for C++, Go, Java, JavaScript and Swift.
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).
## Installation
To install the current release for CPU-only:
```
pip install tensorflow
```
Use the GPU package for CUDA-enabled GPU cards:
```
pip install tensorflow-gpu
```
*See [Installing TensorFlow](https://www.tensorflow.org/install) for detailed
instructions, and how to build from source.*
People who are a little more adventurous can also try our nightly binaries:
**Nightly pip packages**
* We are pleased to announce that TensorFlow now offers nightly pip packages
under the [tf-nightly](https://pypi.python.org/pypi/tf-nightly) and
[tf-nightly-gpu](https://pypi.python.org/pypi/tf-nightly-gpu) project on pypi.
Simply run `pip install tf-nightly` or `pip install tf-nightly-gpu` in a clean
environment to install the nightly TensorFlow build. We support CPU and GPU
packages on Linux, Mac, and Windows.
#### *Try your first TensorFlow program*
```shell
$ python
```
```python
>>> import tensorflow as tf
>>> tf.enable_eager_execution()
>>> tf.add(1, 2).numpy()
3
>>> hello = tf.constant('Hello, TensorFlow!')
>>> hello.numpy()
'Hello, TensorFlow!'
```
Learn more examples about how to do specific tasks in TensorFlow at the
[tutorials page of tensorflow.org](https://www.tensorflow.org/tutorials/).
## Contribution guidelines
**If you want to contribute to TensorFlow, be sure to review the [contribution
guidelines](CONTRIBUTING.md). This project adheres to TensorFlow's
[code of conduct](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, so please see
[TensorFlow Discuss](https://groups.google.com/a/tensorflow.org/forum/#!forum/discuss)
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:
[](https://bestpractices.coreinfrastructure.org/projects/1486)
## Continuous build status
### Official Builds
| Build Type | Status | Artifacts |
| --- | --- | --- |
| **Linux CPU** | [](https://storage.googleapis.com/tensorflow-kokoro-build-badges/ubuntu-cc.html) | [pypi](https://pypi.org/project/tf-nightly/) |
| **Linux GPU** | [](https://storage.googleapis.com/tensorflow-kokoro-build-badges/ubuntu-gpu-py3.html) | [pypi](https://pypi.org/project/tf-nightly-gpu/) |
| **Linux XLA** | [](https://storage.googleapis.com/tensorflow-kokoro-build-badges/ubuntu-xla.html) | TBA |
| **MacOS** | [](https://storage.googleapis.com/tensorflow-kokoro-build-badges/macos-py2-cc.html) | [pypi](https://pypi.org/project/tf-nightly/) |
| **Windows CPU** | [](https://storage.googleapis.com/tensorflow-kokoro-build-badges/windows-cpu.html) | [pypi](https://pypi.org/project/tf-nightly/) |
| **Windows GPU** | [](https://storage.googleapis.com/tensorflow-kokoro-build-badges/windows-gpu.html) | [pypi](https://pypi.org/project/tf-nightly-gpu/) |
| **Android** | [](https://storage.googleapis.com/tensorflow-kokoro-build-badges/android.html) | [](https://bintray.com/google/tensorflow/tensorflow/_latestVersion) |
| **Raspberry Pi 0 and 1** | [](https://storage.googleapis.com/tensorflow-kokoro-build-badges/rpi01-py2.html) [](https://storage.googleapis.com/tensorflow-kokoro-build-badges/rpi01-py3.html) | [Py2](https://storage.googleapis.com/tensorflow-nightly/tensorflow-1.10.0-cp27-none-linux_armv6l.whl) [Py3](https://storage.googleapis.com/tensorflow-nightly/tensorflow-1.10.0-cp34-none-linux_armv6l.whl) |
| **Raspberry Pi 2 and 3** | [](https://storage.googleapis.com/tensorflow-kokoro-build-badges/rpi23-py2.html) [](https://storage.googleapis.com/tensorflow-kokoro-build-badges/rpi23-py3.html) | [Py2](https://storage.googleapis.com/tensorflow-nightly/tensorflow-1.10.0-cp27-none-linux_armv7l.whl) [Py3](https://storage.googleapis.com/tensorflow-nightly/tensorflow-1.10.0-cp34-none-linux_armv7l.whl) |
### Community Supported Builds
Build Type | Status | Artifacts
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------
**IBM s390x** | [](http://ibmz-ci.osuosl.org/job/TensorFlow_IBMZ_CI/) | TBA
**Linux ppc64le CPU** Nightly | [](https://powerci.osuosl.org/job/TensorFlow_PPC64LE_CPU_Build/) | [Nightly](https://powerci.osuosl.org/job/TensorFlow_PPC64LE_CPU_Nightly_Artifact/)
**Linux ppc64le CPU** Stable Release | [](https://powerci.osuosl.org/job/TensorFlow_PPC64LE_CPU_Release_Build/) | [Release](https://powerci.osuosl.org/job/TensorFlow_PPC64LE_CPU_Release_Build/)
**Linux ppc64le GPU** Nightly | [](https://powerci.osuosl.org/job/TensorFlow_PPC64LE_GPU_Build/) | [Nightly](https://powerci.osuosl.org/job/TensorFlow_PPC64LE_GPU_Nightly_Artifact/)
**Linux ppc64le GPU** Stable Release | [](https://powerci.osuosl.org/job/TensorFlow_PPC64LE_GPU_Release_Build/) | [Release](https://powerci.osuosl.org/job/TensorFlow_PPC64LE_GPU_Release_Build/)
**Linux CPU with Intel® MKL-DNN** Nightly | [](https://tensorflow-ci.intel.com/job/tensorflow-mkl-linux-cpu/) | [Nightly](https://tensorflow-ci.intel.com/job/tensorflow-mkl-build-whl-nightly/)
**Linux CPU with Intel® MKL-DNN** Python 2.7<br> **Linux CPU with Intel® MKL-DNN** Python 3.4<br> **Linux CPU with Intel® MKL-DNN** Python 3.5<br> **Linux CPU with Intel® MKL-DNN** Python 3.6 | [](https://tensorflow-ci.intel.com/job/tensorflow-mkl-build-release-whl/lastStableBuild) | [1.12.0 py2.7](https://storage.googleapis.com/intel-optimized-tensorflow/tensorflow-1.12.0-cp27-cp27mu-linux_x86_64.whl)<br>[1.12.0 py3.4](https://storage.googleapis.com/intel-optimized-tensorflow/tensorflow-1.12.0-cp34-cp34m-linux_x86_64.whl)<br>[1.12.0 py3.5](https://storage.googleapis.com/intel-optimized-tensorflow/tensorflow-1.12.0-cp35-cp35m-linux_x86_64.whl)<br>[1.12.0 py3.6](https://storage.googleapis.com/intel-optimized-tensorflow/tensorflow-1.12.0-cp36-cp36m-linux_x86_64.whl)
## For more information
* [TensorFlow Website](https://www.tensorflow.org)
* [TensorFlow Tutorials](https://www.tensorflow.org/tutorials/)
* [TensorFlow Model Zoo](https://github.com/tensorflow/models)
* [TensorFlow Twitter](https://twitter.com/tensorflow)
* [TensorFlow Blog](https://medium.com/tensorflow)
* [TensorFlow Course at Stanford](https://web.stanford.edu/class/cs20si)
* [TensorFlow Roadmap](https://www.tensorflow.org/community/roadmap)
* [TensorFlow White Papers](https://www.tensorflow.org/about/bib)
* [TensorFlow YouTube Channel](https://www.youtube.com/channel/UC0rqucBdTuFTjJiefW5t-IQ)
* [TensorFlow Visualization Toolkit](https://github.com/tensorflow/tensorboard)
Learn more about the TensorFlow community at the [community page of tensorflow.org](https://www.tensorflow.org/community) for a few ways to participate.
## License
[Apache License 2.0](LICENSE)