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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, 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 API and C APIs as well as without API backwards compatibility guarantee like C++, Go, Java, JavaScript and Swift.
Keep up to date with release announcements and security updates by subscribing to announce@tensorflow.org.
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 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 and
tf-nightly-gpu project on pypi.
Simply run
pip install tf-nightlyorpip install tf-nightly-gpuin 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
$ python>>> import tensorflow as tf
>>> tf.enable_eager_execution()
>>> tf.add(1, 2)
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.
Contribution guidelines
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, so 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:
Continuous build status
Official Builds
| 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 |
Community Supported Builds
| Build Type | Status | Artifacts |
|---|---|---|
| IBM s390x | TBA | |
| IBM ppc64le CPU | TBA | |
| IBM ppc64le GPU Nightly | Nightly | |
| IBM ppc64le GPU Stable Release | Release | |
| Linux CPU with Intel® MKL-DNN Nightly | Nightly | |
| Linux CPU with Intel® MKL-DNN Python 2.7 Linux CPU with Intel® MKL-DNN Python 3.4 Linux CPU with Intel® MKL-DNN Python 3.5 Linux CPU with Intel® MKL-DNN Python 3.6 |
1.11.0 py2.7 1.11.0 py3.4 1.11.0 py3.5 1.11.0 py3.6 |
For more information
- TensorFlow Website
- TensorFlow Tutorials
- TensorFlow Model Zoo
- TensorFlow Twitter
- TensorFlow Blog
- TensorFlow Course at Stanford
- TensorFlow Roadmap
- TensorFlow White Papers
- TensorFlow YouTube Channel
- TensorFlow Visualization Toolkit
Learn more about the TensorFlow community at the community page of tensorflow.org for a few ways to participate.