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 lets you 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.
See Installing TensorFlow for instructions on how to install our release binaries or 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 project on pypi.
pip install tf-nightlyin a clean environment to install the nightly tensorflow build. We currently only support CPU packages on Linux, Mac, and Windows. GPU packages on all platforms will arrive soon!
Individual whl files
- Linux CPU-only: Python 2 (build history) / Python 3.4 (build history) / Python 3.5 (build history)
- Linux GPU: Python 2 (build history) / Python 3.4 (build history) / Python 3.5 (build history)
- Mac CPU-only: Python 2 (build history) / Python 3 (build history)
- Windows CPU-only: Python 3.5 64-bit (build history) / Python 3.6 64-bit (build history)
- Windows GPU: Python 3.5 64-bit (build history) / Python 3.6 64-bit (build history)
- Android: demo APK, native libs (build history)
Try your first TensorFlow program
>>> import tensorflow as tf >>> hello = tf.constant('Hello, TensorFlow!') >>> sess = tf.Session() >>> sess.run(hello) 'Hello, TensorFlow!' >>> a = tf.constant(10) >>> b = tf.constant(32) >>> sess.run(a + b) 42 >>> sess.close()
For more information
- TensorFlow website
- TensorFlow White Papers
- TensorFlow Model Zoo
- TensorFlow MOOC on Udacity
- TensorFlow course at Stanford
Learn more about the TensorFlow community at the community page of tensorflow.org for a few ways to participate.