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 and
tf-nightly-gpu project on pypi.
pip install tf-nightlyor
pip install tf-nightly-gpuin a clean
environment to install the nightly TensorFlow build. We support CPU and GPU
packages on Linux, Mac, and Windows.
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
>>> 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()
- 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.