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gerrit-public.fairphone.software / platform / external / tensorflow / 6affacedbbea1a55cf4b7e33f881c6cd8c3c2493
commit6affacedbbea1a55cf4b7e33f881c6cd8c3c2493[log] [tgz]
authorAustin Anderson <angerson@google.com>Tue Dec 05 11:59:17 2017 -0800
committerTensorFlower Gardener <gardener@tensorflow.org>Tue Dec 05 12:04:55 2017 -0800
tree098129ecfd4ff4549e84d9f865573a87fd1bddff
parent21e831dc4a3fe67331dc186d2493678f24838250 [diff]
Add android rule helpers and cleanup input loops

This change teaches the configure script how to search for Android NDK
and SDK installations and create new WORKSPACE rules pointing to them.
It also refactors many similar loop-over-user-input functions into using
a reusable method (not the more complex ones).

Specifying an SDK directory will further query for the available SDK API
levels and build tools versions, but it won't perform any compatibility
checks.

Like other settings, every android-related setting can be set beforehand
via an env param. The script will not ask for any Android settings if
there are already any android repository rules in the WORKSPACE.

The script will emit a warning if using an NDK version newer than 14 due
to https://github.com/bazelbuild/bazel/issues/4068.

PiperOrigin-RevId: 177989785
1 file changed
tree: 098129ecfd4ff4549e84d9f865573a87fd1bddff
  1. tensorflow/
  2. third_party/
  3. tools/
  4. util/
  5. .gitignore
  6. ACKNOWLEDGMENTS
  7. ADOPTERS.md
  8. arm_compiler.BUILD
  9. AUTHORS
  10. BUILD
  11. CODE_OF_CONDUCT.md
  12. CODEOWNERS
  13. configure
  14. configure.py
  15. CONTRIBUTING.md
  16. ISSUE_TEMPLATE.md
  17. LICENSE
  18. models.BUILD
  19. README.md
  20. RELEASE.md
  21. WORKSPACE
README.md
Linux CPULinux GPUMac OS CPUWindows CPUAndroid
Build StatusBuild StatusBuild StatusBuild StatusBuild Status

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.

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.

Installation

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. 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.

Individual whl files

Try your first TensorFlow program

$ python

>>> 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

Learn more about the TensorFlow community at the community page of tensorflow.org for a few ways to participate.

License

Apache License 2.0