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gerrit-public.fairphone.software / platform / external / tensorflow / 005a88f6cc6e4e8c94a4f2d1980737855c4592f4
commit005a88f6cc6e4e8c94a4f2d1980737855c4592f4[log] [tgz]
authorA. Unique TensorFlower <gardener@tensorflow.org>Fri Aug 25 13:12:06 2017 -0700
committerTensorFlower Gardener <gardener@tensorflow.org>Fri Aug 25 13:16:01 2017 -0700
tree4649cc8186d1a931b379e78e47208d3ef724e8ef
parentd8347c81628377ce99bb5d9e5fa55ddc8b7d47f3 [diff]
Add the ability to test AOT compilation in CodegenTestBase.

This change adds the ability to test AOT compilation. It also
changes the way that IR is obtained from the compiler by using
LLVMCompiler hooks.

CodegenTestBase is also modified in the following ways:
* New subclass for LLVM IR testing, with existing CompileAndVerifyIr
  moved into the subclass.
* CreateNewModuleWithEmbeddedIR() is removed.
* Calls to a FileCheck test now have a flag about which version
of the IR to use.
* FileCheck functionality is moved into its library.

PiperOrigin-RevId: 166515069
8 files changed
tree: 4649cc8186d1a931b379e78e47208d3ef724e8ef
  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
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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 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.

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:

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
>>>

For more information

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