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Haskell bindings for TensorFlow
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2016-11-23 10:55:35 -08:00
ci_build Add stack resolver version switch (#38). (#45) 2016-11-23 09:47:01 -08:00
docker Resolve #30 by using nightly. (#32) 2016-11-15 16:42:52 -08:00
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tensorflow Fix the build on ghc-8.0.1 (#38). (#40) 2016-11-21 22:20:08 -08:00
tensorflow-core-ops Fix the build on ghc-8.0.1 (#38). (#40) 2016-11-21 22:20:08 -08:00
tensorflow-mnist Make code --pedantic (#35) 2016-11-18 10:42:02 -08:00
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tensorflow-nn Fix the build on ghc-8.0.1 (#38). (#40) 2016-11-21 22:20:08 -08:00
tensorflow-opgen Fix Ref and Build semantics for generated code. (#37) 2016-11-21 10:19:15 -08:00
tensorflow-ops Fix the build on ghc-8.0.1 (#38). (#40) 2016-11-21 22:20:08 -08:00
tensorflow-proto Initial commit 2016-10-24 19:26:42 +00:00
tensorflow-queue Make code --pedantic (#35) 2016-11-18 10:42:02 -08:00
tensorflow-test Make code --pedantic (#35) 2016-11-18 10:42:02 -08:00
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LICENSE Initial commit 2016-10-24 19:26:42 +00:00
README.md Update OS X instructions (#42) to not require a separate ".so" file. (#44) 2016-11-22 16:15:34 -08:00
stack.yaml test case to show can't calculate grad for embedding (and associated fix) (#23) 2016-11-17 13:54:36 -08:00

Build Status

The tensorflow-haskell package provides Haskell bindings to TensorFlow.

This is not an official Google product.

Instructions

Build with Docker on Linux

As an expedient we use docker for building. Once you have docker working, the following commands will compile and run the tests.

git clone --recursive https://github.com/tensorflow/haskell.git tensorflow-haskell
cd tensorflow-haskell
IMAGE_NAME=tensorflow/haskell:v0
docker build -t $IMAGE_NAME docker
# TODO: move the setup step to the docker script.
stack --docker --docker-image=$IMAGE_NAME setup
stack --docker --docker-image=$IMAGE_NAME test

There is also a demo application:

cd tensorflow-mnist
stack --docker --docker-image=$IMAGE_NAME build --exec Main

Build on Mac OS X

The following instructions were verified with Mac OS X El Capitan.

  • Install dependencies via Homebrew:

      brew install swig
      brew install bazel
    
  • Build the TensorFlow library and install it on your machine:

      cd third_party/tensorflow
      ./configure  # Choose the defaults when prompted
      bazel build -c opt tensorflow:libtensorflow_c.so
      install bazel-bin/tensorflow/libtensorflow_c.so /usr/local/lib/libtensorflow_c.dylib
      install_name_tool -id libtensorflow_c.dylib /usr/local/lib/libtensorflow_c.dylib
      cd ../..
    
  • Run stack:

      stack test
    

Note: you may need to upgrade your version of Clang if you get an error like the following:

tensorflow/core/ops/ctc_ops.cc:60:7: error: return type 'tensorflow::Status' must match previous return type 'const ::tensorflow::Status' when lambda expression has unspecified explicit return type
    return Status::OK();

In that case you can just upgrade XCode and then run gcc --version to get the new version of the compiler.