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Haskell bindings for TensorFlow
455e5a83c9
The script can now be run with, e.g. `env STACK_RESOLVER=lts-7.3 ci_build/outer_launch_tests.sh` and will use the specified version of the resolver. We can't quite enable this for lts-7.3 as the code is not pedantically clean. We will reconsider when 8.0.2 is available which removes `-Wredundant-constraints` from `-Wall`. |
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|---|---|---|
| ci_build | ||
| docker | ||
| docs/haddock | ||
| google-shim | ||
| tensorflow | ||
| tensorflow-core-ops | ||
| tensorflow-mnist | ||
| tensorflow-mnist-input-data | ||
| tensorflow-nn | ||
| tensorflow-opgen | ||
| tensorflow-ops | ||
| tensorflow-proto | ||
| tensorflow-queue | ||
| tensorflow-test | ||
| third_party | ||
| tools | ||
| .gitignore | ||
| .gitmodules | ||
| CONTRIBUTING.md | ||
| LICENSE | ||
| README.md | ||
| stack.yaml | ||
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.