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Haskell bindings for TensorFlow
fc3d398ca9
* Add MNIST data to gitignore
* Add simple tensor round-trip benchmark
* Use deepseq + cleaner imports
* Use safe version of fromIntegral in FFI code
* Don't copy data when fetching tensors
BEFORE
benchmarking feedFetch/4 byte
time 55.79 μs (54.88 μs .. 56.62 μs)
0.998 R² (0.997 R² .. 0.999 R²)
mean 55.61 μs (55.09 μs .. 56.11 μs)
std dev 1.828 μs (1.424 μs .. 2.518 μs)
variance introduced by outliers: 34% (moderately inflated)
benchmarking feedFetch/4 KiB
time 231.4 μs (221.9 μs .. 247.3 μs)
0.988 R² (0.974 R² .. 1.000 R²)
mean 226.6 μs (224.1 μs .. 236.2 μs)
std dev 13.45 μs (7.115 μs .. 27.14 μs)
variance introduced by outliers: 57% (severely inflated)
benchmarking feedFetch/4 MiB
time 485.8 ms (424.6 ms .. 526.7 ms)
0.998 R² (0.994 R² .. 1.000 R²)
mean 515.7 ms (512.5 ms .. 517.9 ms)
std dev 3.320 ms (0.0 s .. 3.822 ms)
variance introduced by outliers: 19% (moderately inflated)
AFTER
benchmarking feedFetch/4 byte
time 53.11 μs (52.12 μs .. 54.22 μs)
0.996 R² (0.995 R² .. 0.998 R²)
mean 54.64 μs (53.59 μs .. 56.18 μs)
std dev 4.249 μs (2.910 μs .. 6.076 μs)
variance introduced by outliers: 75% (severely inflated)
benchmarking feedFetch/4 KiB
time 83.83 μs (82.72 μs .. 84.92 μs)
0.999 R² (0.998 R² .. 0.999 R²)
mean 83.82 μs (83.20 μs .. 84.35 μs)
std dev 1.943 μs (1.557 μs .. 2.614 μs)
variance introduced by outliers: 20% (moderately inflated)
benchmarking feedFetch/4 MiB
time 95.54 ms (93.62 ms .. 97.82 ms)
0.999 R² (0.998 R² .. 1.000 R²)
mean 96.61 ms (95.76 ms .. 97.51 ms)
std dev 1.408 ms (1.005 ms .. 1.889 ms)
|
||
|---|---|---|
| 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 | ||
| 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 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.