STT-tensorflow/tensorflow/go

TensorFlow in Go

Construct and execute TensorFlow graphs in Go.

Warning

: The API defined in this package is not stable and can change without notice. The same goes for the awkward package path (github.com/tensorflow/tensorflow/tensorflow/go).

Quickstart

1. Download and extract the TensorFlow C library, preferably into /usr/local. GPU-enabled versions require CUDA 8.0 and cuDNN 5.1. For other versions, the TensorFlow C library will have to be built from source (see below).

   • Linux: CPU-only, GPU-enabled
   • OS X CPU-only, GPU-enabled

   The following shell snippet downloads and extracts into /usr/local:

   TF_TYPE="cpu" # Set to "gpu" for GPU support
   curl -L \
     "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.0.0.tar.gz" |
   sudo tar -C /usr/local -xz
   

2. go get this package (and run tests):

   go get github.com/tensorflow/tensorflow/tensorflow/go
   go test github.com/tensorflow/tensorflow/tensorflow/go
   

3. Done!

Installing into locations other than /usr/local

The TensorFlow C library (libtensorflow.so) needs to be available at build time (e.g., go build) and run time (go test or executing binaries). If the library has not been extracted into /usr/local, then it needs to be made available through the LIBRARY_PATH environment variable at build time and the LD_LIBRARY_PATH environment variable (DYLD_LIBRARY_PATH on OS X) at run time.

For example, if the TensorFlow C library was extracted into /dir, then:

export LIBRARY_PATH=/dir/lib
export LD_LIBRARY_PATH=/dir/lib   # For Linux
export DYLD_LIBRARY_PATH=/dir/lib # For OS X

Building the TensorFlow C library from source

If the "Quickstart" instructions above do not work (perhaps the release archives are not available for your operating system or architecture, or you're using a different version of CUDA/cuDNN), then the TensorFlow C library must be built from source.

Prerequisites

• bazel
• Environment to build TensorFlow from source code (Linux or OS X). If you don't need GPU support, then try the following: sh # Linux sudo apt-get install python swig python-numpy # OS X with homebrew brew install swig

Build

1. Download the source code

   go get -d github.com/tensorflow/tensorflow/tensorflow/go
   

2. Build the TensorFlow C library:

   cd ${GOPATH}/src/github.com/tensorflow/tensorflow
   ./configure
   bazel build --config opt //tensorflow:libtensorflow.so
   

   This can take a while (tens of minutes, more if also building for GPU).

3. Make libtensorflow.so available to the linker. This can be done by either:

   a. Copying it to a system location, e.g.,

   sudo cp ${GOPATH}/src/github.com/tensorflow/tensorflow/bazel-bin/tensorflow/libtensorflow.so /usr/local/lib
   

   OR

   b. Setting environment variables:

   export LIBRARY_PATH=${GOPATH}/src/github.com/tensorflow/tensorflow/bazel-bin/tensorflow
   # Linux
   export LD_LIBRARY_PATH=${GOPATH}/src/github.com/tensorflow/tensorflow/bazel-bin/tensorflow
   # OS X
   export DYLD_LIBRARY_PATH=${GOPATH}/src/github.com/tensorflow/tensorflow/bazel-bin/tensorflow
   

4. Build and test:

   go test github.com/tensorflow/tensorflow/tensorflow/go
   

Generate wrapper functions for ops

Go functions corresponding to TensorFlow operations are generated in op/wrappers.go. To regenerate them:

Prerequisites:

• Protocol buffer compiler (protoc) 3.x
• The TensorFlow repository under GOPATH

go generate github.com/tensorflow/tensorflow/tensorflow/go/op

Support

Use stackoverflow and/or Github issues.

Contributions

Contributions are welcome. If making any signification changes, probably best to discuss on a Github issue before investing too much time. Github pull requests are used for contributions.