The Operator Vectorization Library, or OVL, is a python productivity library for defining high performance custom operators for the TensorFlow platform. OVL enables TensorFlow users to easily write, test, and use custom operators in pure python without sacrificing the performance gains of implementing, building, and linking custom C++ or CUDA operators. OVL operators are not intended to replace the performance-tuned core API operators, but rather augment the API in cases where it does not cover the end-user's application particularly well. OVL operators are not intended to replace all custom operators -- sometimes there is no substitute to a human writing/tuning the C++/CUDA directly for an especially complicated operator that resides in a performance-critical section. Ultimately, the mission of OVL is to offer a 10x productivity gain for custom ops which exhibit at least 80% of the performance of an equivalently hand-written op.

Users write OVL ops by implementing a vectorized function which statelessly maps input tensors into output tensors. The key abstraction of the OVL programming model is a parallel for, or map. The user defines an abstractly shaped workgroup and a worker function which is applied over the indices of that workgroup. Each worker knows its indices within the workgroup and can read (write) from (to) any point in the input (output) tensors.

TODO: Provide link to Jenkins status here.

OVL is currently tested and supported under Ubuntu 14.04. These instructions will prepare an Ubuntu environment to use OVL.

OVL depends on TensorFlow 0.8.0 and works with both the CPU and GPU versions. Installation instructions are available here.

Note that the GPU version of TensorFlow requires the CUDA SDK 7.5 and cuDNN 4 to be installed on your system. Depending on how these dependencies are installed you may need to explicitly set the CUDA_HOME environment variable, typically:

export CUDA_HOME=/usr/local/cuda

If you see an error like: libnvrtc.so.7.5: cannot open shared object file: No such file or directory You may also need to make sure the CUDA libraries are on your library path, typically:

export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH

First, if you do not have pip >= 8.1.1 installed, install or upgrade it. You can check the installed version by running:

pip --version

If it is not installed or you have a version lower than 8.1.1 installed, upgrade it:

curl -O https://bootstrap.pypa.io/get-pip.py
sudo python get-pip.py

Install dependencies: nose2, protobuf compiler/libraries and g++:

sudo apt-get install python-nose2 protobuf-compiler libprotobuf-dev g++

Install the latest release of OVL:

sudo pip install git+https://github.com/hpe-cct/opveclib

If you see an error message during the install like libnvrtc.so.7.5: cannot open shared object file: No such file or directory you may need to explicitly pass an LD_LIBRARY_PATH to sudo to install the package:

sudo LD_LIBRARY_PATH=/usr/local/cuda/lib64 pip install git+https://github.com/hpe-cct/opveclib

To run the tests, simply run:

nose2 -F opveclib.examples.tensorflow_clustering opveclib.examples

This should take about 5 minutes on a CPU-only installation or 15 minutes on a GPU-enabled installation.