1 unstable release
✓ Uses Rust 2018 edition
|0.1.0||Jul 21, 2019|
#53 in Machine learning
This library provides 2D convolutions accelerated with OpenCL. Convolutions are particularly useful for deep learning tasks, such as image recognition; they are a basic building block for convolutional neural networks.
The library is intended mostly for quick-and-dirty hacking in deep learning research, in which one needs a separate spatial convolution primitive. Note that full-scale DL frameworks (TensorFlow, PyTorch, etc.) will most probably be a more robust and scalable tool for more high-level tasks.
See crate docs for the examples of usage.
OpenCL has a variety of implementations. For quick testing, one may use POCL; it is open source and not tied to hardware (at the cost of being CPU-based, i.e., orders of magnitude slower than OpenCL implementations by GPU vendors). POCL may be installed from sources with the commands like these (showcased here for Ubuntu Xenial):
# Install utils for build apt-get install build-essential cmake pkg-config libhwloc-dev zlib1g-dev # Install OpenCL-related utils apt-get install ocl-icd-libopencl1 ocl-icd-dev ocl-icd-opencl-dev clinfo # Install LLVM / Clang from the official APT repository wget -O - https://apt.llvm.org/llvm-snapshot.gpg.key | apt-key add - add-apt-repository 'deb http://apt.llvm.org/xenial/ llvm-toolchain-xenial-8 main' apt-get update apt-get install clang-8 libclang-8-dev llvm-8 llvm-8-dev # Get POCL sources export POCL_VER=1.3 # latest stable version curl -sSL "https://github.com/pocl/pocl/archive/v$POCL_VER.tar.gz" > pocl-$POCL_VER.tar.gz tar xf "pocl-$POCL_VER.tar.gz" # Build POCL from the sources cd pocl-$POCL_VER mkdir build && cd build cmake -DWITH_LLVM_CONFIG=/usr/bin/llvm-config-8 -DCMAKE_INSTALL_PREFIX=/usr .. make # Verify installation clinfo # If successful, `clinfo` should display information about the POCL platform.
Licensed under the Apache 2.0 license.