cross-pytorch

Scripts for cross-compiling PyTorch with CUDA support on Linux.

Why?

Recent PyTorch binaries no longer support glibc 2.28 and earlier, and can no longer be compiled using older compilers.

How?

This repository has scripts that

1. Download a suitable cross-compiler to make sure that the resulting binaries are compatible with Ubuntu 18.04 Bionic, Debian 10 Buster, Rocky 8 and later.
2. Download a suitable version of CUDA.
3. Cross-compile PyTorch and (some of) its dependencies with the rigth compiler and CUDA version.

Instructions

• In scripts/download-cuda.sh, select the appropriate cuda_version and cudnn_version (the former should be compatible with your driver version, use nvidia-smi to verify the version you have installed).
• In scripts/install-torch.sh, select the appropriate CMAKE_CUDA_ARCHITECTURES and TORCH_CUDA_ARCH_LIST options (see https://developer.nvidia.com/cuda-gpus).
• In scripts/install-torch.sh, select the version of PyTorch you need (main by default).
• You'll need a native C compiler, Python 3 and some standard tools like wget and git. Ccache is recommended and enabled by default, but can be commented out in scripts/install-torch.sh
• Finally, run the following commands:

./install-toolchain.sh # ~120 MiB download
./download-cuda.sh # ~2 GiB download
./install-torch.sh # May take a couple of hours, dependening on your machine's performance

Everything is downloaded and built locally in the current folder. No root privileges are required. The resulting PyTorch install tree can be found in toolchains/x86_64-bionic-linux-gnu/pytorch.

Add it to your CMAKE_FIND_ROOT_PATH, and use the scripts/toolchains/x86_64-bionic-linux-gnu.cmake toolchain file so CMake can locate CUDA correctly when building your own project. A CMake project and a CMakePresets.json file are provided as an example:

cmake --preset develop
cmake --build build -j --config Release
export LD_LIBRARY_PATH="$PWD/toolchains/x-tools/x86_64-bionic-linux-gnu/x86_64-bionic-linux-gnu/lib64"
./build/Release/example-torch

Further reading

• https://pytorch.org/cppdocs/
• https://pytorch.org/cppdocs/installing.html
• https://pytorch.org/tutorials/advanced/cpp_frontend.html