1.8 KiB
How to use a CUDA version that is different from PyTorch
Some features of bitsandbytes may need a newer CUDA version than regularly supported by PyTorch binaries from conda / pip. In that case you can use the following instructions to load a precompiled bitsandbytes binary that works for you.
Installing or determining the CUDA installation
Determine the path of the CUDA version that you want to use. Common paths paths are:
/usr/local/cuda
/usr/local/cuda-XX.X
where XX.X is the CUDA version number.
You can also install CUDA version that you need locally with a script provided by bitsandbytes as follows:
wget https://raw.githubusercontent.com/TimDettmers/bitsandbytes/main/cuda_install.sh
# Syntax cuda_install CUDA_VERSION INSTALL_PREFIX EXPORT_TO_BASH
# CUDA_VERSION in {110, 111, 112, 113, 114, 115, 116, 117, 118, 120, 121, 122}
# EXPORT_TO_BASH in {0, 1} with 0=False and 1=True
# For example, the following installs CUDA 11.7 to ~/local/cuda-11.7 and exports the path to your .bashrc
bash cuda install 117 ~/local 1
Setting the environmental variables CUDA_HOME, CUDA_VERSION, and LD_LIBRARY_PATH
To manually override the PyTorch installed CUDA version you need to set to variable, like so:
export CUDA_HOME=<PATH>
export CUDA_VERSION=<VERSION>
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:<PATH>
For example, to use the local install path from above:
export CUDA_HOME=/home/tim/local/cuda-11.7
export CUDA_VERSION=117
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/tim/local/cuda-11.7
It is best to add these lines to the .bashrc file to make them permanent.
If you now launch bitsandbytes with these environmental variables the PyTorch CUDA version will be overridden by the new CUDA version and a different bitsandbytes library is loaded (in this case version 117).