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# No kernel image available
This problem arises with the cuda version loaded by bitsandbytes is not supported by your GPU, or if you pytorch CUDA version mismatches. So solve this problem you need to debug ``$LD_LIBRARY_PATH``, ``$CUDA_HOME``, ``$PATH``. You can print these via ``echo $PATH``. You should look for multiple paths to different CUDA versions. This can include versions in your anaconda path, for example ``$HOME/anaconda3/lib``. You can check those versions via ``ls -l $HOME/anaconda3/lib/*cuda*`` or equivalent paths. Look at the CUDA versions of files in these paths. Does it match with ``nvidia-smi``?
If you are feeling lucky, you can also try to compile the library from source. This can be still problematic if your PATH variables have multiple cuda versions. As such, it is recommended to figure out path conflicts before you proceed with compilation.
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