# Troubleshooting

## Could not find a suitable CUDA installation

This means that CUDA.jl could not find or provide a CUDA toolkit. For more information, re-run with the JULIA_DEBUG environment variable set to CUDA.

If you're encountering this error when disabling artifacts through by setting JULIA_CUDA_USE_BINARYBUILDER=false, it is your own responsibility to make sure CUDA.jl can detect the necessary pieces, e.g., by putting CUDA's binaries and libraries in discoverable locations (i.e. on PATH, and on the library search path). Additionally, the CUDA_HOME environment can be used to point CUDA.jl to where the CUDA toolkit is installed, but that will only help if the contents of that directory have not been reorganized.

## UNKNOWN_ERROR(999)

If you encounter this error, there are several known issues that may be causing it:

• a mismatch between the CUDA driver and driver library: on Linux, look for clues in dmesg
• the CUDA driver is in a bad state: this can happen after resume. Try rebooting.

Generally though, it's impossible to say what's the reason for the error, but Julia is likely not to blame. Make sure your set-up works (e.g., try executing nvidia-smi, a CUDA C binary, etc), and if everything looks good file an issue.

Check and make sure the NVSMI folder is in your PATH. By default it may not be. Look in C:\Program Files\NVIDIA Corporation for the NVSMI folder - you should see nvml.dll within it. You can add this folder to your PATH and check that nvidia-smi runs properly.