The Julia CUDA stack requires users to have a functional NVIDIA driver and corresponding CUDA toolkit. The former should be installed by you or your system administrator, while the latter can be automatically downloaded by Julia using the artifact subsystem.
All three major operation systems are supported: Linux, Windows and macOS. However, that support is subject to NVIDIA providing a CUDA toolkit for your system, subsequently macOS support might be deprecated soon.
Similarly, we support x86, ARM, PPC, ... as long as Julia is supported on it and there exists an NVIDIA driver and CUDA toolkit for your platform. The main development platform (and the only CI system) however is x86_64 on Linux, so if you are using a more exotic combination there might be bugs.
To use the Julia GPU stack, you need to install the NVIDIA driver for your system and GPU. You can find detailed instructions on the NVIDIA home page.
If you're using Linux you should always consider installing the driver through the package manager of your distribution. In the case that driver is out of date or does not support your GPU, and you need to download a driver from the NVIDIA home page, similarly prefer a distribution-specific package (e.g., deb, rpm) instead of the generic runfile option.
If you are using a shared system, ask your system administrator on how to install or load the NVIDIA driver. Generally, you should be able to find and use the CUDA driver library, called
libcuda.dll on Linux,
libcuda.dylib on macOS and
nvcuda64.dll on Windows. You should also be able to execute the
nvidia-smi command, which lists all available GPUs you have access to.
Finally, to be able to use all of the Julia GPU stack you need to have permission to profile GPU code. On Linux, that means loading the
nvidia kernel module with the
NVreg_RestrictProfilingToAdminUsers=0 option configured (e.g., in
/etc/modprobe.d). Refer to the following document for more information.
There are two different options to provide CUDA: either you install it yourself in a way that is discoverable by the Julia CUDA packages, or you let the packages download CUDA from artifacts. If you can use artifacts (i.e., you are not using an unsupported platform or have no specific requirements), it is recommended to do so: The CUDA toolkit is tightly coupled to the NVIDIA driver, and compatibility is automatically taken into account when selecting an artifact to use.
Use of artifacts is the default option: Importing CUDA.jl will automatically download CUDA upon first use of the API. You can inspect details about the process by enabling debug logging:
$ JULIA_DEBUG=CUDA julia julia> using CUDA julia> CUDA.version() ┌ Debug: Trying to use artifacts... └ @ CUDA CUDA/src/bindeps.jl:52 ┌ Debug: Using CUDA 10.2.89 from an artifact at /home/tim/Julia/depot/artifacts/93956fcdec9ac5ea76289d25066f02c2f4ebe56e └ @ CUDA CUDA/src/bindeps.jl:108 v"10.2.89"
If artifacts are unavailable for your platform, the Julia CUDA packages will look for a local CUDA installation:
julia> CUDA.version() ┌ Debug: Trying to use artifacts... └ @ CUDA CUDA/src/bindeps.jl:52 ┌ Debug: Could not find a compatible artifact. └ @ CUDA CUDA/src/bindeps.jl:73 ┌ Debug: Trying to use local installation... └ @ CUDA CUDA/src/bindeps.jl:114 ┌ Debug: Found local CUDA 10.0.326 at /usr/local/cuda-10.0/targets/aarch64-linux, /usr/local/cuda-10.0 └ @ CUDA CUDA/src/bindeps.jl:141 v"10.0.326"
You might want to disallow use of artifacts, e.g., because an optimized CUDA installation is available for your system. You can do so by setting the environment variable
To troubleshoot discovery of a local CUDA installation, you can set
JULIA_DEBUG=CUDA and see the various paths where CUDA.jl looks. By setting any of the
CUDA_PATH environment variables, you can guide the package to a specific directory.