In the case that driver is out of date or does not support your GPU, and you need to download a driver from the NVIDIA home page, similarly prefer a distribution-specific package (e.g., deb, rpm)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're using Linux you should always consider installing the driver through the package manager of your distribution. You can find detailed instructions on the NVIDIA home page. To use the Julia GPU stack, you need to install the NVIDIA driver for your system and GPU. 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. as long as Julia is supported on it and there exists an NVIDIA driver and CUDA toolkit for your platform. We support the same operation systems that NVIDIA supports: Linux, and Windows. Again, the exact requirements are recorded in CUDA.jl's manifest, but often the following instructions will work: pkg> add GPUCompiler#master In the case you want to use the development version of CUDA.jl with other packages, you cannot use the manifest and you need to manually install those dependencies from the master branch. Pkg> instantiate # to install correct dependencies julia/dev/CUDA.jl # or wherever you have CUDA.jl checked out This information is recorded in the manifest at the root of the repository, which you can use by starting Julia from the CUDA.jl directory with the -project flag: $ cd. Often, however, the development version of this package itself relies on unreleased versions of other packages. In some cases, you might need to use the master version of this package, e.g., because it includes a specific fix you need. Or, equivalently, via the Pkg API: julia> import Pkg Pkg.add("CUDA") You can easily do that using the package manager: pkg> add CUDA Package installationįor most users, installing the latest tagged version of CUDA.jl will be sufficient. The former should be installed by you or your system administrator, while the latter can be automatically downloaded by Julia using the artifact subsystem. The Julia CUDA stack requires users to have a functional NVIDIA driver and corresponding CUDA toolkit.
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