I’ve just tagged new versions of CuArrays/CUDAnative/… and among the usual slurry of features and bug fixes there’s a major change in how the packages are built and loaded. There used to be a
Pkg.build step which could fail if you didn’t have a properly set-up CUDA GPU. This has made a lot of people very angry and been widely regarded as a bad move.
As an alternative, I have dropped this installation-time set-up and moved it to the precompilation phase, i.e., when you first import the package. As a result, you can now safely depend on CUDA packages since they won’t ever fail during installation. This is especially useful for clusters and containers, where you want to install packages in an environment that probably does not have a GPU.
Of course, loading the package might still fail if your user doesn’t have a CUDA GPU, so that’s why CUDAapi now provides a couple of useful functions to determine that:
using CUDAapi # this will NEVER fail if has_cuda() try using CuArrays # we have CUDA, so this should not fail catch ex # something is wrong with the user's set-up (or there's a bug in CuArrays) @warn "CUDA is installed, but CuArrays.jl fails to load" exception=(ex,catch_backtrace()) end end
CUDAapi.has_cuda_gpu() to check if the user actually has a GPU.
As a result of all this, it should be possible to safely depend on any of the CUDA packages, without your users seeing errors because of not having a CUDA GPU. This is important, because it means we can use regular package version compatibility rules and don’t have to roll our own.