Frequently Asked Questions

This page is a compilation of frequently asked questions and answers.

An old version of CUDA.jl keeps getting installed!

Sometimes it happens that a breaking version of CUDA.jl or one of its dependencies is released. If any package you use isn't yet compatible with this release, this will block automatic upgrade of CUDA.jl. For example, with Flux.jl v0.11.1 we get CUDA.jl v1.3.3 despite there being a v2.x release:

pkg> add Flux
  [587475ba] + Flux v0.11.1
pkg> add CUDA
  [052768ef] + CUDA v1.3.3

To examine which package is holding back CUDA.jl, you can "force" an upgrade by specifically requesting a newer version. The resolver will then complain, and explain why this upgrade isn't possible:

pkg> add CUDA.jl@2
  Resolving package versions...
ERROR: Unsatisfiable requirements detected for package Adapt [79e6a3ab]:
 Adapt [79e6a3ab] log:
 ├─possible versions are: [0.3.0-0.3.1, 0.4.0-0.4.2, 1.0.0-1.0.1, 1.1.0, 2.0.0-2.0.2, 2.1.0, 2.2.0, 2.3.0] or uninstalled
 ├─restricted by compatibility requirements with CUDA [052768ef] to versions: [2.2.0, 2.3.0]
 │ └─CUDA [052768ef] log:
 │   ├─possible versions are: [0.1.0, 1.0.0-1.0.2, 1.1.0, 1.2.0-1.2.1, 1.3.0-1.3.3, 2.0.0-2.0.2] or uninstalled
 │   └─restricted to versions 2 by an explicit requirement, leaving only versions 2.0.0-2.0.2
 └─restricted by compatibility requirements with Flux [587475ba] to versions: [0.3.0-0.3.1, 0.4.0-0.4.2, 1.0.0-1.0.1, 1.1.0] — no versions left
   └─Flux [587475ba] log:
     ├─possible versions are: [0.4.1, 0.5.0-0.5.4, 0.6.0-0.6.10, 0.7.0-0.7.3, 0.8.0-0.8.3, 0.9.0, 0.10.0-0.10.4, 0.11.0-0.11.1] or uninstalled
     ├─restricted to versions * by an explicit requirement, leaving only versions [0.4.1, 0.5.0-0.5.4, 0.6.0-0.6.10, 0.7.0-0.7.3, 0.8.0-0.8.3, 0.9.0, 0.10.0-0.10.4, 0.11.0-0.11.1]
     └─restricted by compatibility requirements with CUDA [052768ef] to versions: [0.4.1, 0.5.0-0.5.4, 0.6.0-0.6.10, 0.7.0-0.7.3, 0.8.0-0.8.3, 0.9.0, 0.10.0-0.10.4] or uninstalled, leaving only versions: [0.4.1, 0.5.0-0.5.4, 0.6.0-0.6.10, 0.7.0-0.7.3, 0.8.0-0.8.3, 0.9.0, 0.10.0-0.10.4]
       └─CUDA [052768ef] log: see above

A common source of these incompatibilities is having both CUDA.jl and the older CUDAnative.jl/CuArrays.jl/CUDAdrv.jl stack installed: These are incompatible, and cannot coexist. You can inspect in the Pkg REPL which exact packages you have installed using the status --manifest option.

Can you wrap this or that CUDA API?

If a certain API isn't wrapped with some high-level functionality, you can always use the underlying C APIs which are always available as unexported methods. For example, you can access the CUDA driver library as cu prefixed, unexported functions like CUDA.cuDriverGetVersion. Similarly, vendor libraries like CUBLAS are available through their exported submodule handles, e.g., CUBLAS.cublasGetVersion_v2.

Any help on designing or implementing high-level wrappers for this low-level functionality is greatly appreciated, so please consider contributing your uses of these APIs on the respective repositories.

When installing CUDA.jl on a cluster, why does Julia stall during precompilation?

If you're working on a cluster, precompilation may stall if you have not requested sufficient memory. You may also wish to make sure you have enough disk space prior to installing CUDA.jl.