FAQ

How do I check that AMDGPU.jl works?

AMDGPU.functional() returns true when the ROCm stack is available and a GPU can be used. For a full report of detected devices, libraries and versions, use AMDGPU.versioninfo().

using AMDGPU
AMDGPU.functional()     # true if AMDGPU.jl can run on this machine
AMDGPU.versioninfo()    # detailed diagnostics

How should a package depend on AMDGPU.jl?

AMDGPU.jl loads on any machine, but only works when ROCm and a supported GPU are present. Code that should run with or without a GPU must therefore guard GPU use behind AMDGPU.functional() rather than assume it — importing the package is not enough.

using AMDGPU

if AMDGPU.functional()
    x = AMDGPU.ones(Float32, 1024)   # run on the GPU
else
    x = ones(Float32, 1024)          # CPU fallback
end

For a hard requirement of GPU hardware specifically, has_rocm_gpu() additionally checks that at least one device is present. For a heavier optional dependency, prefer a package extension that loads only when AMDGPU is available, following the pattern used by the wider Julia GPU ecosystem.

Which ROCm libraries are available?

Individual components are queried with AMDGPU.functional(component), useful when a feature depends on a specific library:

AMDGPU.functional(:rocblas)     # dense linear algebra (rocBLAS)
AMDGPU.functional(:rocsolver)   # factorizations (rocSOLVER)
AMDGPU.functional(:rocsparse)   # sparse arrays (rocSPARSE)
AMDGPU.functional(:rocfft)      # FFTs (rocFFT)
AMDGPU.functional(:rocrand)     # random numbers (rocRAND)
AMDGPU.functional(:MIOpen)      # deep-learning primitives (MIOpen)
AMDGPU.functional(:all)         # true only if every component is available

My GPU is not detected or a library is missing

Run AMDGPU.versioninfo() and check that hip and the library you need report as functional. Missing components usually mean the corresponding ROCm package is not installed. See Installation Info for platform-specific setup, including the package list for distributions such as Fedora.

I'm on Arch Linux and ROCm isn't working

For the last few ROCm releases, users have reported problems with the distro-provided ROCm builds and associated tools (#770, #696, #767). Some have had success with the opencl-amd-dev AUR package instead.

How do I control GPU memory usage?

ROCArrays are managed by Julia's garbage collector, and a HIP memory pool caches freed allocations. You can free eagerly, cap usage, and query current usage — see the Memory Allocation and Intrinsics page for AMDGPU.unsafe_free!, memory limits, and the caching allocator.

Where can I get help?

Ask on the Julia Discourse GPU domain or the #gpu channel of the Julia Slack. Bug reports and feature requests are welcome on the issue tracker.