AMDGPU.jl

Appearance

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().

julia
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.

julia
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:

julia
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 installed ROCm 7.14 or newer but it isn't detected ​

ROCm 7.14 changed its on-disk layout, installing libraries under a versioned core-<version> subdirectory (for example /opt/rocm/core-7.14/lib). AMDGPU.jl discovers this automatically, but if detection fails on a minimal or custom install, make sure ROCM_PATH points at the ROCm root (e.g. /opt/rocm) rather than at the versioned subdirectory. See Installation Info for details.

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.