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CUDA: filter devices on secondary discovery (#13317)

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We now do a deeper probe of CUDA devices to verify the library version has
the correct compute capability coverage for the device.  Due to ROCm also
interpreting the CUDA env var to filter AMD devices, we try to avoid setting
it which leads to problems in mixed vendor systems.  However without setting
it for this deeper probe, each CUDA library subprocess discovers all CUDA GPUs
and on systems with lots of GPUs, this can lead to hitting timeouts.  The fix is
to turn on the CUDA visibility env var just for this deeper probe use-case.
This commit is contained in:
Daniel Hiltgen
2025-12-03 12:58:16 -08:00
committed by GitHub
parent cc9555aff0
commit 3f30836734
3 changed files with 16 additions and 7 deletions
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5
discover/runner.go
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@@ -147,7 +147,7 @@ func GPUDevices(ctx context.Context, runners []ml.FilteredRunnerDiscovery) []ml.
wg.Add(1)
go func(i int) {
defer wg.Done()
extraEnvs := ml.GetVisibleDevicesEnv(devices[i : i+1])
extraEnvs := ml.GetVisibleDevicesEnv(devices[i:i+1], true)
devices[i].AddInitValidation(extraEnvs)
if len(bootstrapDevices(ctx2ndPass, devices[i].LibraryPath, extraEnvs)) == 0 {
slog.Debug("filtering device which didn't fully initialize",
@@ -333,7 +333,8 @@ func GPUDevices(ctx context.Context, runners []ml.FilteredRunnerDiscovery) []ml.
defer cancel()
// Apply any dev filters to avoid re-discovering unsupported devices, and get IDs correct
devFilter := ml.GetVisibleDevicesEnv(devices)
// We avoid CUDA filters here to keep ROCm from failing to discover GPUs in a mixed environment
devFilter := ml.GetVisibleDevicesEnv(devices, false)
for dir := range libDirs {
updatedDevices := bootstrapDevices(ctx, []string{ml.LibOllamaPath, dir}, devFilter)