0539621fa1
Training workers are spawned via multiprocessing spawn before detect_hardware() runs, so IS_ROCM is still False. If the user never set HIP_VISIBLE_DEVICES in their shell, _inherits_rocm_visibility is also False, leaving the worker with only CUDA_VISIBLE_DEVICES set. On ROCm hosts the HIP runtime honors HIP_VISIBLE_DEVICES over CUDA_VISIBLE_DEVICES, so the worker saw the full device list and torch raised "no usable HIP accelerator" on some setups. Fall back to probing torch.version.hip (a build-time attribute, safe to read before GPU init) to detect ROCm when neither IS_ROCM nor inherited env vars are available. Mirrors the existing fix in llama_cpp.py for llama-server subprocess GPU pinning. Fixes https://github.com/unslothai/unsloth/issues/5180 |
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| assets | ||
| auth | ||
| core | ||
| loggers | ||
| models | ||
| plugins | ||
| requirements | ||
| routes | ||
| state | ||
| storage | ||
| tests | ||
| utils | ||
| __init__.py | ||
| _platform_compat.py | ||
| colab.py | ||
| main.py | ||
| run.py | ||
| startup_banner.py | ||