f91460113a* Studio: pin CUDA_DEVICE_ORDER=PCI_BUS_ID and list GPUs at startup On a mixed-GPU host, Studio could load a model onto a different physical GPU than the one it selected. The free-VRAM probe numbers GPUs via nvidia-smi (PCI-bus order), but CUDA defaults to FASTEST_FIRST ordering, so a selected index written into CUDA_VISIBLE_DEVICES resolved to the wrong card. Example: 5090 + RTX PRO 6000, the picker chose the emptier RTX PRO 6000 (nvidia-smi index 1) but CUDA read index 1 as the 5090. Pin CUDA_DEVICE_ORDER=PCI_BUS_ID at import (before any CUDA context is created) in both the Studio entrypoint and the hardware module, so torch, nvidia-smi, and CUDA_VISIBLE_DEVICES share one index space. setdefault keeps an explicit user override intact. Child processes inherit it via os.environ. Also list every detected CUDA GPU with its index at startup instead of naming only device 0, matching nvidia-smi -L and making the selected index unambiguous on multi-GPU hosts. * Studio: make CUDA_DEVICE_ORDER tests exercise module import and respect user override * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Studio: guard full _print_cuda_device_list body and fix test PYTHONPATH trailing separator * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: Daniel Han <danielhanchen@gmail.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> |
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| .. | ||
| assets | ||
| auth | ||
| core | ||
| hub | ||
| loggers | ||
| models | ||
| plugins | ||
| requirements | ||
| routes | ||
| state | ||
| storage | ||
| tests | ||
| utils | ||
| __init__.py | ||
| _platform_compat.py | ||
| cloudflare_tunnel.py | ||
| colab.py | ||
| main.py | ||
| run.py | ||
| startup_banner.py | ||