docs: note CUDA_VISIBLE_DEVICES workaround for multi-GPU systems#75
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Importing gpu4pyscf allocates memory on every visible CUDA device, which conflicts with PyTorch and with other processes sharing those GPUs (e.g. in MPI-parallel workloads). Document the CUDA_VISIBLE_DEVICES workaround and link to the upstream tracking issue. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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May 26, 2026
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Adds a short note to the GPU getting-started section: importing
gpu4pyscfallocates memory on every visible CUDA device, which conflicts with PyTorch and with other processes sharing those GPUs (e.g. in MPI-parallel workloads).Documents the
CUDA_VISIBLE_DEVICES=0workaround (and the MPI per-local-rank variant) and links to the upstream tracking issue pyscf/gpu4pyscf#435.