The target machine's card is busy with someone else's job, so a single
end-to-end script stalls on work that does not actually need a GPU.
Compiling the CUDA extensions needs nvcc, not a device, and downloading 18 GB
of data needs neither. Those are the slow parts (~50 min + ~30 min), so phase A
now runs entirely without the card:
run_setup.sh bootstrap, conda, extensions, patches, data no GPU
run_train.sh voxel_max measurement, training, evaluation GPU
run_setup reports the GPU but never fails on it, and verify_env.py gained
SKIP_CUDA_CHECK so import coverage still runs when no device is visible.
TORCH_CUDA_ARCH_LIST is stated rather than probed, since the card may be
unavailable at build time.
run_train waits for the GPU instead of failing when it is busy: it polls until
enough VRAM frees up (12h default), so it can be queued ahead of time. Past the
deadline it proceeds anyway and lets the measured voxel_max adapt to whatever
is actually free.
keepalive.sh now takes the phase to supervise. Replaces run_all.sh and RUN.md
with SETUP.md and TRAIN.md. Adds selfcheck.sh, which syntax-checks every script
and flags CRLF endings - a shell script with either fails at its first line,
which for an unattended weekend run means losing the weekend.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
