| # One recaption.py worker per GPU inside the recap-vllm container; workers claim shards through lock files, | |
| # finished shards have .done (run) / .fixed (fix) markers, so rerunning this script resumes. | |
| # docker exec recap-vllm bash /workspace/prh-vlm/blip3o_recap/launch.sh 0,1,2,3,4,5,6 [out_dir] [run|fix] [extra args] | |
| # Several nodes may share one out_dir, so locks are kept by default; CLEAN_LOCKS=1 removes stale locks (only when no | |
| # worker is alive on any node). | |
| set -e | |
| GPUS=${1:-0,1,2,3,4,5,6,7} | |
| OUT=${2:-/workspace/prh-vlm/data/blip3o_recap/recap} | |
| MODE=${3:-run} | |
| cd /workspace/prh-vlm/blip3o_recap | |
| if pgrep -f "^python3 recaption.py" >/dev/null; then echo "workers already running" >&2; exit 1; fi | |
| mkdir -p "$OUT/logs" | |
| if [ "${CLEAN_LOCKS:-0}" = 1 ]; then rm -f "$OUT"/*.lock "$OUT"/*.fixlock; fi | |
| for g in ${GPUS//,/ }; do | |
| CUDA_VISIBLE_DEVICES=$g nohup python3 recaption.py $MODE --shards ../data/blip3o_recap/shards/*.tar --out "$OUT" \ | |
| --gpu-mem 0.85 "${@:4}" >> "$OUT/logs/${MODE}_$(hostname)_gpu$g.log" 2>&1 & | |
| done | |
| sleep 2; pgrep -f "^python3 recaption.py" | wc -l | |