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code/run_remote.sh

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+#!/bin/bash
+# run_remote.sh — run one pool screening step on a multi-GPU machine (no Slurm).
+#   STEP=1 bash code/run_remote.sh            # run step 1 on every GPU (one vLLM server + one client shard per GPU)
+#   STEP=2 bash code/run_remote.sh --plan-only   # show the remaining work of a step, no GPU used
+# Env (optional): NGPU (default: nvidia-smi count), WORKERS (client threads per GPU; defaults per step),
+#                 VLLM (default: vllm on PATH), PY (default: python3), PORT0 (default 8001), GPU_UTIL (default 0.85)
+# Layout: this script lives in <package>/code/; items are read from <package>/results/pool/ (copy items/* there
+# once), frames from <package>/frames/pool_frames/<video_id>/, outputs go to <package>/results/pool/step<N>/.
+set -u
+CODE=$(cd "$(dirname "$0")" && pwd); ROOT=$(cd "$CODE/.." && pwd)
+export POOL_BASE=${POOL_BASE:-$ROOT/work_base}
+export POOL_FRAMES_DIR=${POOL_FRAMES_DIR:-$ROOT/frames/pool_frames}
+export POOL_RESULTS_ROOT=${POOL_RESULTS_ROOT:-$ROOT/results/pool}
+mkdir -p "$POOL_BASE" "$POOL_RESULTS_ROOT" "$ROOT/logs"
+PY=${PY:-python3}; VLLM=${VLLM:-vllm}; PORT0=${PORT0:-8001}; GPU_UTIL=${GPU_UTIL:-0.85}
+STEPS_PY="$CODE/exp/analysis/pool/pool_steps.py"
+STEP=${STEP:?set STEP=1|2|3}
+case "$STEP" in
+  1) MODEL="Qwen/Qwen3-VL-8B-Instruct"; REV=0c351dd01ed87e9c1b53cbc748cba10e6187ff3b; MAX_LEN=8192;  MAX_SEQS=64; MM_IMAGES=1;  WORKERS=${WORKERS:-32};;
+  2) MODEL="Qwen/Qwen3-VL-8B-Instruct"; REV=0c351dd01ed87e9c1b53cbc748cba10e6187ff3b; MAX_LEN=8192;  MAX_SEQS=32; MM_IMAGES=1;  WORKERS=${WORKERS:-16};;
+  3) MODEL="Qwen/Qwen3-VL-2B-Instruct"; REV=89644892e4d85e24eaac8bacfd4f463576704203; MAX_LEN=16384; MAX_SEQS=16; MM_IMAGES=32; WORKERS=${WORKERS:-8};;
+  *) echo "STEP must be 1, 2 or 3"; exit 2;;
+esac
+[ ! -e "$POOL_RESULTS_ROOT/pool_items_base.parquet" ] && { echo "copy items/* into $POOL_RESULTS_ROOT first"; exit 2; }
+if [ "${1:-}" = "--plan-only" ]; then "$PY" "$STEPS_PY" --step "$STEP" --plan-only --shard 0/1; exit $?; fi
+NGPU=${NGPU:-$(nvidia-smi -L 2>/dev/null | wc -l)}
+[ "$NGPU" -ge 1 ] || { echo "no GPU found"; exit 2; }
+export VLLM_USE_FLASHINFER_SAMPLER=0 PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True TOKENIZERS_PARALLELISM=false
+PIDS=(); EPS=()
+for i in $(seq 0 $((NGPU - 1))); do
+  PORT=$((PORT0 + i)); EPS+=("http://127.0.0.1:$PORT/v1")
+  CUDA_VISIBLE_DEVICES=$i "$VLLM" serve "$MODEL" --revision "$REV" --served-model-name "$MODEL" --host 127.0.0.1 --port "$PORT" \
+      --max-model-len "$MAX_LEN" --gpu-memory-utilization "$GPU_UTIL" --max-num-seqs "$MAX_SEQS" \
+      --limit-mm-per-prompt "{\"image\": $MM_IMAGES}" --mm-processor-cache-gb 0 --max-logprobs 20 \
+      > "$ROOT/logs/vllm_s${STEP}_gpu${i}.log" 2>&1 &
+  PIDS+=($!)
+done
+trap 'for p in "${PIDS[@]}"; do kill "$p" 2>/dev/null; done' EXIT
+for i in $(seq 0 $((NGPU - 1))); do
+  for t in $(seq 1 180); do curl -sf "${EPS[$i]}/models" >/dev/null 2>&1 && break; kill -0 "${PIDS[$i]}" 2>/dev/null || { echo "vLLM on GPU $i died, see logs/vllm_s${STEP}_gpu${i}.log"; exit 1; }; sleep 10; done
+  echo "[run_remote] step $STEP: vLLM ready on GPU $i (${EPS[$i]})"
+done
+CPIDS=()
+for i in $(seq 0 $((NGPU - 1))); do
+  "$PY" "$STEPS_PY" --step "$STEP" --endpoint "${EPS[$i]}" --shard "$i/$NGPU" --workers "$WORKERS" \
+      > "$ROOT/logs/client_s${STEP}_gpu${i}.log" 2>&1 &
+  CPIDS+=($!)
+done
+rc=0; for p in "${CPIDS[@]}"; do wait "$p" || rc=1; done
+echo "[run_remote] step $STEP finished rc=$rc; remaining:"; "$PY" "$STEPS_PY" --step "$STEP" --plan-only --shard 0/1 | tail -1
+exit $rc