#!/bin/bash #SBATCH --job-name=quick_batch_a100 #SBATCH --account=bfqt-delta-gpu #SBATCH --partition=gpuA100x4 #SBATCH --gres=gpu:4 #SBATCH --nodes=1 #SBATCH --ntasks=1 #SBATCH --cpus-per-task=16 #SBATCH --mem=128G #SBATCH --time=01:00:00 #SBATCH --output=/projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/quick_batch_a100-%j.out #SBATCH --error=/projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/quick_batch_a100-%j.err # Quick test: 10 profiles × 5 sessions = 50 sessions on A100 # Tests batch (vanilla) processing while H200 queue is busy set -e cd /projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/collaborativeagents source /u/yurenh2/miniforge3/etc/profile.d/conda.sh conda activate eval export HF_HOME=/projects/bfqt/users/yurenh2/hf_cache/huggingface export PYTHONPATH="${PWD}:${PWD}/../src:${PYTHONPATH}" MODEL_8B="/projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/models/llama-3.1-8b-instruct" PORT_USER=8004 PORT_AGENT=8003 echo "============================================" echo "Quick Test: Batch Processing on A100" echo "============================================" echo "Profiles: 10" echo "Sessions/profile: 5" echo "Total: 50 sessions" echo "" date nvidia-smi --query-gpu=index,name,memory.total --format=csv echo "" # Kill any existing servers pkill -f "vllm.entrypoints" 2>/dev/null || true sleep 2 # Start vLLM servers echo "Starting 8B user simulator (GPU 0-1, TP=2)..." CUDA_VISIBLE_DEVICES=0,1 python -m vllm.entrypoints.openai.api_server \ --model $MODEL_8B \ --port $PORT_USER \ --tensor-parallel-size 2 \ --gpu-memory-utilization 0.85 \ --max-model-len 4096 \ --disable-log-requests \ --dtype bfloat16 & SERVER_USER_PID=$! echo "Starting 8B agent (GPU 2-3, TP=2)..." CUDA_VISIBLE_DEVICES=2,3 python -m vllm.entrypoints.openai.api_server \ --model $MODEL_8B \ --port $PORT_AGENT \ --tensor-parallel-size 2 \ --gpu-memory-utilization 0.85 \ --max-model-len 4096 \ --disable-log-requests \ --dtype bfloat16 & SERVER_AGENT_PID=$! echo "Waiting for servers..." for i in $(seq 1 100); do READY_USER=$(curl -s http://localhost:$PORT_USER/health > /dev/null 2>&1 && echo 1 || echo 0) READY_AGENT=$(curl -s http://localhost:$PORT_AGENT/health > /dev/null 2>&1 && echo 1 || echo 0) if [ "$READY_USER" = "1" ] && [ "$READY_AGENT" = "1" ]; then echo "Both servers ready after $((i*3))s" break fi if [ $((i % 20)) -eq 0 ]; then echo " Still waiting... ($((i*3))s)" fi sleep 3 done if ! curl -s http://localhost:$PORT_USER/health > /dev/null; then echo "ERROR: User server not healthy"; kill $SERVER_USER_PID $SERVER_AGENT_PID 2>/dev/null; exit 1 fi if ! curl -s http://localhost:$PORT_AGENT/health > /dev/null; then echo "ERROR: Agent server not healthy"; kill $SERVER_USER_PID $SERVER_AGENT_PID 2>/dev/null; exit 1 fi echo "Both servers healthy" echo "" # Run quick test with vanilla (batch) echo "============================================" echo "Test: BATCH processing (vanilla method)" echo "============================================" START=$(date +%s) PROFILE_PATH="/projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/collaborativeagents/data/complex_profiles_v2/profiles_200.jsonl" python scripts/run_experiments.py \ --methods vanilla \ --datasets mmlu \ --n-profiles 10 \ --n-sessions 5 \ --use-vllm \ --batch-size 50 \ --parallel-profiles 10 \ --output-dir ../results/quick_test_batch_a100 \ --profile-path "$PROFILE_PATH" END=$(date +%s) ELAPSED=$((END-START)) echo "" echo "Vanilla (batch) completed in ${ELAPSED}s" # Cleanup echo "" echo "Cleaning up..." kill $SERVER_USER_PID $SERVER_AGENT_PID 2>/dev/null || true echo "" echo "============================================" echo "QUICK TEST RESULTS (A100)" echo "============================================" echo "" echo "Vanilla (BATCH): ${ELAPSED}s for 50 sessions" echo "" if [ $ELAPSED -gt 0 ]; then THROUGHPUT=$((50 * 3600 / ELAPSED)) echo "Throughput: ${THROUGHPUT} sessions/hr" fi echo "" echo "Results saved to: ../results/quick_test_batch_a100/" echo "" date