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#!/bin/bash
#SBATCH --job-name=scale_b2
#SBATCH --account=bfqt-delta-gpu
#SBATCH --partition=gpuH200x8-interactive
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=32
#SBATCH --gres=gpu:4
#SBATCH --mem=200G
#SBATCH --time=01:00:00
#SBATCH --output=/projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/scale_b2-%j.out
#SBATCH --error=/projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/scale_b2-%j.err
# Scale Test Batch 2: Users 6-10, 15 sessions each, 3 methods
# With CollaborativeAgents-style prompts
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="/projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/src:$PYTHONPATH"
PROFILE_PATH="/projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/collaborativeagents/data/complex_profiles_v2/profiles_200.jsonl"
AGENT_MODEL="/projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/models/llama-3.1-8b-instruct"
USER_MODEL="meta-llama/Llama-3.1-70B-Instruct"
MEMORY_STORE="/projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/data/corpora/empty_store_b2"
echo "=== Scale Test Batch 2: 5 users × 15 sessions × 3 methods ==="
date
nvidia-smi --query-gpu=index,name,memory.total --format=csv
# Create separate memory store for batch 2
mkdir -p ${MEMORY_STORE}
> ${MEMORY_STORE}/memory_cards.jsonl
# Start vLLM servers
CUDA_VISIBLE_DEVICES=0,1 python -m vllm.entrypoints.openai.api_server \
--model $USER_MODEL \
--port 8004 --tensor-parallel-size 2 --gpu-memory-utilization 0.90 \
--max-model-len 16384 --dtype bfloat16 --download-dir $HF_HOME &
CUDA_VISIBLE_DEVICES=2,3 python -m vllm.entrypoints.openai.api_server \
--model $AGENT_MODEL \
--port 8003 --tensor-parallel-size 2 --gpu-memory-utilization 0.45 \
--max-model-len 16384 --dtype bfloat16 &
echo "Waiting for vLLM servers..."
for i in {1..200}; do
if curl -s http://localhost:8004/health > /dev/null 2>&1; then
echo "User simulator ready after $((i*5))s"
break
fi
sleep 5
done
for i in {1..60}; do
if curl -s http://localhost:8003/health > /dev/null 2>&1; then
echo "Agent ready after $((i*5))s"
break
fi
sleep 5
done
sleep 5
OUTPUT_DIR="../results/scale_test_b2_$(date +%Y%m%d_%H%M%S)"
# Run each method with profiles 6-10 (skip first 5)
for METHOD in vanilla rag rag_vector; do
echo ""
echo "============================================"
echo "Testing: $METHOD (users 6-10 × 15 sessions)"
echo "============================================"
# Clear memory store before each method
> ${MEMORY_STORE}/memory_cards.jsonl
rm -f ${MEMORY_STORE}/memory_embeddings.npy
date
python scripts/run_experiments.py --methods $METHOD \
--datasets math-hard --n-profiles 5 --n-sessions 15 --max-turns 15 \
--use-vllm --no-batch-processing --parallel-profiles 1 \
--profile-offset 5 \
--output-dir $OUTPUT_DIR --profile-path $PROFILE_PATH
echo "Method $METHOD completed"
if [ "$METHOD" != "vanilla" ]; then
echo "Final memory cards: $(wc -l < ${MEMORY_STORE}/memory_cards.jsonl 2>/dev/null || echo 0)"
fi
done
echo ""
echo "=== Scale Test Batch 2 Complete ==="
date
# Generate comparison
python3 << 'PYEOF'
import json
from pathlib import Path
output_base = sorted(Path("../results").glob("scale_test_b2_*"))[-1]
print(f"\n=== Results Summary (Batch 2) ===\nDir: {output_base}\n")
methods = ["vanilla", "rag", "rag_vector"]
results = {}
for subdir in output_base.iterdir():
if subdir.is_dir():
for method in methods:
result_file = subdir / method / "results.json"
if result_file.exists() and method not in results:
with open(result_file) as f:
results[method] = json.load(f)
if results:
print(f"{'Method':<12} {'Success':<10} {'Turns':<10} {'Enforce':<10} {'Sessions':<10}")
print("-" * 55)
for method in methods:
if method in results:
data = results[method]
n = len(data)
succ = sum(r['metrics']['task_success'] for r in data) / n
turns = sum(r['metrics']['total_turns'] for r in data) / n
enf = sum(r['metrics']['enforcement_count'] for r in data) / n
print(f"{method:<12} {succ:<10.1%} {turns:<10.1f} {enf:<10.1f} {n:<10}")
PYEOF
pkill -f "vllm.entrypoints" 2>/dev/null || true
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