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#!/bin/bash
#SBATCH --job-name=fs_vanilla
#SBATCH --partition=gpuH200x8
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=32
#SBATCH --gres=gpu:4
#SBATCH --mem=200G
#SBATCH --time=8:00:00
#SBATCH --output=/projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/fs_vanilla-%j.out
#SBATCH --error=/projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/fs_vanilla-%j.err
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/user_profiles.jsonl"
# Start vLLM servers
CUDA_VISIBLE_DEVICES=0,1 python -m vllm.entrypoints.openai.api_server \
--model hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4 \
--port 8004 --tensor-parallel-size 2 --gpu-memory-utilization 0.90 \
--max-model-len 8192 --dtype float16 --download-dir $HF_HOME &
CUDA_VISIBLE_DEVICES=2,3 python -m vllm.entrypoints.openai.api_server \
--model /projects/bfqt/users/yurenh2/ml-projects/personalization-user-model/models/llama-3.1-8b-instruct \
--port 8003 --tensor-parallel-size 2 --gpu-memory-utilization 0.90 \
--max-model-len 8192 --dtype bfloat16 &
for i in {1..60}; do
curl -s http://localhost:8004/health > /dev/null 2>&1 && curl -s http://localhost:8003/health > /dev/null 2>&1 && break
sleep 5
done
sleep 30
python scripts/run_experiments.py --methods vanilla \
--datasets math-hard --n-profiles 200 --n-sessions 30 --max-turns 15 \
--use-vllm --use-batch-processing --batch-size 100 --parallel-profiles 50 \
--output-dir ../results/fullscale --profile-path $PROFILE_PATH
pkill -f "vllm.entrypoints" 2>/dev/null || true
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