#!/bin/bash #SBATCH --job-name=al2_test_loss_sweep #SBATCH --mail-type=FAIL,END #SBATCH --mail-user=dingqy@umich.edu #SBATCH --nodes=1 #SBATCH --ntasks-per-node=1 #SBATCH --cpus-per-task=4 #SBATCH --mem-per-cpu=11GB #SBATCH --time=4:00:00 #SBATCH --account=ahowens1 #SBATCH --partition=spgpu #SBATCH --qos=foe #SBATCH --reservation=spgpu_foe_nodes #SBATCH --gres=gpu:a40:1 #SBATCH --output=/nfs/turbo/coe-ahowens-nobackup/dingqy/al2_test_loss_sweep-%x-%j.log # # audioldm2 test-loss sweep across all production ckpts (concat + controlnet). # Sharded into 4 single-GPU jobs: # SHARD=1 → controlnet 500 - 12000 (9 ckpts) # SHARD=2 → controlnet 14000 - 30000 (9 ckpts) # SHARD=3 → concat 2000 - 22000 (11 ckpts) # SHARD=4 → concat 24000 - 38600 (19 ckpts) # # Submit: # for s in 1 2 3 4; do SHARD=$s sbatch eval_audioldm2_test_loss.sbat; done set -eu source /nfs/turbo/coe-ahowens-nobackup/dingqy/miniforge3/etc/profile.d/conda.sh source /home/dingqy/.hf_token export HF_CACHE=/nfs/turbo/coe-ahowens-nobackup/dingqy/.cache/huggingface conda activate new_audioldm : "${SHARD:=1}" build_cn() { for s in "$@"; do printf "audioldm2_bg2fg_controlnet_rebalance/step_%08d\n" $s; done; } build_concat() { for s in "$@"; do printf "audioldm2_bg2fg_rebalance/step_%08d\n" $s; done; } case "$SHARD" in 1) CKPTS=$(build_cn 500 1000 1500 2000 4000 6000 8000 10000 12000) ;; 2) CKPTS=$(build_cn 14000 16000 18000 20000 22000 24000 26000 28000 30000) ;; 3) CKPTS=$(build_concat 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 22000) ;; 4) CKPTS=$(build_concat 24000 26000 28000 30000 32000 34000 36000 36200 36400 36600 36800 37000 37200 37400 37600 37800 38000 38200 38600) ;; *) echo "unknown SHARD=$SHARD (use 1/2/3/4)"; exit 1 ;; esac OUT=/nfs/turbo/coe-ahowens-nobackup/dingqy/al2_test_loss_shard${SHARD}.json echo "shard=$SHARD out=$OUT" echo "$CKPTS" echo "" python /nfs/turbo/coe-ahowens-nobackup/dingqy/eval_audioldm2_test_loss.py \ --ckpts $CKPTS \ --out "$OUT" \ --batch-size 8 --num-workers 4 echo "" echo "=== shard $SHARD final ===" python -c " import json r = json.load(open('$OUT')) for k, v in sorted(r.items(), key=lambda x: x[1]['test_loss']): print(f' {v[\"test_loss\"]:.5f} ({v[\"arch\"]:>10}) {k}') "