| #SBATCH --time=02:00:00 | |
| #SBATCH --nodes=1 | |
| #SBATCH --ntasks-per-node=4 | |
| #SBATCH --mem=64G | |
| #SBATCH --gres=gpu:a100:1 | |
| #SBATCH --job-name="probe15_seal_milk" | |
| #SBATCH --output="logs/probe15_seal_milk_%j.out" | |
| export PYTHONUNBUFFERED=1 | |
| echo "============================================" | |
| echo "Probe 15: Seal Milk OOD Topology Benchmark" | |
| echo "============================================" | |
| echo "Started: $(date)" | |
| echo "Node: $(hostname)" | |
| echo "GPU: $(nvidia-smi --query-gpu=name --format=csv,noheader 2>/dev/null || echo 'N/A')" | |
| # βββ Environment ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | |
| source /work/ratul1/supantha/miniconda3/etc/profile.d/conda.sh | |
| conda activate glycanml | |
| pip install umap-learn --quiet 2>/dev/null | |
| cd /work/ratul1/supantha/glycan-SD-VS/bert_training_v3/v3.1_cluster_training | |
| # βββ Run Probe 15 ββββββββββββββββββββββββββββββββββββββββββββββββββββββββ | |
| python3 -u bert_v6_contrastive/scripts/probe_15_seal_milk_ood.py \ | |
| --model v6 \ | |
| --task all | |
| echo "" | |
| echo "============================================" | |
| echo "Completed: $(date)" | |
| echo "============================================" | |