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#!/bin/bash
#SBATCH --job-name=snn_grid_search
#SBATCH --account=bfqt-delta-gpu
#SBATCH --partition=gpuA40x4
#SBATCH --nodes=1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=8
#SBATCH --gpus-per-node=1
#SBATCH --mem=32G
#SBATCH --time=48:00:00
#SBATCH --output=runs/slurm_logs/%j_grid_search.out
#SBATCH --error=runs/slurm_logs/%j_grid_search.err
# ============================================================
# Hyperparameter Grid Search for Lyapunov-Regularized SNN
# ============================================================
# Uses CIFAR-10 (real data) with rate encoding
# Includes warmup for λ_reg to avoid killing learning early
#
# Grid:
# - Depths: 4, 6, 8, 10
# - λ_reg: 0.01, 0.05, 0.1, 0.2, 0.3
# - λ_target: 0.0, -0.05, -0.1, -0.2
#
# Total: 4 depths × 5 λ_reg × 4 λ_target = 80 configurations
# Estimated time: ~4 min/config × 80 = ~5-6 hours
# ============================================================
set -e
PROJECT_DIR="/projects/bfqt/users/yurenh2/ml-projects/snn-training"
cd "$PROJECT_DIR"
mkdir -p runs/slurm_logs
echo "============================================================"
echo "Hyperparameter Grid Search"
echo "Job ID: $SLURM_JOB_ID | Node: $SLURM_NODELIST"
echo "Start: $(date)"
echo "============================================================"
nvidia-smi --query-gpu=name,memory.total --format=csv,noheader
echo "============================================================"
# Using CIFAR-10 (real data) instead of synthetic
# Reduced grid: 4 depths × 5 λ_reg × 4 λ_target = 80 configs
# With warmup to avoid killing learning early
python files/experiments/hyperparameter_grid_search.py \
--depths 4 6 8 10 \
--lambda_regs 0.01 0.05 0.1 0.2 0.3 \
--lambda_targets 0.0 -0.05 -0.1 -0.2 \
--hidden_dim 256 \
--epochs 15 \
--batch_size 128 \
--T 8 \
--lr 0.001 \
--data_dir ./data \
--out_dir runs/grid_search_cifar10 \
--device cuda
echo "============================================================"
echo "Finished: $(date)"
echo "============================================================"
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