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#!/bin/bash
#SBATCH --job-name=snn_smnist
#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_smnist.out
#SBATCH --error=runs/slurm_logs/%j_smnist.err
# ============================================================
# Sequential MNIST Experiment
# ============================================================
# This is a HARD benchmark:
# - 784 timesteps (one pixel at a time)
# - Requires long-range temporal memory
# - Shallow networks struggle, deep networks needed
#
# Expected: Lyapunov regularization significantly helps at depth 6+
# ============================================================
set -e
PROJECT_DIR="/projects/bfqt/users/yurenh2/ml-projects/snn-training"
cd "$PROJECT_DIR"
mkdir -p runs/slurm_logs data
echo "============================================================"
echo "Sequential MNIST Experiment"
echo "Job ID: $SLURM_JOB_ID | Node: $SLURM_NODELIST"
echo "Start: $(date)"
echo "============================================================"
nvidia-smi --query-gpu=name,memory.total --format=csv,noheader
echo "============================================================"
# Sequential MNIST with depths up to 10
python files/experiments/benchmark_experiment.py \
--dataset smnist \
--depths 2 4 6 8 10 \
--hidden_dim 128 \
--epochs 30 \
--batch_size 128 \
--lr 0.001 \
--lambda_reg 0.3 \
--lambda_target -0.1 \
--data_dir ./data \
--out_dir runs/benchmark \
--device cuda
echo "============================================================"
echo "Finished: $(date)"
echo "============================================================"
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