#!/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 "============================================================"