#!/bin/bash
#SBATCH --job-name=snn_cifar10_conv
#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=64G
#SBATCH --time=48:00:00
#SBATCH --output=runs/slurm_logs/%j_cifar10_conv.out
#SBATCH --error=runs/slurm_logs/%j_cifar10_conv.err

# ============================================================
# CIFAR-10 Conv-SNN Experiment (Proper Architecture)
# ============================================================
# Uses convolutional SNN that preserves spatial structure:
#   Image (3,32,32) → Rate Encoding → Conv-LIF-Pool → FC → Output
#
# Tests whether Lyapunov regularization helps deeper Conv-SNNs
# ============================================================

set -e

PROJECT_DIR="/projects/bfqt/users/yurenh2/ml-projects/snn-training"
cd "$PROJECT_DIR"

mkdir -p runs/slurm_logs data

echo "============================================================"
echo "CIFAR-10 Conv-SNN 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 "============================================================"

python files/experiments/cifar10_conv_experiment.py \
    --model simple \
    --T 25 \
    --epochs 50 \
    --batch_size 128 \
    --lr 0.001 \
    --lambda_reg 0.3 \
    --lambda_target -0.1 \
    --data_dir ./data \
    --out_dir runs/cifar10_conv \
    --device cuda

echo "============================================================"
echo "Finished: $(date)"
echo "============================================================"