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