#!/bin/bash
#SBATCH --job-name=snn_cifar10_depth
#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_cifar10_depth.out
#SBATCH --error=runs/slurm_logs/%j_cifar10_depth.err

# ============================================================
# CIFAR-10 Depth Scaling Benchmark (Baseline)
# ============================================================
# CIFAR-10 is easier (10 classes) - used as baseline comparison.
#
# Reference from literature:
# - Spiking VGG 7 layers: ~88% accuracy
# - Spiking VGG 13 layers: ~91.6% accuracy
#
# This experiment validates our implementation against known results.
# ============================================================

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 Depth Scaling Benchmark"
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/depth_scaling_benchmark.py \
    --dataset cifar10 \
    --depths 4 8 12 16 \
    --T 4 \
    --epochs 100 \
    --batch_size 128 \
    --lr 0.001 \
    --lambda_reg 0.3 \
    --lambda_target -0.1 \
    --data_dir ./data \
    --out_dir runs/depth_scaling \
    --device cuda

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