#!/bin/bash
#SBATCH --job-name=snn_cifar10
#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=08:00:00
#SBATCH --output=runs/slurm_logs/%j_cifar10.out
#SBATCH --error=runs/slurm_logs/%j_cifar10.err

# ============================================================
# CIFAR-10 Rate-Coded Experiment
# ============================================================
# Challenge:
# - 3072 input dimensions (32x32x3 flattened)
# - Requires hierarchical feature learning
# - Deep networks essential for good accuracy
#
# This is the hardest benchmark - real image classification
# ============================================================

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 Rate-Coded 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 "============================================================"

# CIFAR-10 with deeper networks (needs more capacity)
python files/experiments/benchmark_experiment.py \
    --dataset cifar10 \
    --depths 4 6 8 10 12 \
    --hidden_dim 256 \
    --epochs 50 \
    --batch_size 64 \
    --lr 0.0005 \
    --T 100 \
    --lambda_reg 0.5 \
    --lambda_target -0.2 \
    --data_dir ./data \
    --out_dir runs/benchmark \
    --device cuda

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