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