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