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authorYurenHao0426 <blackhao0426@gmail.com>2026-01-13 23:49:05 -0600
committerYurenHao0426 <blackhao0426@gmail.com>2026-01-13 23:49:05 -0600
commitcd99d6b874d9d09b3bb87b8485cc787885af71f1 (patch)
tree59a233959932ca0e4f12f196275e07fcf443b33f /scripts/run_depth_scaling_cifar100.sbatch
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+#!/bin/bash
+#SBATCH --job-name=snn_cifar100_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=64G
+#SBATCH --time=48:00:00
+#SBATCH --output=runs/slurm_logs/%j_cifar100_depth.out
+#SBATCH --error=runs/slurm_logs/%j_cifar100_depth.err
+
+# ============================================================
+# CIFAR-100 Depth Scaling Benchmark
+# ============================================================
+# KEY EXPERIMENT: Show that deep SNNs outperform shallow ones
+# when trained with Lyapunov regularization.
+#
+# CIFAR-100 (100 classes) is ideal because:
+# - Complex enough that shallow networks plateau
+# - Deep networks can learn richer representations
+# - Standard benchmark with known baselines
+#
+# Expected results:
+# - Shallow (4 layers): Similar for both methods
+# - Deep (16 layers): Vanilla fails/plateaus, Lyapunov succeeds
+# ============================================================
+
+set -e
+
+PROJECT_DIR="/projects/bfqt/users/yurenh2/ml-projects/snn-training"
+cd "$PROJECT_DIR"
+
+mkdir -p runs/slurm_logs data
+
+echo "============================================================"
+echo "CIFAR-100 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 "============================================================"
+
+# Test depths: 4, 8, 12, 16, 20 conv layers
+python files/experiments/depth_scaling_benchmark.py \
+ --dataset cifar100 \
+ --depths 4 8 12 16 20 \
+ --T 4 \
+ --epochs 150 \
+ --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 "============================================================"