add run prune_rounds_3
Browse files
applied/opt06_uticket_prune/prune_rounds_3.summary.json
ADDED
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|
| 1 |
+
{
|
| 2 |
+
"key": "opt06_uticket_prune/prune_rounds_3",
|
| 3 |
+
"tag": "prune_rounds_3",
|
| 4 |
+
"script": "opt06_uticket_prune.py",
|
| 5 |
+
"script_sha256_16": "13957bb36508b8cf",
|
| 6 |
+
"hf_folder": "applied/opt06_uticket_prune",
|
| 7 |
+
"priority": 2,
|
| 8 |
+
"note": "",
|
| 9 |
+
"phase": 9,
|
| 10 |
+
"n_phases": 10,
|
| 11 |
+
"method": "harmonic_matched",
|
| 12 |
+
"command": [
|
| 13 |
+
"/usr/bin/python3",
|
| 14 |
+
"-m",
|
| 15 |
+
"torch.distributed.run",
|
| 16 |
+
"--standalone",
|
| 17 |
+
"--nproc_per_node=2",
|
| 18 |
+
"/kaggle/working/scripts/opt06_uticket_prune.py",
|
| 19 |
+
"--method",
|
| 20 |
+
"harmonic_matched",
|
| 21 |
+
"--data-root",
|
| 22 |
+
"/kaggle/working/data",
|
| 23 |
+
"--epochs",
|
| 24 |
+
"60",
|
| 25 |
+
"--seed",
|
| 26 |
+
"1234",
|
| 27 |
+
"--batch-size",
|
| 28 |
+
"64",
|
| 29 |
+
"--event-eval",
|
| 30 |
+
"--no-resume",
|
| 31 |
+
"--decision-margin",
|
| 32 |
+
"2.0",
|
| 33 |
+
"--hub-dir",
|
| 34 |
+
"/kaggle/working/hub/opt06_uticket_prune/prune_rounds_3",
|
| 35 |
+
"--keep-epoch-weights",
|
| 36 |
+
"1",
|
| 37 |
+
"--resplit",
|
| 38 |
+
"block",
|
| 39 |
+
"--drop-rest",
|
| 40 |
+
"--bench",
|
| 41 |
+
"--prune-rounds",
|
| 42 |
+
"3"
|
| 43 |
+
],
|
| 44 |
+
"flags": [
|
| 45 |
+
"--prune-rounds",
|
| 46 |
+
"3"
|
| 47 |
+
],
|
| 48 |
+
"epochs_requested": 60,
|
| 49 |
+
"gpus": 2,
|
| 50 |
+
"relative_epoch_cost": 1.0,
|
| 51 |
+
"exit_code": 0,
|
| 52 |
+
"status": "ok",
|
| 53 |
+
"duration_s": 320.7,
|
| 54 |
+
"started_utc": "2026-08-23T04:03:57.671625+00:00",
|
| 55 |
+
"finished_utc": "2026-08-23T04:09:18.399792+00:00",
|
| 56 |
+
"optimization_summary": {
|
| 57 |
+
"optimization": "uticket_workload_balanced_pruning",
|
| 58 |
+
"research_section": "3. Spike sparsity & MAC->AC restructuring",
|
| 59 |
+
"reference": "u-Ticket: Workload-Balanced Pruning for Sparse SNNs, arXiv:2302.06746",
|
| 60 |
+
"script": "opt06_uticket_prune.py",
|
| 61 |
+
"baseline_mode": false,
|
| 62 |
+
"method": "harmonic_matched",
|
| 63 |
+
"accuracy": {
|
| 64 |
+
"test_acc": 0.9057142857142857,
|
| 65 |
+
"test_target_acc": 0.9057142857142857,
|
| 66 |
+
"best_val_acc": 0.9407142857142857
|
| 67 |
+
},
|
| 68 |
+
"efficiency": {
|
| 69 |
+
"spikes_per_inference": null,
|
| 70 |
+
"syn_ops_per_inference": 550376.6811079546,
|
| 71 |
+
"dense_macs_per_inference": 122675200.0,
|
| 72 |
+
"event_fraction_of_dense": 0.004486454320905567,
|
| 73 |
+
"syn_ops_per_inference_dense_fanout": 65058668.56331169,
|
| 74 |
+
"weight_sparsity": 0.9592550083472454,
|
| 75 |
+
"effective_ticks": 13.450714285714286,
|
| 76 |
+
"num_ticks": 100,
|
| 77 |
+
"membrane_bytes_per_inference": 451200,
|
| 78 |
+
"weight_bytes": 4907008,
|
| 79 |
+
"input_raster_bytes": null,
|
| 80 |
+
"train_ms_per_step": 94.40872569999783,
|
| 81 |
+
"infer_ms_per_sample": 0.6202037340624855,
|
| 82 |
+
"peak_vram_mb": 2837.544448,
|
| 83 |
+
"weight_bytes_nonzero": 160314
|
| 84 |
+
},
|
| 85 |
+
"optimization_specific": {
|
| 86 |
+
"pruning_enabled": true,
|
| 87 |
+
"balance_enabled": true,
|
| 88 |
+
"rounds": 3,
|
| 89 |
+
"final_sparsity": 0.9673295454545454,
|
| 90 |
+
"nnz_per_layer": {
|
| 91 |
+
"layer0": 2880,
|
| 92 |
+
"layer1": 20480,
|
| 93 |
+
"layer2": 16384
|
| 94 |
+
},
|
| 95 |
+
"pe_stats_per_layer": {
|
| 96 |
+
"layer0": {
|
| 97 |
+
"mean_nnz": 9.0,
|
| 98 |
+
"max_nnz": 9.0,
|
| 99 |
+
"min_nnz": 9.0,
|
| 100 |
+
"total_nnz": 2880,
|
| 101 |
+
"numel": 921600,
|
| 102 |
+
"sparsity": 0.996875,
|
| 103 |
+
"utilization": 1.0
|
| 104 |
+
},
|
| 105 |
+
"layer1": {
|
| 106 |
+
"mean_nnz": 40.0,
|
| 107 |
+
"max_nnz": 40.0,
|
| 108 |
+
"min_nnz": 40.0,
|
| 109 |
+
"total_nnz": 20480,
|
| 110 |
+
"numel": 163840,
|
| 111 |
+
"sparsity": 0.875,
|
| 112 |
+
"utilization": 1.0
|
| 113 |
+
},
|
| 114 |
+
"layer2": {
|
| 115 |
+
"mean_nnz": 64.0,
|
| 116 |
+
"max_nnz": 64.0,
|
| 117 |
+
"min_nnz": 64.0,
|
| 118 |
+
"total_nnz": 16384,
|
| 119 |
+
"numel": 131072,
|
| 120 |
+
"sparsity": 0.875,
|
| 121 |
+
"utilization": 1.0
|
| 122 |
+
}
|
| 123 |
+
},
|
| 124 |
+
"pe_utilization_final": 1.0,
|
| 125 |
+
"pe_utilization_lth": 0.623062015503876,
|
| 126 |
+
"pe_utilization_uticket": 1.0,
|
| 127 |
+
"latency_proxy_dense": 3712,
|
| 128 |
+
"latency_proxy_final": 113,
|
| 129 |
+
"latency_reduction_frac": 0.9695581896551724,
|
| 130 |
+
"rounds_history": [
|
| 131 |
+
{
|
| 132 |
+
"round": 1,
|
| 133 |
+
"layers": {
|
| 134 |
+
"layer0": {
|
| 135 |
+
"lth": {
|
| 136 |
+
"mean_nnz": 36.0,
|
| 137 |
+
"max_nnz": 49.0,
|
| 138 |
+
"min_nnz": 25.0,
|
| 139 |
+
"total_nnz": 11520,
|
| 140 |
+
"numel": 921600,
|
| 141 |
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"sparsity": 0.9875,
|
| 142 |
+
"utilization": 0.7346938775510204
|
| 143 |
+
},
|
| 144 |
+
"uticket": {
|
| 145 |
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"mean_nnz": 36.0,
|
| 146 |
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"max_nnz": 36.0,
|
| 147 |
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"min_nnz": 36.0,
|
| 148 |
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"total_nnz": 11520,
|
| 149 |
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"numel": 921600,
|
| 150 |
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"sparsity": 0.9875,
|
| 151 |
+
"utilization": 1.0
|
| 152 |
+
}
|
| 153 |
+
},
|
| 154 |
+
"layer1": {
|
| 155 |
+
"lth": {
|
| 156 |
+
"mean_nnz": 160.0,
|
| 157 |
+
"max_nnz": 190.0,
|
| 158 |
+
"min_nnz": 93.0,
|
| 159 |
+
"total_nnz": 81920,
|
| 160 |
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"numel": 163840,
|
| 161 |
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"sparsity": 0.5,
|
| 162 |
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"utilization": 0.8421052631578947
|
| 163 |
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},
|
| 164 |
+
"uticket": {
|
| 165 |
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"mean_nnz": 160.0,
|
| 166 |
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"max_nnz": 160.0,
|
| 167 |
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"min_nnz": 160.0,
|
| 168 |
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"total_nnz": 81920,
|
| 169 |
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"numel": 163840,
|
| 170 |
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"sparsity": 0.5,
|
| 171 |
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"utilization": 1.0
|
| 172 |
+
}
|
| 173 |
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},
|
| 174 |
+
"layer2": {
|
| 175 |
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"lth": {
|
| 176 |
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"mean_nnz": 256.0,
|
| 177 |
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"max_nnz": 297.0,
|
| 178 |
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"min_nnz": 193.0,
|
| 179 |
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"total_nnz": 65536,
|
| 180 |
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"numel": 131072,
|
| 181 |
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"sparsity": 0.5,
|
| 182 |
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"utilization": 0.8619528619528619
|
| 183 |
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},
|
| 184 |
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"uticket": {
|
| 185 |
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"mean_nnz": 256.0,
|
| 186 |
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"max_nnz": 256.0,
|
| 187 |
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"min_nnz": 256.0,
|
| 188 |
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"total_nnz": 65536,
|
| 189 |
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"numel": 131072,
|
| 190 |
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"sparsity": 0.5,
|
| 191 |
+
"utilization": 1.0
|
| 192 |
+
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