add run prune_readout
Browse files
applied/opt06_uticket_prune/prune_readout.summary.json
ADDED
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@@ -0,0 +1,684 @@
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| 1 |
+
{
|
| 2 |
+
"key": "opt06_uticket_prune/prune_readout",
|
| 3 |
+
"tag": "prune_readout",
|
| 4 |
+
"script": "opt06_uticket_prune.py",
|
| 5 |
+
"script_sha256_16": "13957bb36508b8cf",
|
| 6 |
+
"hf_folder": "applied/opt06_uticket_prune",
|
| 7 |
+
"priority": 2,
|
| 8 |
+
"note": "also prune the class readout",
|
| 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_readout",
|
| 35 |
+
"--keep-epoch-weights",
|
| 36 |
+
"1",
|
| 37 |
+
"--resplit",
|
| 38 |
+
"block",
|
| 39 |
+
"--drop-rest",
|
| 40 |
+
"--bench",
|
| 41 |
+
"--prune-readout"
|
| 42 |
+
],
|
| 43 |
+
"flags": [
|
| 44 |
+
"--prune-readout"
|
| 45 |
+
],
|
| 46 |
+
"epochs_requested": 60,
|
| 47 |
+
"gpus": 2,
|
| 48 |
+
"relative_epoch_cost": 1.0,
|
| 49 |
+
"exit_code": 0,
|
| 50 |
+
"status": "ok",
|
| 51 |
+
"duration_s": 582.6,
|
| 52 |
+
"started_utc": "2026-08-23T03:54:10.977939+00:00",
|
| 53 |
+
"finished_utc": "2026-08-23T04:03:53.533433+00:00",
|
| 54 |
+
"optimization_summary": {
|
| 55 |
+
"optimization": "uticket_workload_balanced_pruning",
|
| 56 |
+
"research_section": "3. Spike sparsity & MAC->AC restructuring",
|
| 57 |
+
"reference": "u-Ticket: Workload-Balanced Pruning for Sparse SNNs, arXiv:2302.06746",
|
| 58 |
+
"script": "opt06_uticket_prune.py",
|
| 59 |
+
"baseline_mode": false,
|
| 60 |
+
"method": "harmonic_matched",
|
| 61 |
+
"accuracy": {
|
| 62 |
+
"test_acc": 0.4135714285714286,
|
| 63 |
+
"test_target_acc": 0.4135714285714286,
|
| 64 |
+
"best_val_acc": 0.9335714285714286
|
| 65 |
+
},
|
| 66 |
+
"efficiency": {
|
| 67 |
+
"spikes_per_inference": null,
|
| 68 |
+
"syn_ops_per_inference": 104257.90097402596,
|
| 69 |
+
"dense_macs_per_inference": 122675200.0,
|
| 70 |
+
"event_fraction_of_dense": 0.0008498694191982239,
|
| 71 |
+
"syn_ops_per_inference_dense_fanout": 65062433.861201294,
|
| 72 |
+
"weight_sparsity": 0.9917057400354758,
|
| 73 |
+
"effective_ticks": 11.177142857142858,
|
| 74 |
+
"num_ticks": 100,
|
| 75 |
+
"membrane_bytes_per_inference": 451200,
|
| 76 |
+
"weight_bytes": 4907008,
|
| 77 |
+
"input_raster_bytes": null,
|
| 78 |
+
"train_ms_per_step": 94.6925303999933,
|
| 79 |
+
"infer_ms_per_sample": 0.6165744149999597,
|
| 80 |
+
"peak_vram_mb": 2836.776448,
|
| 81 |
+
"weight_bytes_nonzero": 40703
|
| 82 |
+
},
|
| 83 |
+
"optimization_specific": {
|
| 84 |
+
"pruning_enabled": true,
|
| 85 |
+
"balance_enabled": true,
|
| 86 |
+
"rounds": 5,
|
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