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Upload results/experiments.json with huggingface_hub

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results/experiments.json ADDED
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+ "experiment": "gnn-generation-analysis",
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+ "description": "Simple (MSE) loss, GAT decoder",
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+ "error": "SSH never became ready",
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+ "description": "Comprehensive (combined) loss, GAT decoder",
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+ "error": "setup:\n Optimizer: Adam (lr=0.001)\n Scheduler: ReduceLROnPlateau (patience=5)\n Loss function: Improved Reconstruction Loss\n AMP Enabled: True\n\n\ud83c\udfcb\ufe0f Starting training...\n==================================================\nTraceback (most recent call last):\n File \"/workspace/experiment/train_autoencoder.py\", line 393, in <module>\n main()\n File \"/workspace/experiment/train_autoencoder.py\", line 332, in main\n train_loss = train_epoch(model, train_loader, optimizer, device, args.type_weight, args.parent_weight, scaler, loss_fn=loss_fn)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/workspace/experiment/train_autoencoder.py\", line 65, in train_epoch\n loss = loss_fn(\n ^^^^^^^^\nTypeError: ast_reconstruction_loss_comprehensive() got an unexpected keyword argument 'type_weight'\nERROR: train_autoencoder.py exited with code 1\nMETRICS:{\"error\": \"training_failed\", \"exit_code\": 1}\n",
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+ "timestamp": "2026-04-13T06:43:41.645183+00:00"
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+ "experiment": "gnn-generation-analysis",
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+ "description": "Improved loss, GAT decoder, hidden_dim=512",
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+ "description": "Iterative predict\u2192refine, GAT decoder",
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+ "teacher-forced-sage": {
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+ "experiment": "gnn-decoder-topology",
412
+ "arm_name": "teacher-forced-sage",
413
+ "description": "Teacher-forced tree edges, SAGE decoder",
414
+ "instance_id": 34818986,
415
+ "gpu_info": "NVIDIA GeForce RTX 4090, 24564 MiB, 1, 16\n---\n total used free shared buff/cache available\nMem: 566Gi 25Gi 143Gi 171Mi 398Gi 534Gi\nModel name: AMD EPYC 7763 64-Core Processor",
416
+ "metrics": {
417
+ "syntactic_validity_pct": 0.0,
418
+ "val_loss": 7.7987,
419
+ "samples_evaluated": 100,
420
+ "valid_samples": 0,
421
+ "decoder_edge_mode": "teacher_forced",
422
+ "decoder_conv_type": "SAGE",
423
+ "hidden_dim": 256,
424
+ "num_layers": 5,
425
+ "loss_fn": "improved",
426
+ "type_weight": 2.0,
427
+ "parent_weight": 1.0,
428
+ "learning_rate": 0.001,
429
+ "epochs": 30
430
+ },
431
+ "exit_code": 0,
432
+ "error": "",
433
+ "duration_seconds": 85.2959101049928,
434
+ "timestamp": "2026-04-13T06:52:18.888290+00:00"
435
+ },
436
+ "teacher-forced-gin": {
437
+ "experiment": "gnn-decoder-topology",
438
+ "arm_name": "teacher-forced-gin",
439
+ "description": "Teacher-forced tree edges, GIN decoder",
440
+ "instance_id": 34818988,
441
+ "gpu_info": "NVIDIA GeForce RTX 4090, 24564 MiB, 1, 16\n---\n total used free shared buff/cache available\nMem: 503Gi 4.6Gi 109Gi 9.0Mi 389Gi 494Gi\nModel name: AMD Ryzen Threadripper PRO 3955WX 16-Cores",
442
+ "metrics": {
443
+ "syntactic_validity_pct": 7.0,
444
+ "val_loss": 8.3833,
445
+ "samples_evaluated": 100,
446
+ "valid_samples": 7,
447
+ "decoder_edge_mode": "teacher_forced",
448
+ "decoder_conv_type": "GIN",
449
+ "hidden_dim": 256,
450
+ "num_layers": 5,
451
+ "loss_fn": "improved",
452
+ "type_weight": 2.0,
453
+ "parent_weight": 1.0,
454
+ "learning_rate": 0.001,
455
+ "epochs": 30
456
+ },
457
+ "exit_code": 0,
458
+ "error": "",
459
+ "duration_seconds": 57.38957904101699,
460
+ "timestamp": "2026-04-13T06:52:18.888298+00:00"
461
+ },
462
+ "teacher-forced-gat-comprehensive": {
463
+ "experiment": "gnn-decoder-topology",
464
+ "arm_name": "teacher-forced-gat-comprehensive",
465
+ "description": "Teacher-forced, GAT, comprehensive loss",
466
+ "instance_id": 34818993,
467
+ "gpu_info": "NVIDIA GeForce RTX 4090, 24564 MiB, 1, 16\n---\n total used free shared buff/cache available\nMem: 503Gi 25Gi 5.7Gi 252Mi 472Gi 474Gi\nModel name: AMD EPYC 7K62 48-Core Processor",
468
+ "metrics": {
469
+ "error": "training_failed",
470
+ "exit_code": 1
471
+ },
472
+ "exit_code": 1,
473
+ "error": "setup:\n Optimizer: Adam (lr=0.001)\n Scheduler: ReduceLROnPlateau (patience=5)\n Loss function: Improved Reconstruction Loss\n AMP Enabled: True\n\n\ud83c\udfcb\ufe0f Starting training...\n==================================================\nTraceback (most recent call last):\n File \"/workspace/experiment/train_autoencoder.py\", line 393, in <module>\n main()\n File \"/workspace/experiment/train_autoencoder.py\", line 332, in main\n train_loss = train_epoch(model, train_loader, optimizer, device, args.type_weight, args.parent_weight, scaler, loss_fn=loss_fn)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/workspace/experiment/train_autoencoder.py\", line 65, in train_epoch\n loss = loss_fn(\n ^^^^^^^^\nTypeError: ast_reconstruction_loss_comprehensive() got an unexpected keyword argument 'type_weight'\nERROR: train_autoencoder.py exited with code 1\nMETRICS:{\"error\": \"training_failed\", \"exit_code\": 1}\n",
474
+ "duration_seconds": 21.593328246992314,
475
+ "timestamp": "2026-04-13T06:52:18.888305+00:00"
476
+ }
477
+ }
478
+ }