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Prune redundant 5090 success runs with 6000 clean counterparts

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  1. 5090-Success/c10/forestdiffusion/forest-c10-20260419_200956/_fd_X_host.npy +0 -3
  2. 5090-Success/c10/forestdiffusion/forest-c10-20260419_200956/_fd_gen.py +0 -8
  3. 5090-Success/c10/forestdiffusion/forest-c10-20260419_200956/_fd_meta_host.json +0 -3
  4. 5090-Success/c10/forestdiffusion/forest-c10-20260419_200956/_fd_train.py +0 -20
  5. 5090-Success/c10/forestdiffusion/forest-c10-20260419_200956/forest-c10-820008-20260420_115742.csv +0 -3
  6. 5090-Success/c10/forestdiffusion/forest-c10-20260419_200956/forestdiffusion_model.joblib +0 -3
  7. 5090-Success/c10/forestdiffusion/forest-c10-20260419_200956/input_snapshot.json +0 -3
  8. 5090-Success/c10/forestdiffusion/forest-c10-20260419_200956/models_fd/model.joblib +0 -3
  9. 5090-Success/c10/forestdiffusion/forest-c10-20260419_200956/public_gate/normalized_schema_snapshot.json +0 -3
  10. 5090-Success/c10/forestdiffusion/forest-c10-20260419_200956/public_gate/public_gate_report.json +0 -3
  11. 5090-Success/c10/forestdiffusion/forest-c10-20260419_200956/public_gate/staged_input_manifest.json +0 -3
  12. 5090-Success/c10/forestdiffusion/forest-c10-20260419_200956/runtime_result.json +0 -3
  13. 5090-Success/c10/forestdiffusion/forest-c10-20260419_200956/staged/forestdiffusion/adapter_report.json +0 -3
  14. 5090-Success/c10/forestdiffusion/forest-c10-20260419_200956/staged/forestdiffusion/adapter_transforms_applied.json +0 -3
  15. 5090-Success/c10/forestdiffusion/forest-c10-20260419_200956/staged/forestdiffusion/model_input_manifest.json +0 -3
  16. 5090-Success/c10/forestdiffusion/forest-c10-20260419_200956/staged/public/staged_features.json +0 -3
  17. 5090-Success/c10/forestdiffusion/forest-c10-20260419_200956/staged/public/test.csv +0 -3
  18. 5090-Success/c10/forestdiffusion/forest-c10-20260419_200956/staged/public/train.csv +0 -3
  19. 5090-Success/c10/forestdiffusion/forest-c10-20260419_200956/staged/public/val.csv +0 -3
  20. 5090-Success/c10/forestdiffusion/forest-c10-20260419_200956/train_20260419_200958.log +0 -3
  21. 5090-Success/c10/realtabformer/rtf-c10-20260424_191110/gen_20260424_221406.log +0 -3
  22. 5090-Success/c10/realtabformer/rtf-c10-20260424_191110/input_snapshot.json +0 -3
  23. 5090-Success/c10/realtabformer/rtf-c10-20260424_191110/models_10epochs/id000017770400323585077248/rtf_config.json +0 -3
  24. 5090-Success/c10/realtabformer/rtf-c10-20260424_191110/models_10epochs/id000017770400323585077248/rtf_model.pt +0 -3
  25. 5090-Success/c10/realtabformer/rtf-c10-20260424_191110/public_gate/normalized_schema_snapshot.json +0 -3
  26. 5090-Success/c10/realtabformer/rtf-c10-20260424_191110/public_gate/public_gate_report.json +0 -3
  27. 5090-Success/c10/realtabformer/rtf-c10-20260424_191110/public_gate/staged_input_manifest.json +0 -3
  28. 5090-Success/c10/realtabformer/rtf-c10-20260424_191110/realtabformer_features.json +0 -3
  29. 5090-Success/c10/realtabformer/rtf-c10-20260424_191110/realtabformer_sanitized_info.json +0 -3
  30. 5090-Success/c10/realtabformer/rtf-c10-20260424_191110/realtabformer_train_sanitized.csv +0 -3
  31. 5090-Success/c10/realtabformer/rtf-c10-20260424_191110/rtf-c10-820008-20260424_221406.csv +0 -3
  32. 5090-Success/c10/realtabformer/rtf-c10-20260424_191110/rtf_checkpoints/checkpoint-255800/config.json +0 -3
  33. 5090-Success/c10/realtabformer/rtf-c10-20260424_191110/rtf_checkpoints/checkpoint-255800/generation_config.json +0 -3
  34. 5090-Success/c10/realtabformer/rtf-c10-20260424_191110/rtf_checkpoints/checkpoint-255800/model.safetensors +0 -3
  35. 5090-Success/c10/realtabformer/rtf-c10-20260424_191110/rtf_checkpoints/checkpoint-255800/optimizer.pt +0 -3
  36. 5090-Success/c10/realtabformer/rtf-c10-20260424_191110/rtf_checkpoints/checkpoint-255800/rng_state.pth +0 -3
  37. 5090-Success/c10/realtabformer/rtf-c10-20260424_191110/rtf_checkpoints/checkpoint-255800/scaler.pt +0 -3
  38. 5090-Success/c10/realtabformer/rtf-c10-20260424_191110/rtf_checkpoints/checkpoint-255800/scheduler.pt +0 -3
  39. 5090-Success/c10/realtabformer/rtf-c10-20260424_191110/rtf_checkpoints/checkpoint-255800/trainer_state.json +0 -3
  40. 5090-Success/c10/realtabformer/rtf-c10-20260424_191110/rtf_checkpoints/checkpoint-255800/training_args.bin +0 -3
  41. 5090-Success/c10/realtabformer/rtf-c10-20260424_191110/rtf_checkpoints/checkpoint-255900/config.json +0 -3
  42. 5090-Success/c10/realtabformer/rtf-c10-20260424_191110/rtf_checkpoints/checkpoint-255900/generation_config.json +0 -3
  43. 5090-Success/c10/realtabformer/rtf-c10-20260424_191110/rtf_checkpoints/checkpoint-255900/model.safetensors +0 -3
  44. 5090-Success/c10/realtabformer/rtf-c10-20260424_191110/rtf_checkpoints/checkpoint-255900/optimizer.pt +0 -3
  45. 5090-Success/c10/realtabformer/rtf-c10-20260424_191110/rtf_checkpoints/checkpoint-255900/rng_state.pth +0 -3
  46. 5090-Success/c10/realtabformer/rtf-c10-20260424_191110/rtf_checkpoints/checkpoint-255900/scaler.pt +0 -3
  47. 5090-Success/c10/realtabformer/rtf-c10-20260424_191110/rtf_checkpoints/checkpoint-255900/scheduler.pt +0 -3
  48. 5090-Success/c10/realtabformer/rtf-c10-20260424_191110/rtf_checkpoints/checkpoint-255900/trainer_state.json +0 -3
  49. 5090-Success/c10/realtabformer/rtf-c10-20260424_191110/rtf_checkpoints/checkpoint-255900/training_args.bin +0 -3
  50. 5090-Success/c10/realtabformer/rtf-c10-20260424_191110/rtf_checkpoints/checkpoint-256000/config.json +0 -3
5090-Success/c10/forestdiffusion/forest-c10-20260419_200956/_fd_X_host.npy DELETED
@@ -1,3 +0,0 @@
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- version https://git-lfs.github.com/spec/v1
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5090-Success/c10/forestdiffusion/forest-c10-20260419_200956/_fd_gen.py DELETED
@@ -1,8 +0,0 @@
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-
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- import joblib, pandas as pd
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- m, meta = joblib.load(r'/work/output-SpecializedModels/c10/forestdiffusion/forest-c10-20260419_200956/forestdiffusion_model.joblib')
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- # generate：batch_size 为样本数
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- arr = m.generate(batch_size=int(820008))
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- df = pd.DataFrame(arr, columns=meta["column_names"])
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- df.to_csv(r'/work/output-SpecializedModels/c10/forestdiffusion/forest-c10-20260419_200956/forest-c10-820008-20260420_115742.csv', index=False)
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- print("saved", len(df))
 
 
 
 
 
 
 
 
 
5090-Success/c10/forestdiffusion/forest-c10-20260419_200956/_fd_meta_host.json DELETED
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5090-Success/c10/forestdiffusion/forest-c10-20260419_200956/_fd_train.py DELETED
@@ -1,20 +0,0 @@
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-
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- import shutil, json
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- shutil.copy(r'/work/output-SpecializedModels/c10/forestdiffusion/forest-c10-20260419_200956/_fd_X_host.npy', '/tmp/fd_X.npy')
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- with open(r'/work/output-SpecializedModels/c10/forestdiffusion/forest-c10-20260419_200956/_fd_meta_host.json') as f:
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- open('/tmp/fd_meta.json','w').write(f.read())
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-
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- import numpy as np, joblib, json, os
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- from ForestDiffusion import ForestDiffusionModel
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- X = np.load("/tmp/fd_X.npy")
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- with open("/tmp/fd_meta.json") as f:
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- meta = json.load(f)
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- cat_indexes = meta["cat_indexes"]
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- m = ForestDiffusionModel(
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- X, n_t=20, n_estimators=100, duplicate_K=20, n_jobs=2,
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- model="xgboost", max_depth=6, tree_method="hist", cat_indexes=cat_indexes,
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- )
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- joblib.dump((m, meta), "/tmp/fd_model.joblib")
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- print("ForestDiffusion train OK")
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-
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- shutil.copy('/tmp/fd_model.joblib', r'/work/output-SpecializedModels/c10/forestdiffusion/forest-c10-20260419_200956/forestdiffusion_model.joblib')
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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