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Resume SynthData0523 main/c2 batch 26

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  1. .gitattributes +17 -0
  2. SynthData0523/main/c2/tvae/tvae-c2-20260504_175027/input_snapshot.json +36 -0
  3. SynthData0523/main/c2/tvae/tvae-c2-20260504_175027/models_300epochs/train_20260504_175027.log +3 -0
  4. SynthData0523/main/c2/tvae/tvae-c2-20260504_175027/models_300epochs/tvae_300epochs.pt +3 -0
  5. SynthData0523/main/c2/tvae/tvae-c2-20260504_175027/public_gate/normalized_schema_snapshot.json +144 -0
  6. SynthData0523/main/c2/tvae/tvae-c2-20260504_175027/public_gate/public_gate_report.json +37 -0
  7. SynthData0523/main/c2/tvae/tvae-c2-20260504_175027/public_gate/staged_input_manifest.json +149 -0
  8. SynthData0523/main/c2/tvae/tvae-c2-20260504_175027/run_config.json +45 -0
  9. SynthData0523/main/c2/tvae/tvae-c2-20260504_175027/runtime_result.json +27 -0
  10. SynthData0523/main/c2/tvae/tvae-c2-20260504_175027/staged/public/staged_features.json +37 -0
  11. SynthData0523/main/c2/tvae/tvae-c2-20260504_175027/staged/public/test.csv +3 -0
  12. SynthData0523/main/c2/tvae/tvae-c2-20260504_175027/staged/public/train.csv +3 -0
  13. SynthData0523/main/c2/tvae/tvae-c2-20260504_175027/staged/public/val.csv +3 -0
  14. SynthData0523/main/c2/tvae/tvae-c2-20260504_175027/staged/tvae/adapter_report.json +7 -0
  15. SynthData0523/main/c2/tvae/tvae-c2-20260504_175027/staged/tvae/adapter_transforms_applied.json +1 -0
  16. SynthData0523/main/c2/tvae/tvae-c2-20260504_175027/staged/tvae/model_input_manifest.json +151 -0
  17. SynthData0523/main/c2/tvae/tvae-c2-20260504_175027/tvae-c2-1382-20260504_175041.csv +3 -0
  18. SynthData0523/main/c2/tvae/tvae-c2-20260504_175027/tvae_metadata.json +32 -0
  19. SynthData0523/main/c2/tvae/tvae-c2-20260504_175059/_tvae_generate.py +23 -0
  20. SynthData0523/main/c2/tvae/tvae-c2-20260504_175059/_tvae_train.py +39 -0
  21. SynthData0523/main/c2/tvae/tvae-c2-20260504_175059/gen_20260504_175116.log +3 -0
  22. SynthData0523/main/c2/tvae/tvae-c2-20260504_175059/input_snapshot.json +36 -0
  23. SynthData0523/main/c2/tvae/tvae-c2-20260504_175059/models_300epochs/train_20260504_175100.log +3 -0
  24. SynthData0523/main/c2/tvae/tvae-c2-20260504_175059/models_300epochs/tvae_300epochs.pt +3 -0
  25. SynthData0523/main/c2/tvae/tvae-c2-20260504_175059/public_gate/normalized_schema_snapshot.json +144 -0
  26. SynthData0523/main/c2/tvae/tvae-c2-20260504_175059/public_gate/public_gate_report.json +37 -0
  27. SynthData0523/main/c2/tvae/tvae-c2-20260504_175059/public_gate/staged_input_manifest.json +149 -0
  28. SynthData0523/main/c2/tvae/tvae-c2-20260504_175059/run_config.json +45 -0
  29. SynthData0523/main/c2/tvae/tvae-c2-20260504_175059/runtime_result.json +27 -0
  30. SynthData0523/main/c2/tvae/tvae-c2-20260504_175059/staged/public/staged_features.json +37 -0
  31. SynthData0523/main/c2/tvae/tvae-c2-20260504_175059/staged/public/test.csv +3 -0
  32. SynthData0523/main/c2/tvae/tvae-c2-20260504_175059/staged/public/train.csv +3 -0
  33. SynthData0523/main/c2/tvae/tvae-c2-20260504_175059/staged/public/val.csv +3 -0
  34. SynthData0523/main/c2/tvae/tvae-c2-20260504_175059/staged/tvae/adapter_report.json +7 -0
  35. SynthData0523/main/c2/tvae/tvae-c2-20260504_175059/staged/tvae/adapter_transforms_applied.json +1 -0
  36. SynthData0523/main/c2/tvae/tvae-c2-20260504_175059/staged/tvae/model_input_manifest.json +151 -0
  37. SynthData0523/main/c2/tvae/tvae-c2-20260504_175059/tvae-c2-1382-20260504_175116.csv +3 -0
  38. SynthData0523/main/c2/tvae/tvae-c2-20260504_175059/tvae_metadata.json +32 -0
  39. SynthData0523/main/c2/tvae/tvae-c2-20260504_175134/_tvae_generate.py +23 -0
  40. SynthData0523/main/c2/tvae/tvae-c2-20260504_175134/_tvae_train.py +39 -0
  41. SynthData0523/main/c2/tvae/tvae-c2-20260504_175134/gen_20260504_175151.log +3 -0
  42. SynthData0523/main/c2/tvae/tvae-c2-20260504_175134/input_snapshot.json +36 -0
  43. SynthData0523/main/c2/tvae/tvae-c2-20260504_175134/models_300epochs/train_20260504_175134.log +3 -0
  44. SynthData0523/main/c2/tvae/tvae-c2-20260504_175134/models_300epochs/tvae_300epochs.pt +3 -0
  45. SynthData0523/main/c2/tvae/tvae-c2-20260504_175134/public_gate/normalized_schema_snapshot.json +144 -0
  46. SynthData0523/main/c2/tvae/tvae-c2-20260504_175134/public_gate/public_gate_report.json +37 -0
  47. SynthData0523/main/c2/tvae/tvae-c2-20260504_175134/public_gate/staged_input_manifest.json +149 -0
  48. SynthData0523/main/c2/tvae/tvae-c2-20260504_175134/run_config.json +45 -0
  49. SynthData0523/main/c2/tvae/tvae-c2-20260504_175134/runtime_result.json +27 -0
  50. SynthData0523/main/c2/tvae/tvae-c2-20260504_175134/staged/public/staged_features.json +37 -0
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SynthData0523/main/c2/tvae/tvae-c2-20260504_175059/_tvae_generate.py ADDED
@@ -0,0 +1,23 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os, sys
2
+ sys.path.insert(0, "/work")
3
+ from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env
4
+ apply_parallel_cap_from_env()
5
+ from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix
6
+ apply_ctgan_inverse_fix()
7
+ import pandas as pd
8
+ from ctgan.synthesizers.tvae import TVAE
9
+ os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8")
10
+ os.environ.setdefault("OPENBLAS_NUM_THREADS", "8")
11
+ os.environ.setdefault("MKL_NUM_THREADS", "8")
12
+ model = TVAE.load("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/models_300epochs/tvae_300epochs.pt")
13
+ total = 1382
14
+ chunk = min(50000, total) if total > 50000 else total
15
+ parts = []
16
+ left = total
17
+ while left > 0:
18
+ take = min(chunk, left)
19
+ parts.append(model.sample(take))
20
+ left -= take
21
+ samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0]
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23
+ print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/tvae-c2-1382-20260504_175116.csv")
SynthData0523/main/c2/tvae/tvae-c2-20260504_175059/_tvae_train.py ADDED
@@ -0,0 +1,39 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import json, os, sys
2
+ sys.path.insert(0, "/work")
3
+ from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env
4
+ apply_parallel_cap_from_env()
5
+ import pandas as pd
6
+ from ctgan.data import read_csv
7
+ from ctgan.synthesizers.tvae import TVAE
8
+
9
+ # Keep transform stage parallelism bounded for stability on shared host.
10
+ os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8")
11
+ os.environ.setdefault("OPENBLAS_NUM_THREADS", "8")
12
+ os.environ.setdefault("MKL_NUM_THREADS", "8")
13
+ _nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip()
14
+ if _nj:
15
+ print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj)
16
+ else:
17
+ print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)")
18
+ print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", "")))
19
+
20
+ csv_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/staged/public/train.csv"
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+ meta_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/tvae_metadata.json"
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+ save_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175059/models_300epochs/tvae_300epochs.pt"
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+ epochs = int(os.environ.get("TVAE_EPOCHS", 300))
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+ embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128"))
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+ def _parse_dims(name, default):
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+ raw = (os.environ.get(name) or "").strip()
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+ if not raw:
29
+ return default
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+ return tuple(int(x.strip()) for x in raw.split(",") if x.strip())
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32
+ decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128))
33
+
34
+ data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None)
35
+ print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}")
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+ model.fit(data, discrete_columns)
38
+ model.save(save_path)
39
+ print(f"[TVAE] Model saved -> {save_path}")
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SynthData0523/main/c2/tvae/tvae-c2-20260504_175134/_tvae_generate.py ADDED
@@ -0,0 +1,23 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os, sys
2
+ sys.path.insert(0, "/work")
3
+ from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env
4
+ apply_parallel_cap_from_env()
5
+ from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix
6
+ apply_ctgan_inverse_fix()
7
+ import pandas as pd
8
+ from ctgan.synthesizers.tvae import TVAE
9
+ os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8")
10
+ os.environ.setdefault("OPENBLAS_NUM_THREADS", "8")
11
+ os.environ.setdefault("MKL_NUM_THREADS", "8")
12
+ model = TVAE.load("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175134/models_300epochs/tvae_300epochs.pt")
13
+ total = 1382
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+ chunk = min(50000, total) if total > 50000 else total
15
+ parts = []
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+ left = total
17
+ while left > 0:
18
+ take = min(chunk, left)
19
+ parts.append(model.sample(take))
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+ left -= take
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+ samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0]
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23
+ print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175134/tvae-c2-1382-20260504_175151.csv")
SynthData0523/main/c2/tvae/tvae-c2-20260504_175134/_tvae_train.py ADDED
@@ -0,0 +1,39 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import json, os, sys
2
+ sys.path.insert(0, "/work")
3
+ from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env
4
+ apply_parallel_cap_from_env()
5
+ import pandas as pd
6
+ from ctgan.data import read_csv
7
+ from ctgan.synthesizers.tvae import TVAE
8
+
9
+ # Keep transform stage parallelism bounded for stability on shared host.
10
+ os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8")
11
+ os.environ.setdefault("OPENBLAS_NUM_THREADS", "8")
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+ os.environ.setdefault("MKL_NUM_THREADS", "8")
13
+ _nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip()
14
+ if _nj:
15
+ print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj)
16
+ else:
17
+ print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)")
18
+ print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", "")))
19
+
20
+ csv_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175134/staged/public/train.csv"
21
+ meta_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175134/tvae_metadata.json"
22
+ save_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_175134/models_300epochs/tvae_300epochs.pt"
23
+ epochs = int(os.environ.get("TVAE_EPOCHS", 300))
24
+ batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500"))
25
+ embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128"))
26
+ def _parse_dims(name, default):
27
+ raw = (os.environ.get(name) or "").strip()
28
+ if not raw:
29
+ return default
30
+ return tuple(int(x.strip()) for x in raw.split(",") if x.strip())
31
+ compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128))
32
+ decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128))
33
+
34
+ data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None)
35
+ print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}")
36
+ model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims)
37
+ model.fit(data, discrete_columns)
38
+ model.save(save_path)
39
+ print(f"[TVAE] Model saved -> {save_path}")
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