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syntheticSuccess/c2/tvae/tvae-c2-20260501_060652/_tvae_generate.py

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+import os, sys
+sys.path.insert(0, "/work")
+from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env
+apply_parallel_cap_from_env()
+from src.SpecificModels.ctgan_rdt_inverse_fix import apply_ctgan_inverse_fix
+apply_ctgan_inverse_fix()
+import pandas as pd
+from ctgan.synthesizers.tvae import TVAE
+os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8")
+os.environ.setdefault("OPENBLAS_NUM_THREADS", "8")
+os.environ.setdefault("MKL_NUM_THREADS", "8")
+model = TVAE.load("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260501_060652/models_300epochs/tvae_300epochs.pt")
+total = 1382
+chunk = min(50000, total) if total > 50000 else total
+parts = []
+left = total
+while left > 0:
+    take = min(chunk, left)
+    parts.append(model.sample(take))
+    left -= take
+samples = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0]
+samples.to_csv("/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260501_060652/tvae-c2-1382-20260501_060709.csv", index=False)
+print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260501_060652/tvae-c2-1382-20260501_060709.csv")

syntheticSuccess/c2/tvae/tvae-c2-20260501_060652/_tvae_train.py

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+import json, os, sys
+sys.path.insert(0, "/work")
+from src.SpecificModels.ctgan_joblib_parallel_cap import apply_parallel_cap_from_env
+apply_parallel_cap_from_env()
+import pandas as pd
+from ctgan.data import read_csv
+from ctgan.synthesizers.tvae import TVAE
+
+# Keep transform stage parallelism bounded for stability on shared host.
+os.environ.setdefault("LOKY_MAX_CPU_COUNT", "8")
+os.environ.setdefault("OPENBLAS_NUM_THREADS", "8")
+os.environ.setdefault("MKL_NUM_THREADS", "8")
+_nj = (os.environ.get("TVAE_CTGAN_JOBTRANS_N_JOBS") or "").strip()
+if _nj:
+    print("[TVAE] joblib Parallel cap ON, TVAE_CTGAN_JOBTRANS_N_JOBS=" + _nj)
+else:
+    print("[TVAE] joblib Parallel cap OFF (unset TVAE_CTGAN_JOBTRANS_N_JOBS)")
+print("[TVAE] LOKY_MAX_CPU_COUNT=" + str(os.environ.get("LOKY_MAX_CPU_COUNT", "")))
+
+csv_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260501_060652/staged/public/train.csv"
+meta_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260501_060652/tvae_metadata.json"
+save_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260501_060652/models_300epochs/tvae_300epochs.pt"
+epochs = 300
+
+data, discrete_columns = read_csv(csv_path, meta_path, header=True, discrete=None)
+print(f"[TVAE] Training on {len(data)} rows, {len(data.columns)} cols, epochs={epochs}")
+model = TVAE(epochs=epochs, batch_size=500)
+model.fit(data, discrete_columns)
+model.save(save_path)
+print(f"[TVAE] Model saved -> {save_path}")

syntheticSuccess/c2/tvae/tvae-c2-20260501_060652/gen_20260501_060709.log

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