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diff --git a/SynthData0523/main/c13/tvae/tvae-c13-20260425_212442/_tvae_generate.py b/SynthData0523/main/c13/tvae/tvae-c13-20260425_212442/_tvae_generate.py
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index 0000000000000000000000000000000000000000..48d859bae9df7237e7f3d823fa735e36b372de06
--- /dev/null
+++ b/SynthData0523/main/c13/tvae/tvae-c13-20260425_212442/_tvae_generate.py
@@ -0,0 +1,23 @@
+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-SpecializedModels/c13/tvae/tvae-c13-20260425_212442/models_50epochs/tvae_50epochs.pt")
+total = 1966628
+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-SpecializedModels/c13/tvae/tvae-c13-20260425_212442/tvae-c13-1966628-20260426_000517.csv", index=False)
+print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-SpecializedModels/c13/tvae/tvae-c13-20260425_212442/tvae-c13-1966628-20260426_000517.csv")
diff --git a/SynthData0523/main/c13/tvae/tvae-c13-20260425_212442/_tvae_train.py b/SynthData0523/main/c13/tvae/tvae-c13-20260425_212442/_tvae_train.py
new file mode 100644
index 0000000000000000000000000000000000000000..02951c702f38e2e292bf3f5c1c662164d69057e7
--- /dev/null
+++ b/SynthData0523/main/c13/tvae/tvae-c13-20260425_212442/_tvae_train.py
@@ -0,0 +1,30 @@
+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-SpecializedModels/c13/tvae/tvae-c13-20260425_212442/staged/public/train.csv"
+meta_path = "/work/output-SpecializedModels/c13/tvae/tvae-c13-20260425_212442/tvae_metadata.json"
+save_path = "/work/output-SpecializedModels/c13/tvae/tvae-c13-20260425_212442/models_50epochs/tvae_50epochs.pt"
+epochs = 50
+
+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}")
diff --git a/SynthData0523/main/c13/tvae/tvae-c13-20260425_212442/gen_20260426_000517.log b/SynthData0523/main/c13/tvae/tvae-c13-20260425_212442/gen_20260426_000517.log
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