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syntheticSuccess/c18/tvae/tvae-c18-20260511_061930/_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
+def _fit_cpu_staged_compat(self, *args, **kwargs):
+    raise RuntimeError("_fit_cpu_staged_compat should never be called during generate-only load")
+if not hasattr(TVAE, "_fit_cpu_staged"):
+    TVAE._fit_cpu_staged = _fit_cpu_staged_compat
+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/c18/tvae/tvae-c18-20260511_061930/models_300epochs/tvae_300epochs.pt")
+total = 103976
+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/c18/tvae/tvae-c18-20260511_061930/tvae-c18-103976-20260511_174828.csv", index=False)
+print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/c18/tvae/tvae-c18-20260511_061930/tvae-c18-103976-20260511_174828.csv")

syntheticSuccess/c18/tvae/tvae-c18-20260511_061930/_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
+import torch
+from torch.optim import Adam
+from torch.utils.data import DataLoader, TensorDataset
+from tqdm import tqdm
+from ctgan.data import read_csv
+from ctgan.data_transformer import DataTransformer
+from ctgan.synthesizers.tvae import TVAE, Encoder, Decoder, _loss_function
+
+# 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/c18/tvae/tvae-c18-20260511_061930/staged/public/train.csv"
+meta_path = "/work/output-Benchmark-trainonly-v1/c18/tvae/tvae-c18-20260511_061930/tvae_metadata.json"
+save_path = "/work/output-Benchmark-trainonly-v1/c18/tvae/tvae-c18-20260511_061930/models_300epochs/tvae_300epochs.pt"
+epochs = int(os.environ.get("TVAE_EPOCHS", 300))
+batch_size = int(os.environ.get("TVAE_BATCH_SIZE", "500"))
+embedding_dim = int(os.environ.get("TVAE_EMBEDDING_DIM", "128"))
+def _parse_dims(name, default):
+    raw = (os.environ.get(name) or "").strip()
+    if not raw:
+        return default
+    return tuple(int(x.strip()) for x in raw.split(",") if x.strip())
+compress_dims = _parse_dims("TVAE_COMPRESS_DIMS", (128, 128))
+decompress_dims = _parse_dims("TVAE_DECOMPRESS_DIMS", (128, 128))
+
+def _fit_cpu_staged(self, train_data, discrete_columns=()):
+    self.transformer = DataTransformer()
+    self.transformer.fit(train_data, discrete_columns)
+    train_data = self.transformer.transform(train_data)
+    pin_memory = getattr(self._device, "type", str(self._device)) == "cuda"
+    dataset = TensorDataset(torch.from_numpy(train_data.astype("float32")))
+    loader = DataLoader(
+        dataset,
+        batch_size=self.batch_size,
+        shuffle=True,
+        drop_last=False,
+        pin_memory=pin_memory,
+    )
+
+    data_dim = self.transformer.output_dimensions
+    encoder = Encoder(data_dim, self.compress_dims, self.embedding_dim).to(self._device)
+    self.decoder = Decoder(self.embedding_dim, self.decompress_dims, data_dim).to(self._device)
+    optimizerAE = Adam(
+        list(encoder.parameters()) + list(self.decoder.parameters()),
+        weight_decay=self.l2scale,
+    )
+
+    self.loss_values = pd.DataFrame(columns=["Epoch", "Batch", "Loss"])
+    iterator = tqdm(range(self.epochs), disable=(not self.verbose))
+    if self.verbose:
+        iterator_description = "Loss: {loss:.3f}"
+        iterator.set_description(iterator_description.format(loss=0))
+
+    for i in iterator:
+        loss_values = []
+        batch = []
+        for id_, data in enumerate(loader):
+            optimizerAE.zero_grad()
+            real = data[0].to(self._device, non_blocking=pin_memory)
+            mu, std, logvar = encoder(real)
+            eps = torch.randn_like(std)
+            emb = eps * std + mu
+            rec, sigmas = self.decoder(emb)
+            loss_1, loss_2 = _loss_function(
+                rec,
+                real,
+                sigmas,
+                mu,
+                logvar,
+                self.transformer.output_info_list,
+                self.loss_factor,
+            )
+            loss = loss_1 + loss_2
+            loss.backward()
+            optimizerAE.step()
+            self.decoder.sigma.data.clamp_(0.01, 1.0)
+
+            batch.append(id_)
+            loss_values.append(loss.detach().cpu().item())
+
+        epoch_loss_df = pd.DataFrame({
+            "Epoch": [i] * len(batch),
+            "Batch": batch,
+            "Loss": loss_values,
+        })
+        if not self.loss_values.empty:
+            self.loss_values = pd.concat(
+                [self.loss_values, epoch_loss_df]
+            ).reset_index(drop=True)
+        else:
+            self.loss_values = epoch_loss_df
+
+        if self.verbose:
+            iterator.set_description(
+                iterator_description.format(loss=loss.detach().cpu().item())
+            )
+
+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}, batch_size={batch_size}, embedding_dim={embedding_dim}, compress_dims={compress_dims}, decompress_dims={decompress_dims}")
+model = TVAE(epochs=epochs, batch_size=batch_size, embedding_dim=embedding_dim, compress_dims=compress_dims, decompress_dims=decompress_dims)
+model.fit = _fit_cpu_staged.__get__(model, TVAE)
+model.fit(data, discrete_columns)
+model.save(save_path)
+print(f"[TVAE] Model saved -> {save_path}")

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