Resume SynthData0523 main/c2 batch 23

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SynthData0523/main/c2/tabsyn/tabsyn-c2-20260501_054336/staged/tabsyn/adapter_report.json

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+{
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+  "adapter_fail_reason_code": null,
+  "adapter_fail_detail": null,
+  "adapter_transforms_applied": [],
+  "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260501_054336/staged/tabsyn/model_input_manifest.json"
+}

SynthData0523/main/c2/tabsyn/tabsyn-c2-20260501_054336/staged/tabsyn/adapter_transforms_applied.json

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+[]

SynthData0523/main/c2/tabsyn/tabsyn-c2-20260501_054336/staged/tabsyn/model_input_manifest.json

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+{
+  "dataset_id": "c2",
+  "model": "tabsyn",
+  "target_column": "class",
+  "task_type": "classification",
+  "column_schema": [
+    {
+      "name": "buying",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
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+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.002894,
+        "example_values": [
+          "vhigh",
+          "med",
+          "high",
+          "low"
+        ]
+      }
+    },
+    {
+      "name": "maint",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
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+        "unique_ratio": 0.002894,
+        "example_values": [
+          "vhigh",
+          "low",
+          "med",
+          "high"
+        ]
+      }
+    },
+    {
+      "name": "doors",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.002894,
+        "example_values": [
+          "2",
+          "5more",
+          "3",
+          "4"
+        ]
+      }
+    },
+    {
+      "name": "persons",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
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+        "unique_ratio": 0.002171,
+        "example_values": [
+          "2",
+          "4",
+          "more"
+        ]
+      }
+    },
+    {
+      "name": "lug_boot",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
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+        "example_values": [
+          "small",
+          "big",
+          "med"
+        ]
+      }
+    },
+    {
+      "name": "safety",
+      "role": "feature",
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+      "nullable": false,
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+          "high",
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+    {
+      "name": "class",
+      "role": "target",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
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+      "impute_strategy": "mode",
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+        "unique_ratio": 0.002894,
+        "example_values": [
+          "unacc",
+          "good",
+          "acc",
+          "vgood"
+        ]
+      }
+    }
+  ],
+  "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260501_054336/public_gate/staged_input_manifest.json",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260501_054336/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260501_054336/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260501_054336/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260501_054336/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabsyn/tabsyn-c2-20260501_054336/public_gate/public_gate_report.json"
+}

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SynthData0523/main/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")

SynthData0523/main/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}")

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SynthData0523/main/c2/tvae/tvae-c2-20260504_173008/_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-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/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-c2-20260504_173008/tvae-c2-1382-20260504_173024.csv", index=False)
+print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/tvae-c2-1382-20260504_173024.csv")

SynthData0523/main/c2/tvae/tvae-c2-20260504_173008/_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-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/staged/public/train.csv"
+meta_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/tvae_metadata.json"
+save_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/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}")

SynthData0523/main/c2/tvae/tvae-c2-20260504_173008/gen_20260504_173024.log

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c250d16c78bb78776dfd39677e43fa301524a14766e56dfb776757716b41ad3c
+size 404

SynthData0523/main/c2/tvae/tvae-c2-20260504_173008/input_snapshot.json

@@ -0,0 +1,36 @@
+{
+  "dataset_id": "c2",
+  "model": "tvae",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv",
+      "exists": true,
+      "size": 42948,
+      "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv",
+      "exists": true,
+      "size": 5349,
+      "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv",
+      "exists": true,
+      "size": 5448,
+      "sha256": "cbcbb062a1faf5fa44b66c80532baa229e05b94fc42137269761e6c6d84af20a"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_profile.json",
+      "exists": true,
+      "size": 3240,
+      "sha256": "526b7163b2076c93c0bf4638438081ee8a6907065d5b608faa40d1a3dbc2a27b"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c2/c2-dataset_contract_v1.json",
+      "exists": true,
+      "size": 3731,
+      "sha256": "fb595a876054c2ee9b4e10cfe83a5691588de1d25466cbb9d473c18ad3604009"
+    }
+  }
+}

SynthData0523/main/c2/tvae/tvae-c2-20260504_173008/models_300epochs/train_20260504_173008.log

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+size 531

SynthData0523/main/c2/tvae/tvae-c2-20260504_173008/models_300epochs/tvae_300epochs.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:359d4ce6570b67da168f59f79a24d6afcc861aaf7416c29c0ad54e031827ea1b
+size 344876

SynthData0523/main/c2/tvae/tvae-c2-20260504_173008/public_gate/normalized_schema_snapshot.json

@@ -0,0 +1,144 @@
+{
+  "dataset_id": "c2",
+  "target_column": "class",
+  "task_type": "classification",
+  "columns": [
+    {
+      "name": "buying",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.002894,
+        "example_values": [
+          "vhigh",
+          "med",
+          "high",
+          "low"
+        ]
+      }
+    },
+    {
+      "name": "maint",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.002894,
+        "example_values": [
+          "vhigh",
+          "low",
+          "med",
+          "high"
+        ]
+      }
+    },
+    {
+      "name": "doors",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.002894,
+        "example_values": [
+          "2",
+          "5more",
+          "3",
+          "4"
+        ]
+      }
+    },
+    {
+      "name": "persons",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 0.002171,
+        "example_values": [
+          "2",
+          "4",
+          "more"
+        ]
+      }
+    },
+    {
+      "name": "lug_boot",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 0.002171,
+        "example_values": [
+          "small",
+          "big",
+          "med"
+        ]
+      }
+    },
+    {
+      "name": "safety",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 0.002171,
+        "example_values": [
+          "low",
+          "high",
+          "med"
+        ]
+      }
+    },
+    {
+      "name": "class",
+      "role": "target",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
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+        "unique_ratio": 0.002894,
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+          "unacc",
+          "good",
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+        ]
+      }
+    }
+  ]
+}

SynthData0523/main/c2/tvae/tvae-c2-20260504_173008/public_gate/public_gate_report.json

@@ -0,0 +1,37 @@
+{
+  "dataset_id": "c2",
+  "status": "pass",
+  "checks": [
+    {
+      "check_id": "PG001_csv_parse_ok",
+      "status": "pass"
+    },
+    {
+      "check_id": "PG002_split_header_consistent",
+      "status": "pass"
+    },
+    {
+      "check_id": "PG003_profile_header_match",
+      "status": "pass"
+    },
+    {
+      "check_id": "PG004_missing_token_normalized",
+      "status": "pass"
+    },
+    {
+      "check_id": "PG005_semantic_type_validated",
+      "status": "pass"
+    },
+    {
+      "check_id": "PG006_target_defined_and_valid",
+      "status": "pass"
+    }
+  ],
+  "target_column": "class",
+  "task_type": "classification",
+  "input_splits": {
+    "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv",
+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-test.csv"
+  }
+}

SynthData0523/main/c2/tvae/tvae-c2-20260504_173008/public_gate/staged_input_manifest.json

@@ -0,0 +1,149 @@
+{
+  "dataset_id": "c2",
+  "target_column": "class",
+  "task_type": "classification",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/public_gate/public_gate_report.json",
+  "column_schema": [
+    {
+      "name": "buying",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.002894,
+        "example_values": [
+          "vhigh",
+          "med",
+          "high",
+          "low"
+        ]
+      }
+    },
+    {
+      "name": "maint",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.002894,
+        "example_values": [
+          "vhigh",
+          "low",
+          "med",
+          "high"
+        ]
+      }
+    },
+    {
+      "name": "doors",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
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+        "unique_ratio": 0.002894,
+        "example_values": [
+          "2",
+          "5more",
+          "3",
+          "4"
+        ]
+      }
+    },
+    {
+      "name": "persons",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
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+      "impute_strategy": "mode",
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+        "example_values": [
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+        ]
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+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 0.002171,
+        "example_values": [
+          "small",
+          "big",
+          "med"
+        ]
+      }
+    },
+    {
+      "name": "safety",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 0.002171,
+        "example_values": [
+          "low",
+          "high",
+          "med"
+        ]
+      }
+    },
+    {
+      "name": "class",
+      "role": "target",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.002894,
+        "example_values": [
+          "unacc",
+          "good",
+          "acc",
+          "vgood"
+        ]
+      }
+    }
+  ]
+}

SynthData0523/main/c2/tvae/tvae-c2-20260504_173008/run_config.json

@@ -0,0 +1,40 @@
+{
+  "schema_version": 1,
+  "recorded_at": "2026-05-04T17:30:08",
+  "dataset_id": "c2",
+  "model": "tvae",
+  "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008",
+  "dataset_source_requested": "new",
+  "dataset_source_resolved": "new",
+  "cli_args": {
+    "model": "tvae",
+    "dataset": "c2",
+    "dataset_source": "new",
+    "train": true,
+    "generate": true,
+    "num_rows": 0,
+    "epochs": null,
+    "output_dir": null,
+    "model_dir": null,
+    "work_dir": null,
+    "resume": false,
+    "no_stats": false
+  },
+  "resolved": {
+    "num_rows": 1382,
+    "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/models_300epochs/tvae_300epochs.pt",
+    "output_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/tvae-c2-1382-20260504_173024.csv"
+  },
+  "input_artifacts": {
+    "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/public_gate/public_gate_report.json",
+    "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/public_gate/staged_input_manifest.json",
+    "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/staged/tvae/model_input_manifest.json",
+    "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/staged/public/train.csv",
+    "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/staged/public/staged_features.json",
+    "target_column": "class",
+    "task_type": "classification"
+  },
+  "env_overrides": {
+    "BENCHMARK_TVAE_GPUS": "device=3"
+  }
+}

SynthData0523/main/c2/tvae/tvae-c2-20260504_173008/runtime_result.json

@@ -0,0 +1,27 @@
+{
+  "dataset_id": "c2",
+  "model": "tvae",
+  "run_id": "tvae-c2-20260504_173008",
+  "public_gate_status": "pass",
+  "adapter_ready_status": "pass",
+  "train_status": "success",
+  "generate_status": "success",
+  "reason_code": null,
+  "reason_detail": null,
+  "artifacts": {
+    "synthetic_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/tvae-c2-1382-20260504_173024.csv",
+    "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/models_300epochs/tvae_300epochs.pt"
+  },
+  "timings": {
+    "train": {
+      "started_at": "2026-05-04T17:30:08",
+      "ended_at": "2026-05-04T17:30:24",
+      "duration_sec": 15.956
+    },
+    "generate": {
+      "started_at": "2026-05-04T17:30:24",
+      "ended_at": "2026-05-04T17:30:30",
+      "duration_sec": 5.259
+    }
+  }
+}

SynthData0523/main/c2/tvae/tvae-c2-20260504_173008/staged/public/staged_features.json

@@ -0,0 +1,37 @@
+[
+  {
+    "feature_name": "buying",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "maint",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "doors",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "persons",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "lug_boot",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "safety",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "class",
+    "data_type": "categorical",
+    "is_target": true
+  }
+]

SynthData0523/main/c2/tvae/tvae-c2-20260504_173008/staged/public/test.csv

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+size 41565

SynthData0523/main/c2/tvae/tvae-c2-20260504_173008/staged/public/val.csv

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+size 5176

SynthData0523/main/c2/tvae/tvae-c2-20260504_173008/staged/tvae/adapter_report.json

@@ -0,0 +1,7 @@
+{
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+  "adapter_fail_reason_code": null,
+  "adapter_fail_detail": null,
+  "adapter_transforms_applied": [],
+  "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/staged/tvae/model_input_manifest.json"
+}

SynthData0523/main/c2/tvae/tvae-c2-20260504_173008/staged/tvae/adapter_transforms_applied.json

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+[]

SynthData0523/main/c2/tvae/tvae-c2-20260504_173008/staged/tvae/model_input_manifest.json

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+{
+  "dataset_id": "c2",
+  "model": "tvae",
+  "target_column": "class",
+  "task_type": "classification",
+  "column_schema": [
+    {
+      "name": "buying",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.002894,
+        "example_values": [
+          "vhigh",
+          "med",
+          "high",
+          "low"
+        ]
+      }
+    },
+    {
+      "name": "maint",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.002894,
+        "example_values": [
+          "vhigh",
+          "low",
+          "med",
+          "high"
+        ]
+      }
+    },
+    {
+      "name": "doors",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.002894,
+        "example_values": [
+          "2",
+          "5more",
+          "3",
+          "4"
+        ]
+      }
+    },
+    {
+      "name": "persons",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 0.002171,
+        "example_values": [
+          "2",
+          "4",
+          "more"
+        ]
+      }
+    },
+    {
+      "name": "lug_boot",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 0.002171,
+        "example_values": [
+          "small",
+          "big",
+          "med"
+        ]
+      }
+    },
+    {
+      "name": "safety",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 3,
+        "unique_ratio": 0.002171,
+        "example_values": [
+          "low",
+          "high",
+          "med"
+        ]
+      }
+    },
+    {
+      "name": "class",
+      "role": "target",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 4,
+        "unique_ratio": 0.002894,
+        "example_values": [
+          "unacc",
+          "good",
+          "acc",
+          "vgood"
+        ]
+      }
+    }
+  ],
+  "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/public_gate/staged_input_manifest.json",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_173008/public_gate/public_gate_report.json"
+}

SynthData0523/main/c2/tvae/tvae-c2-20260504_173008/tvae-c2-1382-20260504_173024.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5c13329eb5fa57144da4e37e2c72a488103b21bb242e3711339b259178094e71
+size 41572

SynthData0523/main/c2/tvae/tvae-c2-20260504_173008/tvae_metadata.json

@@ -0,0 +1,32 @@
+{
+  "columns": [
+    {
+      "name": "buying",
+      "type": "categorical"
+    },
+    {
+      "name": "maint",
+      "type": "categorical"
+    },
+    {
+      "name": "doors",
+      "type": "categorical"
+    },
+    {
+      "name": "persons",
+      "type": "categorical"
+    },
+    {
+      "name": "lug_boot",
+      "type": "categorical"
+    },
+    {
+      "name": "safety",
+      "type": "categorical"
+    },
+    {
+      "name": "class",
+      "type": "categorical"
+    }
+  ]
+}

SynthData0523/main/c2/tvae/tvae-c2-20260504_174013/_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-c2-20260504_174013/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-c2-20260504_174013/tvae-c2-1382-20260504_174029.csv", index=False)
+print(f"[TVAE] Generated {total} rows (chunks={len(parts)}) -> /work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174013/tvae-c2-1382-20260504_174029.csv")

SynthData0523/main/c2/tvae/tvae-c2-20260504_174013/_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-c2-20260504_174013/staged/public/train.csv"
+meta_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174013/tvae_metadata.json"
+save_path = "/work/output-Benchmark-trainonly-v1/c2/tvae/tvae-c2-20260504_174013/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))
+
+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(data, discrete_columns)
+model.save(save_path)
+print(f"[TVAE] Model saved -> {save_path}")