Add syntheticFail m4

syntheticFail/m4/tabbyflow/tabbyflow-m4-20260501_005424/_tabbyflow_train.py

@@ -0,0 +1,22 @@
+
+import os, shutil, subprocess, sys
+root = r"/workspace/ef-vfm"
+name = r"pipeline_m4"
+src = r"/work/output-Benchmark-trainonly-v1/m4/tabbyflow/tabbyflow-m4-20260501_005424/tabular_bundle/pipeline_m4"
+os.makedirs(os.path.join(root, "data", name), exist_ok=True)
+dst_data = os.path.join(root, "data", name)
+dst_syn = os.path.join(root, "synthetic", name)
+shutil.rmtree(dst_data, ignore_errors=True)
+shutil.copytree(src, dst_data)
+os.makedirs(dst_syn, exist_ok=True)
+for fn in ("real.csv", "test.csv", "val.csv"):
+    shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn))
+os.chdir(root)
+os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "")
+os.environ["EFVFM_SMOKE_STEPS"] = "500"
+os.environ["EFVFM_ADAPTER_TRAIN"] = "1"
+subprocess.check_call([
+    sys.executable, "main.py",
+    "--dataname", name, "--mode", "train", "--gpu", "0",
+    "--no_wandb", "--exp_name", r"adapter_efvfm",
+])

syntheticFail/m4/tabbyflow/tabbyflow-m4-20260501_005424/input_snapshot.json

@@ -0,0 +1,36 @@
+{
+  "dataset_id": "m4",
+  "model": "tabbyflow",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv",
+      "exists": true,
+      "size": 92191,
+      "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1"
+    },
+    "val_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv",
+      "exists": true,
+      "size": 11482,
+      "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv",
+      "exists": true,
+      "size": 11559,
+      "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_profile.json",
+      "exists": true,
+      "size": 3336,
+      "sha256": "83e2764810e4a0e8cdece3a28dbd9134b7c9df6f2e56953e46d024ad2c4e035f"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_contract_v1.json",
+      "exists": true,
+      "size": 3810,
+      "sha256": "c23641b258629a845b164099bd0132886f8f6d0100e990494e0f92540f8987d9"
+    }
+  }
+}

syntheticFail/m4/tabbyflow/tabbyflow-m4-20260501_005424/public_gate/normalized_schema_snapshot.json

@@ -0,0 +1,147 @@
+{
+  "dataset_id": "m4",
+  "target_column": "charges",
+  "task_type": "regression",
+  "columns": [
+    {
+      "name": "age",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 47,
+        "unique_ratio": 0.0212,
+        "example_values": [
+          "46",
+          "38",
+          "19",
+          "27",
+          "26"
+        ]
+      }
+    },
+    {
+      "name": "sex",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000902,
+        "example_values": [
+          "female",
+          "male"
+        ]
+      }
+    },
+    {
+      "name": "bmi",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 538,
+        "unique_ratio": 0.24267,
+        "example_values": [
+          "23.655",
+          "19.3",
+          "30.59",
+          "32.67",
+          "29.45"
+        ]
+      }
+    },
+    {
+      "name": "children",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 6,
+        "unique_ratio": 0.002706,
+        "example_values": [
+          "1",
+          "0",
+          "3",
+          "2",
+          "5"
+        ]
+      }
+    },
+    {
+      "name": "smoker",
+      "role": "feature",
+      "semantic_type": "boolean",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000902,
+        "example_values": [
+          "yes",
+          "no"
+        ]
+      }
+    },
+    {
+      "name": "region",
+      "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": 0.001804,
+        "example_values": [
+          "northwest",
+          "southwest",
+          "southeast",
+          "northeast"
+        ]
+      }
+    },
+    {
+      "name": "charges",
+      "role": "target",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 1281,
+        "unique_ratio": 0.577808,
+        "example_values": [
+          "21677.28345",
+          "15820.699",
+          "1639.5631",
+          "2497.0383",
+          "2897.3235"
+        ]
+      }
+    }
+  ]
+}

syntheticFail/m4/tabbyflow/tabbyflow-m4-20260501_005424/public_gate/public_gate_report.json

@@ -0,0 +1,37 @@
+{
+  "dataset_id": "m4",
+  "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": "charges",
+  "task_type": "regression",
+  "input_splits": {
+    "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv",
+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv"
+  }
+}

syntheticFail/m4/tabbyflow/tabbyflow-m4-20260501_005424/public_gate/staged_input_manifest.json

@@ -0,0 +1,152 @@
+{
+  "dataset_id": "m4",
+  "target_column": "charges",
+  "task_type": "regression",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabbyflow/tabbyflow-m4-20260501_005424/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabbyflow/tabbyflow-m4-20260501_005424/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabbyflow/tabbyflow-m4-20260501_005424/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabbyflow/tabbyflow-m4-20260501_005424/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabbyflow/tabbyflow-m4-20260501_005424/public_gate/public_gate_report.json",
+  "column_schema": [
+    {
+      "name": "age",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 47,
+        "unique_ratio": 0.0212,
+        "example_values": [
+          "46",
+          "38",
+          "19",
+          "27",
+          "26"
+        ]
+      }
+    },
+    {
+      "name": "sex",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000902,
+        "example_values": [
+          "female",
+          "male"
+        ]
+      }
+    },
+    {
+      "name": "bmi",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 538,
+        "unique_ratio": 0.24267,
+        "example_values": [
+          "23.655",
+          "19.3",
+          "30.59",
+          "32.67",
+          "29.45"
+        ]
+      }
+    },
+    {
+      "name": "children",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 6,
+        "unique_ratio": 0.002706,
+        "example_values": [
+          "1",
+          "0",
+          "3",
+          "2",
+          "5"
+        ]
+      }
+    },
+    {
+      "name": "smoker",
+      "role": "feature",
+      "semantic_type": "boolean",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000902,
+        "example_values": [
+          "yes",
+          "no"
+        ]
+      }
+    },
+    {
+      "name": "region",
+      "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": 0.001804,
+        "example_values": [
+          "northwest",
+          "southwest",
+          "southeast",
+          "northeast"
+        ]
+      }
+    },
+    {
+      "name": "charges",
+      "role": "target",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 1281,
+        "unique_ratio": 0.577808,
+        "example_values": [
+          "21677.28345",
+          "15820.699",
+          "1639.5631",
+          "2497.0383",
+          "2897.3235"
+        ]
+      }
+    }
+  ]
+}

syntheticFail/m4/tabbyflow/tabbyflow-m4-20260501_005424/runtime_result.json

@@ -0,0 +1,24 @@
+{
+  "dataset_id": "m4",
+  "model": "tabbyflow",
+  "run_id": "tabbyflow-m4-20260501_005424",
+  "public_gate_status": "pass",
+  "adapter_ready_status": "pass",
+  "train_status": "fail",
+  "generate_status": "skipped",
+  "reason_code": "adapter_runtime_error",
+  "reason_detail": "Command '['docker', 'run', '--rm', '--init', '--cidfile', '/tmp/bench_docker_tabbyflow_aijd2i80/container.cid', '--gpus', 'device=2', '-e', 'WANDB_MODE=disabled', '-v', '/data/jialinzhang/SynthesizePipeline-server:/work', '-w', '/work', '-v', '/data/jialinzhang/synthetic_benchmark/third_party/ef-vfm:/workspace/ef-vfm', 'benchmark:tabdiff-tabbyflow-zjl', 'python', '/work/output-Benchmark-trainonly-v1/m4/tabbyflow/tabbyflow-m4-20260501_005424/_tabbyflow_train.py']' returned non-zero exit status 137.",
+  "artifacts": {},
+  "timings": {
+    "train": {
+      "started_at": "2026-05-01T00:54:24",
+      "ended_at": "2026-05-01T01:00:36",
+      "duration_sec": 372.473
+    },
+    "generate": {
+      "started_at": null,
+      "ended_at": null,
+      "duration_sec": null
+    }
+  }
+}

syntheticFail/m4/tabbyflow/tabbyflow-m4-20260501_005424/staged/public/staged_features.json

@@ -0,0 +1,37 @@
+[
+  {
+    "feature_name": "age",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "sex",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "bmi",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "children",
+    "data_type": "continuous",
+    "is_target": false
+  },
+  {
+    "feature_name": "smoker",
+    "data_type": "binary",
+    "is_target": false
+  },
+  {
+    "feature_name": "region",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "charges",
+    "data_type": "continuous",
+    "is_target": true
+  }
+]

syntheticFail/m4/tabbyflow/tabbyflow-m4-20260501_005424/staged/public/test.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4c541b677fb2d45c5bc79338eeee9fd8c91484b195a0c2c546c2fbabf113b7ea
+size 11298

syntheticFail/m4/tabbyflow/tabbyflow-m4-20260501_005424/staged/public/train.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:614ebfac5f41d6f661aff675fa18c0f933a8d9bcc77ea71b7b2360c2f0155837
+size 90069

syntheticFail/m4/tabbyflow/tabbyflow-m4-20260501_005424/staged/public/val.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e55f5edbe7942ff01630de91a9c70dfbc6445eba76c7b970350364546b67c2d7
+size 11218

syntheticFail/m4/tabbyflow/tabbyflow-m4-20260501_005424/staged/tabbyflow/adapter_report.json

@@ -0,0 +1,7 @@
+{
+  "adapter_ready_status": "pass",
+  "adapter_fail_reason_code": null,
+  "adapter_fail_detail": null,
+  "adapter_transforms_applied": [],
+  "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabbyflow/tabbyflow-m4-20260501_005424/staged/tabbyflow/model_input_manifest.json"
+}

syntheticFail/m4/tabbyflow/tabbyflow-m4-20260501_005424/staged/tabbyflow/adapter_transforms_applied.json

@@ -0,0 +1 @@
+[]

syntheticFail/m4/tabbyflow/tabbyflow-m4-20260501_005424/staged/tabbyflow/model_input_manifest.json

@@ -0,0 +1,154 @@
+{
+  "dataset_id": "m4",
+  "model": "tabbyflow",
+  "target_column": "charges",
+  "task_type": "regression",
+  "column_schema": [
+    {
+      "name": "age",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 47,
+        "unique_ratio": 0.0212,
+        "example_values": [
+          "46",
+          "38",
+          "19",
+          "27",
+          "26"
+        ]
+      }
+    },
+    {
+      "name": "sex",
+      "role": "feature",
+      "semantic_type": "categorical",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "mode",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 2,
+        "unique_ratio": 0.000902,
+        "example_values": [
+          "female",
+          "male"
+        ]
+      }
+    },
+    {
+      "name": "bmi",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 538,
+        "unique_ratio": 0.24267,
+        "example_values": [
+          "23.655",
+          "19.3",
+          "30.59",
+          "32.67",
+          "29.45"
+        ]
+      }
+    },
+    {
+      "name": "children",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
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+        "unique_count": 6,
+        "unique_ratio": 0.002706,
+        "example_values": [
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+          "0",
+          "3",
+          "2",
+          "5"
+        ]
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+      "role": "feature",
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+      "nullable": false,
+      "missing_tokens": [],
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+      "impute_strategy": "mode",
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+        "unique_ratio": 0.000902,
+        "example_values": [
+          "yes",
+          "no"
+        ]
+      }
+    },
+    {
+      "name": "region",
+      "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": 0.001804,
+        "example_values": [
+          "northwest",
+          "southwest",
+          "southeast",
+          "northeast"
+        ]
+      }
+    },
+    {
+      "name": "charges",
+      "role": "target",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
+        "unique_count": 1281,
+        "unique_ratio": 0.577808,
+        "example_values": [
+          "21677.28345",
+          "15820.699",
+          "1639.5631",
+          "2497.0383",
+          "2897.3235"
+        ]
+      }
+    }
+  ],
+  "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabbyflow/tabbyflow-m4-20260501_005424/public_gate/staged_input_manifest.json",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabbyflow/tabbyflow-m4-20260501_005424/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabbyflow/tabbyflow-m4-20260501_005424/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabbyflow/tabbyflow-m4-20260501_005424/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabbyflow/tabbyflow-m4-20260501_005424/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabbyflow/tabbyflow-m4-20260501_005424/public_gate/public_gate_report.json"
+}

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+{
+  "name": "pipeline_m4",
+  "task_type": "regression",
+  "n_num_features": 3,
+  "n_cat_features": 3,
+  "train_size": 2217,
+  "test_num": 2217,
+  "val_num": 2217,
+  "train_num": 2217,
+  "bundle_note": "val/test matrices are train copies (train-only policy; no real held-out rows).",
+  "num_col_idx": [
+    0,
+    1,
+    2
+  ],
+  "cat_col_idx": [
+    3,
+    4,
+    5
+  ],
+  "target_col_idx": [
+    6
+  ],
+  "column_names": [
+    "age",
+    "bmi",
+    "children",
+    "sex",
+    "smoker",
+    "region",
+    "charges"
+  ],
+  "int_col_idx": [],
+  "int_columns": [],
+  "int_col_idx_wrt_num": [],
+  "metadata": {
+    "columns": {
+      "0": {
+        "sdtype": "numerical",
+        "computer_representation": "Float"
+      },
+      "1": {
+        "sdtype": "numerical",
+        "computer_representation": "Float"
+      },
+      "2": {
+        "sdtype": "numerical",
+        "computer_representation": "Float"
+      },
+      "3": {
+        "sdtype": "categorical"
+      },
+      "4": {
+        "sdtype": "categorical"
+      },
+      "5": {
+        "sdtype": "categorical"
+      },
+      "6": {
+        "sdtype": "numerical",
+        "computer_representation": "Float"
+      }
+    }
+  },
+  "idx_mapping": {
+    "0": 0,
+    "1": 1,
+    "2": 2,
+    "3": 3,
+    "4": 4,
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+    "6": 6
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+    "0": "age",
+    "1": "bmi",
+    "2": "children",
+    "3": "sex",
+    "4": "smoker",
+    "5": "region",
+    "6": "charges"
+  }
+}

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syntheticFail/m4/tabbyflow/tabbyflow-m4-20260501_005424/train_20260501_005424.log

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syntheticFail/m4/tabsyn/tabsyn-m4-20260505_093658/_tabsyn_train.py

@@ -0,0 +1,69 @@
+import os, sys, subprocess
+
+work_dir = "/work/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260505_093658"
+dataname = "tabsyn_m4"
+tabsyn_root = "/workspace/tabsyn"
+
+assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}"
+
+old = os.environ.get("PYTHONPATH", "")
+os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "")
+sys.path.insert(0, tabsyn_root)
+
+os.chdir(tabsyn_root)
+
+# Symlink data dir into TabSyn data/
+data_link = os.path.join(tabsyn_root, "data", dataname)
+data_src = os.path.join(work_dir, "data", dataname)
+os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True)
+if os.path.exists(data_link):
+    os.remove(data_link)
+os.symlink(data_src, data_link)
+
+env = os.environ.copy()
+env.setdefault("TABSYN_RESUME", "0")
+env.setdefault("TABSYN_VAE_BATCH_SIZE", "32")
+env.setdefault("TABSYN_VAE_NUM_WORKERS", "0")
+env.setdefault("TABSYN_VAE_EVAL_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"])
+env.setdefault("TABSYN_VAE_INFER_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"])
+env.setdefault("TABSYN_VAE_ENCODE_BATCH_SIZE", env["TABSYN_VAE_BATCH_SIZE"])
+# Safer defaults for wide tables on Docker: reduce shared-memory pressure in diffusion DataLoader.
+env.setdefault("TABSYN_DIFFUSION_NUM_WORKERS", "0")
+_te = None
+if _te is not None:
+    env["TABSYN_VAE_EPOCHS"] = str(_te)
+    env["TABSYN_DIFFUSION_MAX_EPOCHS"] = str(max(_te + 1, 2))
+
+# Data preprocessing is done on the host side (_prepare_data_dir)
+# which creates .npy files, train/test CSVs, and info.json
+
+# Step 1: Train VAE (produces latent embeddings)
+print(f"[TabSyn] Step 1/2: Training VAE in {tabsyn_root}, dataname={dataname}")
+ret = subprocess.run(
+    [sys.executable, "main.py",
+     "--dataname", dataname,
+     "--mode", "train",
+     "--method", "vae",
+     "--gpu", "0"],
+    cwd=tabsyn_root,
+    env=env
+)
+if ret.returncode != 0:
+    print("[TabSyn] VAE training failed")
+    sys.exit(ret.returncode)
+
+# Step 2: Train diffusion model on latent space
+print(f"[TabSyn] Step 2/2: Training diffusion model")
+ret = subprocess.run(
+    [sys.executable, "main.py",
+     "--dataname", dataname,
+     "--mode", "train",
+     "--method", "tabsyn",
+     "--gpu", "0"],
+    cwd=tabsyn_root,
+    env=env
+)
+if ret.returncode != 0:
+    print("[TabSyn] Diffusion training failed")
+    sys.exit(ret.returncode)
+print("[TabSyn] Training complete (VAE + Diffusion)")

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+{
+  "name": "tabsyn_m4",
+  "task_type": "regression",
+  "n_num_features": 3,
+  "n_cat_features": 3,
+  "train_size": 2217,
+  "num_col_idx": [
+    0,
+    2,
+    3
+  ],
+  "cat_col_idx": [
+    1,
+    4,
+    5
+  ],
+  "target_col_idx": [
+    6
+  ],
+  "column_names": [
+    "age",
+    "sex",
+    "bmi",
+    "children",
+    "smoker",
+    "region",
+    "charges"
+  ],
+  "train_num": 2217,
+  "test_num": 2217,
+  "header": 0,
+  "file_type": "csv",
+  "data_path": "data/tabsyn_m4/train.csv",
+  "test_path": null,
+  "idx_mapping": {
+    "0": 0,
+    "1": 3,
+    "2": 1,
+    "3": 2,
+    "4": 4,
+    "5": 5,
+    "6": 6
+  },
+  "inverse_idx_mapping": {
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+    "3": 1,
+    "1": 2,
+    "2": 3,
+    "4": 4,
+    "5": 5,
+    "6": 6
+  },
+  "idx_name_mapping": {
+    "0": "age",
+    "1": "sex",
+    "2": "bmi",
+    "3": "children",
+    "4": "smoker",
+    "5": "region",
+    "6": "charges"
+  },
+  "metadata": {
+    "columns": {
+      "0": {
+        "sdtype": "numerical",
+        "computer_representation": "Float"
+      },
+      "2": {
+        "sdtype": "numerical",
+        "computer_representation": "Float"
+      },
+      "3": {
+        "sdtype": "numerical",
+        "computer_representation": "Float"
+      },
+      "1": {
+        "sdtype": "categorical"
+      },
+      "4": {
+        "sdtype": "categorical"
+      },
+      "5": {
+        "sdtype": "categorical"
+      },
+      "6": {
+        "sdtype": "numerical",
+        "computer_representation": "Float"
+      }
+    }
+  }
+}

syntheticFail/m4/tabsyn/tabsyn-m4-20260505_093658/data/tabsyn_m4/test.csv

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syntheticFail/m4/tabsyn/tabsyn-m4-20260505_093658/input_snapshot.json

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+{
+  "dataset_id": "m4",
+  "model": "tabsyn",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv",
+      "exists": true,
+      "size": 92191,
+      "sha256": "396b9d409ca21bf4a4cd329bdf5b7796aa0ae6356fa8d89b8eb669b5880b81f1"
+    },
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+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv",
+      "exists": true,
+      "size": 11482,
+      "sha256": "ee3c247d02f56e1687d03c381e13125d6a3a2a411ac7f202ba8520a4be9f1784"
+    },
+    "test_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv",
+      "exists": true,
+      "size": 11559,
+      "sha256": "cadb9941124001b8fa7cb1ebae43b70a9ca56294f4df4d3c2f22c164c41757d4"
+    },
+    "profile_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_profile.json",
+      "exists": true,
+      "size": 3336,
+      "sha256": "83e2764810e4a0e8cdece3a28dbd9134b7c9df6f2e56953e46d024ad2c4e035f"
+    },
+    "contract_json": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/m4/m4-dataset_contract_v1.json",
+      "exists": true,
+      "size": 3810,
+      "sha256": "c23641b258629a845b164099bd0132886f8f6d0100e990494e0f92540f8987d9"
+    }
+  }
+}

syntheticFail/m4/tabsyn/tabsyn-m4-20260505_093658/public_gate/normalized_schema_snapshot.json

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+  "target_column": "charges",
+  "task_type": "regression",
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+    {
+      "name": "age",
+      "role": "feature",
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+      "impute_strategy": "median",
+      "profile_stats": {
+        "missing_rate": 0.0,
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+        "unique_ratio": 0.0212,
+        "example_values": [
+          "46",
+          "38",
+          "19",
+          "27",
+          "26"
+        ]
+      }
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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": {
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+        "unique_ratio": 0.000902,
+        "example_values": [
+          "female",
+          "male"
+        ]
+      }
+    },
+    {
+      "name": "bmi",
+      "role": "feature",
+      "semantic_type": "numeric",
+      "nullable": false,
+      "missing_tokens": [],
+      "parse_format": null,
+      "impute_strategy": "median",
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+        "unique_ratio": 0.24267,
+        "example_values": [
+          "23.655",
+          "19.3",
+          "30.59",
+          "32.67",
+          "29.45"
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+      "role": "feature",
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+      "nullable": false,
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+        "example_values": [
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+          "5"
+        ]
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+      "role": "feature",
+      "semantic_type": "boolean",
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+          "northwest",
+          "southwest",
+          "southeast",
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+        ]
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+      "role": "target",
+      "semantic_type": "numeric",
+      "nullable": false,
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+      "impute_strategy": "median",
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+        "example_values": [
+          "21677.28345",
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+  ]
+}

syntheticFail/m4/tabsyn/tabsyn-m4-20260505_093658/public_gate/public_gate_report.json

@@ -0,0 +1,37 @@
+{
+  "dataset_id": "m4",
+  "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": "charges",
+  "task_type": "regression",
+  "input_splits": {
+    "train": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-train.csv",
+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/m4/m4-test.csv"
+  }
+}

syntheticFail/m4/tabsyn/tabsyn-m4-20260505_093658/public_gate/staged_input_manifest.json

@@ -0,0 +1,152 @@
+{
+  "dataset_id": "m4",
+  "target_column": "charges",
+  "task_type": "regression",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260505_093658/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260505_093658/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260505_093658/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260505_093658/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260505_093658/public_gate/public_gate_report.json",
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+          "southeast",
+          "northeast"
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+      "role": "target",
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+          "2897.3235"
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+}

syntheticFail/m4/tabsyn/tabsyn-m4-20260505_093658/run_config.json

@@ -0,0 +1,48 @@
+{
+  "schema_version": 1,
+  "recorded_at": "2026-05-05T09:36:58",
+  "dataset_id": "m4",
+  "model": "tabsyn",
+  "work_dir": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260505_093658",
+  "dataset_source_requested": "new",
+  "dataset_source_resolved": "new",
+  "cli_args": {
+    "model": "tabsyn",
+    "dataset": "m4",
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+    "train": true,
+    "generate": true,
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+    "no_stats": false
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+    "model_path": null,
+    "output_csv": null
+  },
+  "input_artifacts": {
+    "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260505_093658/public_gate/public_gate_report.json",
+    "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260505_093658/public_gate/staged_input_manifest.json",
+    "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260505_093658/staged/tabsyn/model_input_manifest.json",
+    "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260505_093658/staged/public/train.csv",
+    "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260505_093658/staged/public/staged_features.json",
+    "target_column": "charges",
+    "task_type": "regression"
+  },
+  "env_overrides": {
+    "BENCHMARK_TABSYN_GPUS": "device=3",
+    "TABSYN_DIFFUSION_MAX_EPOCHS": "5",
+    "TABSYN_RESUME": "0",
+    "TABSYN_VAE_BATCH_SIZE": "128",
+    "TABSYN_VAE_ENCODE_BATCH_SIZE": "256",
+    "TABSYN_VAE_EPOCHS": "5",
+    "TABSYN_VAE_EVAL_BATCH_SIZE": "256",
+    "TABSYN_VAE_INFER_BATCH_SIZE": "256",
+    "TABSYN_VAE_NUM_WORKERS": "0"
+  }
+}

syntheticFail/m4/tabsyn/tabsyn-m4-20260505_093658/runtime_result.json

@@ -0,0 +1,24 @@
+{
+  "dataset_id": "m4",
+  "model": "tabsyn",
+  "run_id": "tabsyn-m4-20260505_093658",
+  "public_gate_status": "pass",
+  "adapter_ready_status": "pass",
+  "train_status": "fail",
+  "generate_status": "skipped",
+  "reason_code": "adapter_runtime_error",
+  "reason_detail": "Command '['docker', 'run', '--rm', '--init', '--cidfile', '/tmp/bench_docker_tabsyn_d11j4bt2/container.cid', '--gpus', 'device=3', '-v', '/data/jialinzhang/SynthesizePipeline-server:/work', '-w', '/work', '-v', '/data/jialinzhang/synthetic_benchmark/tabsyn:/workspace/tabsyn', 'benchmark:tabsyn-zjl', 'python', '/work/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260505_093658/_tabsyn_train.py']' returned non-zero exit status 129.",
+  "artifacts": {},
+  "timings": {
+    "train": {
+      "started_at": "2026-05-05T09:36:58",
+      "ended_at": "2026-05-05T09:49:06",
+      "duration_sec": 728.832
+    },
+    "generate": {
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+      "ended_at": null,
+      "duration_sec": null
+    }
+  }
+}

syntheticFail/m4/tabsyn/tabsyn-m4-20260505_093658/staged/public/staged_features.json

@@ -0,0 +1,37 @@
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+  },
+  {
+    "feature_name": "charges",
+    "data_type": "continuous",
+    "is_target": true
+  }
+]

syntheticFail/m4/tabsyn/tabsyn-m4-20260505_093658/staged/public/test.csv

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syntheticFail/m4/tabsyn/tabsyn-m4-20260505_093658/staged/public/train.csv

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syntheticFail/m4/tabsyn/tabsyn-m4-20260505_093658/staged/public/val.csv

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syntheticFail/m4/tabsyn/tabsyn-m4-20260505_093658/staged/tabsyn/adapter_report.json

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+  "adapter_fail_detail": null,
+  "adapter_transforms_applied": [],
+  "model_input_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260505_093658/staged/tabsyn/model_input_manifest.json"
+}

syntheticFail/m4/tabsyn/tabsyn-m4-20260505_093658/staged/tabsyn/adapter_transforms_applied.json

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syntheticFail/m4/tabsyn/tabsyn-m4-20260505_093658/staged/tabsyn/model_input_manifest.json

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+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260505_093658/staged/public/train.csv",
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+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260505_093658/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260505_093658/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/m4/tabsyn/tabsyn-m4-20260505_093658/public_gate/public_gate_report.json"
+}