Resume SynthData0523 main/c2 batch 19

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+    "model_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/staged/tabddpm/model_input_manifest.json",
+    "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/staged/public/train.csv",
+    "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/staged/public/staged_features.json",
+    "target_column": "class",
+    "task_type": "classification"
+  },
+  "env_overrides": {
+    "BENCHMARK_TABDDPM_GPUS": "device=3",
+    "TABDDPM_NUM_TIMESTEPS": "1000",
+    "TABDDPM_SAMPLE_BATCH_SIZE": "256",
+    "TABDDPM_STEPS_PER_EPOCH": "100",
+    "TABDDPM_TRAIN_BATCH_SIZE": "256",
+    "TABDDPM_TRAIN_LR": "0.001"
+  }
+}

SynthData0523/main/c2/tabddpm/tabddpm-c2-20260504_181207/runtime_result.json

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+{
+  "dataset_id": "c2",
+  "model": "tabddpm",
+  "run_id": "tabddpm-c2-20260504_181207",
+  "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/tabddpm/tabddpm-c2-20260504_181207/tabddpm-c2-1382-20260504_181327.csv",
+    "model_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207"
+  },
+  "timings": {
+    "train": {
+      "started_at": "2026-05-04T18:12:07",
+      "ended_at": "2026-05-04T18:13:27",
+      "duration_sec": 80.183
+    },
+    "generate": {
+      "started_at": "2026-05-04T18:13:27",
+      "ended_at": "2026-05-04T18:14:13",
+      "duration_sec": 45.511
+    }
+  }
+}

SynthData0523/main/c2/tabddpm/tabddpm-c2-20260504_181207/staged/public/staged_features.json

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+[
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+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
+    "feature_name": "maint",
+    "data_type": "categorical",
+    "is_target": false
+  },
+  {
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+    "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/tabddpm/tabddpm-c2-20260504_181207/staged/public/test.csv

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SynthData0523/main/c2/tabddpm/tabddpm-c2-20260504_181207/staged/public/train.csv

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SynthData0523/main/c2/tabddpm/tabddpm-c2-20260504_181207/staged/public/val.csv

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SynthData0523/main/c2/tabddpm/tabddpm-c2-20260504_181207/staged/tabddpm/adapter_report.json

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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/tabddpm/tabddpm-c2-20260504_181207/staged/tabddpm/model_input_manifest.json"
+}

SynthData0523/main/c2/tabddpm/tabddpm-c2-20260504_181207/staged/tabddpm/adapter_transforms_applied.json

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SynthData0523/main/c2/tabddpm/tabddpm-c2-20260504_181207/staged/tabddpm/model_input_manifest.json

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+        "example_values": [
+          "vhigh",
+          "med",
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+    },
+    {
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+      "nullable": false,
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+          "med"
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+        "example_values": [
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+          "good",
+          "acc",
+          "vgood"
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+  ],
+  "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/public_gate/staged_input_manifest.json",
+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/staged/public/val.csv",
+  "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/staged/public/test.csv",
+  "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/staged/public/staged_features.json",
+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c2/tabddpm/tabddpm-c2-20260504_181207/public_gate/public_gate_report.json"
+}

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SynthData0523/main/c2/tabddpm/tabddpm-c2-20260504_181207/train_20260504_181207.log

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SynthData0523/main/c2/tabdiff/tabdiff-c2-20260501_053602/_tabdiff_gen.py

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+
+import os, shutil, subprocess, sys
+td = r"/workspace/TabDiff"
+name = r"pipeline_c2"
+src = r"/work/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260501_053602/tabular_bundle/pipeline_c2"
+dst_data = os.path.join(td, "data", name)
+dst_syn = os.path.join(td, "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(td)
+os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "")
+subprocess.check_call([
+    sys.executable, "-m", "tabdiff.main",
+    "--dataname", name, "--mode", "test", "--gpu", "0",
+    "--no_wandb", "--exp_name", r"adapter_learnable",
+    "--ckpt_path", r"/workspace/TabDiff/tabdiff/ckpt/pipeline_c2/adapter_learnable/model_500.pt",
+    "--num_samples_to_generate", str(int(1382)),
+])
+# test() 写入 tabdiff/result/<dataname>/<exp>/<epoch>/samples.csv
+import glob as g
+base = os.path.join(td, "tabdiff", "result", name, r"adapter_learnable")
+best = None
+best_t = -1.0
+for root, _, files in os.walk(base):
+    if "samples.csv" in files:
+        p = os.path.join(root, "samples.csv")
+        t = os.path.getmtime(p)
+        if t > best_t:
+            best_t = t
+            best = p
+if not best:
+    raise SystemExit("tabdiff: no samples.csv under " + base)
+shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260501_053602/tabdiff-c2-1382-20260501_054253.csv")

SynthData0523/main/c2/tabdiff/tabdiff-c2-20260501_053602/_tabdiff_train.py

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+
+import os, shutil, subprocess, sys
+td = r"/workspace/TabDiff"
+name = r"pipeline_c2"
+src = r"/work/output-Benchmark-trainonly-v1/c2/tabdiff/tabdiff-c2-20260501_053602/tabular_bundle/pipeline_c2"
+dst_data = os.path.join(td, "data", name)
+dst_syn = os.path.join(td, "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(td)
+os.environ["PYTHONPATH"] = td + os.pathsep + os.environ.get("PYTHONPATH", "")
+os.environ["TABDIFF_SMOKE_STEPS"] = "500"
+os.environ["TABDIFF_ADAPTER_TRAIN"] = "1"
+subprocess.check_call([
+    sys.executable, "-m", "tabdiff.main",
+    "--dataname", name, "--mode", "train", "--gpu", "0",
+    "--no_wandb", "--exp_name", r"adapter_learnable",
+])

SynthData0523/main/c2/tabdiff/tabdiff-c2-20260501_053602/gen_20260501_054253.log

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SynthData0523/main/c2/tabdiff/tabdiff-c2-20260501_053602/input_snapshot.json

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+{
+  "dataset_id": "c2",
+  "model": "tabdiff",
+  "inputs": {
+    "train_csv": {
+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-train.csv",
+      "exists": true,
+      "size": 42948,
+      "sha256": "17bc560fa96bd00fb3b526e1e65bc91210b701d0d0a4e8bb9b4c5196cab56def"
+    },
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+      "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c2/c2-val.csv",
+      "exists": true,
+      "size": 5349,
+      "sha256": "61e565eca62e65a7dccd9d51039a3170413379e10fc494e25870e7c4294863c9"
+    },
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+      "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/tabdiff/tabdiff-c2-20260501_053602/models_tabdiff/trained.pt

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SynthData0523/main/c2/tabdiff/tabdiff-c2-20260501_053602/public_gate/normalized_schema_snapshot.json

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SynthData0523/main/c2/tabdiff/tabdiff-c2-20260501_053602/public_gate/public_gate_report.json

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+{
+  "dataset_id": "c2",
+  "status": "pass",
+  "checks": [
+    {
+      "check_id": "PG001_csv_parse_ok",
+      "status": "pass"
+    },
+    {
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+      "status": "pass"
+    },
+    {
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+      "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"
+  }
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