Resume SynthData0523 main/c4 batch 5

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SynthData0523/main/c4/tabpfgen/tabpfgen-c4-20260422_200030/public_gate/public_gate_report.json

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+    "val": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c4/c4-val.csv",
+    "test": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c4/c4-test.csv"
+  }
+}

SynthData0523/main/c4/tabpfgen/tabpfgen-c4-20260422_200030/public_gate/staged_input_manifest.json

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+  "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c4/tabpfgen/tabpfgen-c4-20260422_200030/staged/public/train.csv",
+  "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c4/tabpfgen/tabpfgen-c4-20260422_200030/staged/public/val.csv",
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+  "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c4/tabpfgen/tabpfgen-c4-20260422_200030/public_gate/public_gate_report.json"
+}

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+  "json_path": "/data/jialinzhang/SynthesizePipeline-server/output-SpecializedModels/c4/tabpfgen/tabpfgen-c4-20260422_200030/staged/public/staged_features.json",
+  "target_col": "class",
+  "is_classification": true,
+  "n_rows": 2557,
+  "n_cols": 37
+}

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SynthData0523/main/c4/tabsyn/tabsyn-c4-20260510_080926/_tabsyn_sample.py

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+import os, sys, subprocess
+
+work_dir = "/work/output-Benchmark-trainonly-v1/c4/tabsyn/tabsyn-c4-20260510_080926"
+dataname = "tabsyn_c4_tabsyn_c4_20260510_080926"
+output_csv = "/work/output-Benchmark-trainonly-v1/c4/tabsyn/tabsyn-c4-20260510_080926/tabsyn-c4-2557-20260510_081222.csv"
+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)
+
+# Ensure data symlink exists
+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)
+
+print(f"[TabSyn] Sampling 2557 rows")
+env = os.environ.copy()
+env.setdefault("TABSYN_RESUME", "0")
+env.setdefault("TABSYN_VAE_BATCH_SIZE", "32")
+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"])
+ret = subprocess.run(
+    [sys.executable, "main.py",
+     "--dataname", dataname,
+     "--mode", "sample",
+     "--method", "tabsyn",
+     "--gpu", "0",
+     "--save_path", output_csv],
+    cwd=tabsyn_root,
+    env=env
+)
+if ret.returncode != 0:
+    sys.exit(ret.returncode)
+print(f"[TabSyn] Saved -> {output_csv}")

SynthData0523/main/c4/tabsyn/tabsyn-c4-20260510_080926/_tabsyn_train.py

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+import os, sys, subprocess
+
+work_dir = "/work/output-Benchmark-trainonly-v1/c4/tabsyn/tabsyn-c4-20260510_080926"
+dataname = "tabsyn_c4_tabsyn_c4_20260510_080926"
+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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