Resume SynthData0523 main/m8 batch 3

.gitattributes

@@ -10058,3 +10058,82 @@ SynthData0523/main/m8/tabpfgen/tabpfgen-m8-20260501_012611/train_20260501_012611
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SynthData0523/main/m8/tabsyn/tabsyn-m8-20260420_230925/_tabsyn_sample.py

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+import os, sys, subprocess
+
+work_dir = "/work/output-SpecializedModels/m8/tabsyn/tabsyn-m8-20260420_230925"
+dataname = "tabsyn_m8"
+output_csv = "/work/output-SpecializedModels/m8/tabsyn/tabsyn-m8-20260420_230925/tabsyn-m8-36168-20260421_005047.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 36168 rows")
+env = os.environ.copy()
+env.setdefault("TABSYN_RESUME", "1")
+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/m8/tabsyn/tabsyn-m8-20260420_230925/_tabsyn_train.py

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+import os, sys, subprocess
+
+work_dir = "/work/output-SpecializedModels/m8/tabsyn/tabsyn-m8-20260420_230925"
+dataname = "tabsyn_m8"
+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", "1")
+_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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+size 3631424

SynthData0523/main/m8/tabsyn/tabsyn-m8-20260501_041249/_tabsyn_sample.py

@@ -0,0 +1,39 @@
+import os, sys, subprocess
+
+work_dir = "/work/output-Benchmark-trainonly-v1/m8/tabsyn/tabsyn-m8-20260501_041249"
+dataname = "tabsyn_m8"
+output_csv = "/work/output-Benchmark-trainonly-v1/m8/tabsyn/tabsyn-m8-20260501_041249/tabsyn-m8-36168-20260501_052934.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 36168 rows")
+env = os.environ.copy()
+env.setdefault("TABSYN_RESUME", "1")
+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/m8/tabsyn/tabsyn-m8-20260501_041249/_tabsyn_train.py

@@ -0,0 +1,65 @@
+import os, sys, subprocess
+
+work_dir = "/work/output-Benchmark-trainonly-v1/m8/tabsyn/tabsyn-m8-20260501_041249"
+dataname = "tabsyn_m8"
+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", "1")
+env.setdefault("TABSYN_VAE_BATCH_SIZE", "1024")
+# 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)")

SynthData0523/main/m8/tabsyn/tabsyn-m8-20260501_041249/data/tabsyn_m8/X_cat_test.npy

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+size 2604224

SynthData0523/main/m8/tabsyn/tabsyn-m8-20260501_041249/data/tabsyn_m8/X_cat_train.npy

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SynthData0523/main/m8/tabsyn/tabsyn-m8-20260501_041249/data/tabsyn_m8/X_num_test.npy

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SynthData0523/main/m8/tabsyn/tabsyn-m8-20260501_041249/data/tabsyn_m8/X_num_train.npy

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+size 1012832

SynthData0523/main/m8/tabsyn/tabsyn-m8-20260501_041249/data/tabsyn_m8/info.json

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+version https://git-lfs.github.com/spec/v1
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+size 2851

SynthData0523/main/m8/tabsyn/tabsyn-m8-20260501_041249/data/tabsyn_m8/test.csv

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