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a/SynthData0523/main/c4/tabsyn/tabsyn-c4-20260510_080926/_tabsyn_sample.py b/SynthData0523/main/c4/tabsyn/tabsyn-c4-20260510_080926/_tabsyn_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..85b7d432401b5b9c1e63adb841e3e6794ed03de2 --- /dev/null +++ b/SynthData0523/main/c4/tabsyn/tabsyn-c4-20260510_080926/_tabsyn_sample.py @@ -0,0 +1,43 @@ +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}") diff --git a/SynthData0523/main/c4/tabsyn/tabsyn-c4-20260510_080926/_tabsyn_train.py b/SynthData0523/main/c4/tabsyn/tabsyn-c4-20260510_080926/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..b2bbaf119a03f20b45fd08b2f1330c22d11f5d0c --- /dev/null +++ b/SynthData0523/main/c4/tabsyn/tabsyn-c4-20260510_080926/_tabsyn_train.py @@ -0,0 +1,69 @@ +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)") diff --git a/SynthData0523/main/c4/tabsyn/tabsyn-c4-20260510_080926/data/tabsyn_c4_tabsyn_c4_20260510_080926/X_cat_test.npy 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