Prune redundant 5090 runs after discrete fixes

5090-Success/c11/forestdiffusion/forest-c11-20260419_200956/_fd_X_host.npy

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5090-Success/c11/forestdiffusion/forest-c11-20260419_200956/_fd_gen.py

@@ -1,8 +0,0 @@
-
-import joblib, pandas as pd
-m, meta = joblib.load(r'/work/output-SpecializedModels/c11/forestdiffusion/forest-c11-20260419_200956/forestdiffusion_model.joblib')
-# generate：batch_size 为样本数
-arr = m.generate(batch_size=int(54045))
-df = pd.DataFrame(arr, columns=meta["column_names"])
-df.to_csv(r'/work/output-SpecializedModels/c11/forestdiffusion/forest-c11-20260419_200956/forest-c11-54045-20260421_163815.csv', index=False)
-print("saved", len(df))

5090-Success/c11/forestdiffusion/forest-c11-20260419_200956/_fd_meta_host.json

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5090-Success/c11/forestdiffusion/forest-c11-20260419_200956/_fd_train.py

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-
-import shutil, json
-shutil.copy(r'/work/output-SpecializedModels/c11/forestdiffusion/forest-c11-20260419_200956/_fd_X_host.npy', '/tmp/fd_X.npy')
-with open(r'/work/output-SpecializedModels/c11/forestdiffusion/forest-c11-20260419_200956/_fd_meta_host.json') as f:
-    open('/tmp/fd_meta.json','w').write(f.read())
-
-import numpy as np, joblib, json, os
-from ForestDiffusion import ForestDiffusionModel
-X = np.load("/tmp/fd_X.npy")
-with open("/tmp/fd_meta.json") as f:
-    meta = json.load(f)
-cat_indexes = meta["cat_indexes"]
-m = ForestDiffusionModel(
-    X, n_t=20, n_estimators=100, duplicate_K=20, n_jobs=2,
-    model="xgboost", max_depth=6, tree_method="hist", cat_indexes=cat_indexes,
-)
-joblib.dump((m, meta), "/tmp/fd_model.joblib")
-print("ForestDiffusion train OK")
-
-shutil.copy('/tmp/fd_model.joblib', r'/work/output-SpecializedModels/c11/forestdiffusion/forest-c11-20260419_200956/forestdiffusion_model.joblib')

5090-Success/c11/forestdiffusion/forest-c11-20260419_200956/forest-c11-54045-20260421_163815.csv

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5090-Success/c11/forestdiffusion/forest-c11-20260419_200956/forestdiffusion_model.joblib

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5090-Success/c11/forestdiffusion/forest-c11-20260419_200956/input_snapshot.json

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5090-Success/c11/forestdiffusion/forest-c11-20260419_200956/public_gate/normalized_schema_snapshot.json

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5090-Success/c11/forestdiffusion/forest-c11-20260419_200956/public_gate/public_gate_report.json

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5090-Success/c11/forestdiffusion/forest-c11-20260419_200956/runtime_result.json

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5090-Success/c11/forestdiffusion/forest-c11-20260419_200956/staged/forestdiffusion/adapter_report.json

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5090-Success/c11/forestdiffusion/forest-c11-20260419_200956/staged/public/val.csv

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5090-Success/c11/forestdiffusion/forest-c11-20260419_200956/train_20260419_200956.log

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5090-Success/c11/tabbyflow/tabbyflow-c11-20260422_035745/_tabbyflow_gen.py

@@ -1,33 +0,0 @@
-
-import os, shutil, subprocess, sys
-root = r"/workspace/ef-vfm"
-name = r"pipeline_ds"
-src = r"/work/output-SpecializedModels/c11/tabbyflow/tabbyflow-c11-20260422_035745/tabular_bundle/pipeline_ds"
-dst_data = os.path.join(root, "data", name)
-shutil.rmtree(dst_data, ignore_errors=True)
-shutil.copytree(src, dst_data)
-dst_syn = os.path.join(root, "synthetic", name)
-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", "")
-subprocess.check_call([
-    sys.executable, "main.py",
-    "--dataname", name, "--mode", "test", "--gpu", "0",
-    "--no_wandb", "--exp_name", r"adapter_tabbyflow-c11-20260422_035745",
-    "--ckpt_path", r"/workspace/ef-vfm/ef_vfm/ckpt/pipeline_ds/adapter_tabbyflow-c11-20260422_035745/model_500.pt",
-    "--num_samples_to_generate", str(int(54045)),
-])
-base = os.path.join(root, "ef_vfm", "result", name, r"adapter_tabbyflow-c11-20260422_035745")
-best = None
-best_t = -1.0
-for r, _, files in os.walk(base):
-    if "samples.csv" in files:
-        p = os.path.join(r, "samples.csv")
-        t = os.path.getmtime(p)
-        if t > best_t:
-            best_t, best = t, p
-if not best:
-    raise SystemExit("tabbyflow: no samples.csv in " + base)
-shutil.copy(best, r"/work/output-SpecializedModels/c11/tabbyflow/tabbyflow-c11-20260422_035745/tabbyflow-c11-54045-20260422_052600.csv")

5090-Success/c11/tabbyflow/tabbyflow-c11-20260422_035745/_tabbyflow_train.py

@@ -1,22 +0,0 @@
-
-import os, shutil, subprocess, sys
-root = r"/workspace/ef-vfm"
-name = r"pipeline_ds"
-src = r"/work/output-SpecializedModels/c11/tabbyflow/tabbyflow-c11-20260422_035745/tabular_bundle/pipeline_ds"
-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_tabbyflow-c11-20260422_035745",
-])

5090-Success/c11/tabbyflow/tabbyflow-c11-20260422_035745/input_snapshot.json

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5090-Success/c11/tabbyflow/tabbyflow-c11-20260422_035745/models_tabbyflow/trained.pt

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