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tasks/activeprune-al-unlabeled-pool-pruning/environment/app/data/dev_eval_y.npy

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tasks/activeprune-al-unlabeled-pool-pruning/environment/app/data/eval_a_X.npy

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tasks/activeprune-al-unlabeled-pool-pruning/environment/app/data/eval_c_X.npy

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tasks/activeprune-al-unlabeled-pool-pruning/environment/app/data/pool_a.jsonl.gz

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tasks/activeprune-al-unlabeled-pool-pruning/environment/app/data/pool_a_X.npy

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tasks/activeprune-al-unlabeled-pool-pruning/environment/app/data/pool_b.jsonl.gz

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tasks/activeprune-al-unlabeled-pool-pruning/environment/app/data/pool_c.jsonl.gz

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tasks/activeprune-al-unlabeled-pool-pruning/tests/hidden_data/final/eval_s0_X.npy

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tasks/activeprune-al-unlabeled-pool-pruning/tests/hidden_data/pools/pool_s0_y.npy

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tasks/activeprune-al-unlabeled-pool-pruning/tests/hidden_data/pools/pool_s1.jsonl.gz

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tasks/activeprune-al-unlabeled-pool-pruning/tests/hidden_data/pools/pool_s1_X.npy

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tasks/activeprune-al-unlabeled-pool-pruning/tests/hidden_data/pools/pool_s1_y.npy

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tasks/budgeted-imputation-mcar50/environment/app/cellrunner.py

@@ -0,0 +1,24 @@
+import sys
+
+import numpy as np
+
+
+def main() -> int:
+    in_path, out_path, module_dir = sys.argv[1], sys.argv[2], sys.argv[3]
+
+    with np.load(in_path, allow_pickle=False) as data:
+        X_train = np.ascontiguousarray(data["X_train"], dtype=np.float64)
+        X_eval = np.ascontiguousarray(data["X_eval"], dtype=np.float64)
+        seed = int(data["seed"][0])
+
+    sys.path.insert(0, module_dir)
+    import impute
+
+    result = impute.impute(X_train, X_eval, seed)
+
+    np.save(out_path, np.asarray(result), allow_pickle=False)
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())

tasks/budgeted-imputation-mcar50/environment/app/cellspec.py

@@ -0,0 +1,91 @@
+from __future__ import annotations
+
+import numpy as np
+from sklearn.preprocessing import QuantileTransformer
+
+MISSING_RATE = 0.50
+
+QT_N_QUANTILES = 1000
+QT_SUBSAMPLE = 100_000
+QT_RANDOM_STATE = 0
+
+
+def build_cell(X, seed, n_train, n_eval):
+    """Build one cell from the raw feature matrix `X` (float64, no missing values).
+
+    Rows are permuted with `seed` and then *partitioned*: the first `n_train` rows of the
+    permutation become the training rows and the next `n_eval` become the evaluation rows,
+    so the two are disjoint by construction. Independent MCAR masks at MISSING_RATE are
+    drawn for each matrix from the same generator. A QuantileTransformer with a normal
+    output distribution is fitted on the *incomplete* training matrix and applied to both.
+
+    Returns (X_train, X_eval, X_eval_true, mask_eval):
+      X_train     (n_train, d) float64, gaussianized, np.nan at masked entries
+      X_eval      (n_eval, d)  float64, gaussianized, np.nan at masked entries
+      X_eval_true (n_eval, d)  float64, gaussianized, complete -- the ground truth
+      mask_eval   (n_eval, d)  bool, True where X_eval is missing
+    """
+    X = np.asarray(X, dtype=np.float64)
+    n_rows = X.shape[0]
+    if n_train + n_eval > n_rows:
+        raise ValueError(f"cell needs {n_train + n_eval} rows, matrix has {n_rows}")
+
+    rng = np.random.default_rng(seed)
+    perm = rng.permutation(n_rows)
+    rows_train = perm[:n_train]
+    rows_eval = perm[n_train : n_train + n_eval]
+
+    raw_train = X[rows_train]
+    raw_eval = X[rows_eval]
+
+    mask_train = rng.random(raw_train.shape) < MISSING_RATE
+    mask_eval = rng.random(raw_eval.shape) < MISSING_RATE
+
+    incomplete_train = raw_train.copy()
+    incomplete_train[mask_train] = np.nan
+
+    qt = QuantileTransformer(
+        output_distribution="normal",
+        n_quantiles=min(QT_N_QUANTILES, n_train),
+        subsample=QT_SUBSAMPLE,
+        random_state=QT_RANDOM_STATE,
+        copy=True,
+    )
+    qt.fit(incomplete_train)
+
+    X_train = np.ascontiguousarray(qt.transform(incomplete_train), dtype=np.float64)
+    X_eval_true = np.ascontiguousarray(qt.transform(raw_eval), dtype=np.float64)
+    X_eval = X_eval_true.copy()
+    X_eval[mask_eval] = np.nan
+
+    return X_train, X_eval, X_eval_true, mask_eval
+
+
+def imputation_r2(X_pred, X_true, mask):
+    """Per-feature R^2 over masked entries, averaged unweighted over features.
+
+    For feature j, over the entries where `mask` is True:
+        R2_j = 1 - sum((true - pred)^2) / sum((true - mean(true))^2)
+    Features with fewer than two masked entries, or with a degenerate ground-truth
+    variance over those entries, are skipped. Returns the mean of the kept features.
+    """
+    X_pred = np.asarray(X_pred, dtype=np.float64)
+    X_true = np.asarray(X_true, dtype=np.float64)
+    mask = np.asarray(mask, dtype=bool)
+
+    scores = []
+    for j in range(X_true.shape[1]):
+        sel = mask[:, j]
+        if int(sel.sum()) < 2:
+            continue
+        truth = X_true[sel, j]
+        pred = X_pred[sel, j]
+        ss_tot = float(np.sum((truth - truth.mean()) ** 2))
+        if not np.isfinite(ss_tot) or ss_tot <= 1e-12:
+            continue
+        ss_res = float(np.sum((truth - pred) ** 2))
+        scores.append(1.0 - ss_res / ss_tot)
+
+    if not scores:
+        raise ValueError("no scorable feature in this cell")
+    return float(np.mean(scores))

tasks/budgeted-imputation-mcar50/environment/app/data/dev/dev1.npz

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tasks/budgeted-imputation-mcar50/environment/app/data/dev/dev2.npz

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tasks/budgeted-imputation-mcar50/environment/app/data/dev/dev3.npz

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tasks/budgeted-imputation-mcar50/environment/app/data/dev/dev4.npz

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tasks/budgeted-imputation-mcar50/environment/app/data/dev/dev5.npz

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tasks/budgeted-imputation-mcar50/environment/app/data/dev/dev6.npz

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tasks/budgeted-imputation-mcar50/environment/app/data/dev/manifest.json

@@ -0,0 +1,113 @@
+{
+  "missing_rate": 0.5,
+  "datasets": [
+    {
+      "id": "dev1",
+      "file": "dev1.npz",
+      "source": "Bike_Sharing_Demand",
+      "n_rows": 16984,
+      "n_features": 6,
+      "n_train": 10000,
+      "n_eval": 2500,
+      "seeds": [
+        30615,
+        79312,
+        26369,
+        71673,
+        56378,
+        80138,
+        72677
+      ]
+    },
+    {
+      "id": "dev2",
+      "file": "dev2.npz",
+      "source": "wine_quality",
+      "n_rows": 5318,
+      "n_features": 11,
+      "n_train": 4255,
+      "n_eval": 1063,
+      "seeds": [
+        72644,
+        86588,
+        68686,
+        21963,
+        30836,
+        22644,
+        36510
+      ]
+    },
+    {
+      "id": "dev3",
+      "file": "dev3.npz",
+      "source": "delays_zurich_transport",
+      "n_rows": 3526,
+      "n_features": 8,
+      "n_train": 2821,
+      "n_eval": 705,
+      "seeds": [
+        72900,
+        75973,
+        28715,
+        26999,
+        60207,
+        14546,
+        49960
+      ]
+    },
+    {
+      "id": "dev4",
+      "file": "dev4.npz",
+      "source": "house_sales",
+      "n_rows": 20000,
+      "n_features": 15,
+      "n_train": 10000,
+      "n_eval": 2500,
+      "seeds": [
+        62334,
+        74597,
+        49445,
+        75161,
+        14619,
+        83965,
+        71728
+      ]
+    },
+    {
+      "id": "dev5",
+      "file": "dev5.npz",
+      "source": "Brazilian_houses",
+      "n_rows": 10228,
+      "n_features": 8,
+      "n_train": 8183,
+      "n_eval": 2045,
+      "seeds": [
+        79767,
+        57676,
+        30019,
+        33717,
+        15457,
+        46325,
+        78038
+      ]
+    },
+    {
+      "id": "dev6",
+      "file": "dev6.npz",
+      "source": "Ailerons",
+      "n_rows": 13750,
+      "n_features": 33,
+      "n_train": 10000,
+      "n_eval": 2500,
+      "seeds": [
+        74029,
+        74447,
+        68763,
+        20736,
+        50761,
+        85855,
+        69837
+      ]
+    }
+  ]
+}

tasks/budgeted-imputation-mcar50/environment/app/devkit.py

@@ -0,0 +1,156 @@
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import subprocess
+import sys
+import tempfile
+import time
+from pathlib import Path
+
+import numpy as np
+
+sys.path.insert(0, str(Path(__file__).resolve().parent))
+
+import cellspec
+
+DEV_ROOT = Path("/app/data/dev")
+DEFAULT_MODULE_DIR = Path("/app/output")
+CELLRUNNER = Path(__file__).resolve().parent / "cellrunner.py"
+
+CELL_BUDGET_SEC = 30.0
+
+
+def manifest() -> dict:
+    with open(DEV_ROOT / "manifest.json") as fh:
+        return json.load(fh)
+
+
+def _entry(dataset_id: str) -> dict:
+    for entry in manifest()["datasets"]:
+        if entry["id"] == dataset_id:
+            return entry
+    raise KeyError(f"unknown dev dataset {dataset_id!r}")
+
+
+def load_dev_cell(dataset_id: str, seed_index: int = 0):
+    """Return (X_train, X_eval, X_eval_true, mask_eval) for one development cell."""
+    entry = _entry(dataset_id)
+    with np.load(DEV_ROOT / entry["file"], allow_pickle=False) as data:
+        X = np.ascontiguousarray(data["X"], dtype=np.float64)
+    return cellspec.build_cell(
+        X, int(entry["seeds"][seed_index]), entry["n_train"], entry["n_eval"]
+    )
+
+
+def _score_cell(module_dir: Path, X_train, X_eval, X_eval_true, mask_eval, seed):
+    """Run the submitted module on one cell. Returns (score_or_None, reason, seconds)."""
+    env = dict(os.environ)
+    env.update(
+        {
+            "OMP_NUM_THREADS": "1",
+            "OPENBLAS_NUM_THREADS": "1",
+            "MKL_NUM_THREADS": "1",
+            "NUMEXPR_NUM_THREADS": "1",
+            "PYTHONSAFEPATH": "1",
+        }
+    )
+    with tempfile.TemporaryDirectory() as tmp:
+        env["TMPDIR"] = tmp
+        env["HOME"] = tmp
+        in_path = os.path.join(tmp, "in.npz")
+        out_path = os.path.join(tmp, "out.npy")
+        np.savez(in_path, X_train=X_train, X_eval=X_eval, seed=np.array([seed], dtype=np.int64))
+        argv = [sys.executable, str(CELLRUNNER), in_path, out_path, str(module_dir)]
+        started = time.monotonic()
+        try:
+            proc = subprocess.run(
+                argv,
+                cwd=tmp,
+                env=env,
+                capture_output=True,
+                text=True,
+                timeout=CELL_BUDGET_SEC,
+                start_new_session=True,
+            )
+        except subprocess.TimeoutExpired:
+            return None, "timeout", time.monotonic() - started
+        elapsed = time.monotonic() - started
+        if proc.returncode != 0:
+            tail = (proc.stderr or "").strip().splitlines()[-3:]
+            return None, "error: " + " | ".join(tail), elapsed
+        if not os.path.isfile(out_path):
+            return None, "no output", elapsed
+        try:
+            arr = np.load(out_path, allow_pickle=False)
+        except Exception as exc:
+            return None, f"unreadable output: {exc}", elapsed
+
+    if not isinstance(arr, np.ndarray) or arr.dtype.kind != "f":
+        return None, f"bad dtype {getattr(arr, 'dtype', type(arr))}", elapsed
+    if arr.shape != X_eval_true.shape:
+        return None, f"bad shape {arr.shape} != {X_eval_true.shape}", elapsed
+    arr = np.ascontiguousarray(arr, dtype=np.float64)
+    if not np.all(np.isfinite(arr)):
+        return None, "non-finite value returned", elapsed
+    return cellspec.imputation_r2(arr, X_eval_true, mask_eval), "ok", elapsed
+
+
+def main() -> int:
+    ap = argparse.ArgumentParser(description="run the development panel")
+    ap.add_argument("--list", action="store_true", help="show the development datasets")
+    ap.add_argument("--datasets", default="", help="comma-separated dataset ids")
+    ap.add_argument("--seeds", default="", help="comma-separated seed indices")
+    ap.add_argument("--module", default=str(DEFAULT_MODULE_DIR), help="directory holding impute.py")
+    args = ap.parse_args()
+
+    entries = manifest()["datasets"]
+    if args.list:
+        print(f"{'id':6s} {'dataset':22s} {'rows':>6s} {'d':>4s} {'n_train':>8s} {'n_eval':>7s} seeds")
+        for e in entries:
+            print(
+                f"{e['id']:6s} {e['source']:22s} {e['n_rows']:6d} {e['n_features']:4d} "
+                f"{e['n_train']:8d} {e['n_eval']:7d} {len(e['seeds'])}"
+            )
+        return 0
+
+    wanted = [s for s in args.datasets.split(",") if s] or [e["id"] for e in entries]
+    module_dir = Path(args.module).resolve()
+    if not (module_dir / "impute.py").is_file():
+        print(f"no impute.py under {module_dir}", file=sys.stderr)
+        return 2
+
+    panel = []
+    print(f"{'dataset':10s} {'seed':>5s} {'seconds':>8s}  R2")
+    for dataset_id in wanted:
+        entry = _entry(dataset_id)
+        indices = [int(s) for s in args.seeds.split(",") if s] or list(range(len(entry["seeds"])))
+        per_dataset = []
+        with np.load(DEV_ROOT / entry["file"], allow_pickle=False) as data:
+            X = np.ascontiguousarray(data["X"], dtype=np.float64)
+        for k in indices:
+            seed = int(entry["seeds"][k])
+            X_train, X_eval, X_eval_true, mask_eval = cellspec.build_cell(
+                X, seed, entry["n_train"], entry["n_eval"]
+            )
+            score, reason, elapsed = _score_cell(
+                module_dir, X_train, X_eval, X_eval_true, mask_eval, seed
+            )
+            shown = "0.0000  <- scored as zero: " + reason if score is None else f"{score:.4f}"
+            print(f"{dataset_id:10s} {k:5d} {elapsed:8.2f}  {shown}")
+            per_dataset.append(0.0 if score is None else score)
+        if per_dataset:
+            spread = max(per_dataset) - min(per_dataset)
+            print(
+                f"{dataset_id:10s} {'mean':>5s} {'':>8s}  {np.mean(per_dataset):.4f} "
+                f"(spread over seeds {spread:.4f})"
+            )
+            panel.extend(per_dataset)
+    if panel:
+        print(f"\npanel mean over {len(panel)} cells: {np.mean(panel):.4f}")
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())

tasks/budgeted-imputation-mcar50/environment/app/output/impute.py

@@ -0,0 +1,11 @@
+import numpy as np
+
+
+def impute(X_train: np.ndarray, X_eval: np.ndarray, seed: int) -> np.ndarray:
+    column_mean = np.nanmean(X_train, axis=0)
+    column_mean = np.where(np.isfinite(column_mean), column_mean, 0.0)
+
+    filled = np.array(X_eval, dtype=np.float64, copy=True)
+    missing = ~np.isfinite(filled)
+    filled[missing] = np.take(column_mean, np.nonzero(missing)[1])
+    return filled

tasks/budgeted-imputation-mcar50/tests/hidden_data/final/f1.npz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e68dd964c4e46bec5afc0b8819a116b51cb939523fe1cf7ae682c124c2205f2b
+size 181876

tasks/budgeted-imputation-mcar50/tests/hidden_data/final/f2.npz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6c3a6a036c1f061ee4d2b34603827d2ae7bfd775f0f1a33bd8dc7810bed5dbcf
+size 356757

tasks/budgeted-imputation-mcar50/tests/hidden_data/final/f3.npz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4028d3f8b95401c61f15f872ee4c238c1b8b52c2ce1e1aa03ed5bb0e447008e2
+size 249013

tasks/budgeted-imputation-mcar50/tests/hidden_data/final/f4.npz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cce05c910e2dae3279fefced9ab946b8c32fe020cf695195c85ccd13212fdc7a
+size 406677

tasks/budgeted-imputation-mcar50/tests/hidden_data/final/manifest.json

@@ -0,0 +1,77 @@
+{
+  "missing_rate": 0.5,
+  "datasets": [
+    {
+      "id": "f1",
+      "file": "f1.npz",
+      "source": "nyc-taxi-green-dec-2016",
+      "n_rows": 20000,
+      "n_features": 9,
+      "n_train": 10000,
+      "n_eval": 2500,
+      "seeds": [
+        55074,
+        36481,
+        50471,
+        85394,
+        72916,
+        13221,
+        89674
+      ]
+    },
+    {
+      "id": "f2",
+      "file": "f2.npz",
+      "source": "sulfur",
+      "n_rows": 10081,
+      "n_features": 6,
+      "n_train": 8065,
+      "n_eval": 2016,
+      "seeds": [
+        71924,
+        27470,
+        80931,
+        68457,
+        59126,
+        76829,
+        64891
+      ]
+    },
+    {
+      "id": "f3",
+      "file": "f3.npz",
+      "source": "pol",
+      "n_rows": 14958,
+      "n_features": 26,
+      "n_train": 10000,
+      "n_eval": 2500,
+      "seeds": [
+        86393,
+        46146,
+        13829,
+        35670,
+        27766,
+        73616,
+        10910
+      ]
+    },
+    {
+      "id": "f4",
+      "file": "f4.npz",
+      "source": "houses",
+      "n_rows": 20000,
+      "n_features": 8,
+      "n_train": 10000,
+      "n_eval": 2500,
+      "seeds": [
+        81584,
+        86934,
+        69383,
+        75427,
+        29508,
+        58091,
+        65785
+      ]
+    }
+  ]
+}

tasks/budgeted-imputation-mcar50/tests/hidden_data/intermediate/i1.npz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:313809a73fdecbdca2c4b900ce7b304f9dcc222ef74dd3227d019785b1b5aeaf
+size 260594

tasks/budgeted-imputation-mcar50/tests/hidden_data/intermediate/i2.npz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d05f939518a062a7324825a01245e96eaaf4cb89208e6b4350a5572e5ebf39e1
+size 1776544

tasks/budgeted-imputation-mcar50/tests/hidden_data/intermediate/i3.npz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5391931965a1ca9bc6158d040fb01d116fc65095d59037fff919a8564d00108f
+size 598120

tasks/budgeted-imputation-mcar50/tests/hidden_data/intermediate/i4.npz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7199a0a8bd799c3796d819a8c2560a8fef1cdd7ac73d6d4499f8e28197ff18f6
+size 654201

tasks/budgeted-imputation-mcar50/tests/hidden_data/intermediate/manifest.json

@@ -0,0 +1,77 @@
+{
+  "missing_rate": 0.5,
+  "datasets": [
+    {
+      "id": "i1",
+      "file": "i1.npz",
+      "source": "medical_charges",
+      "n_rows": 20000,
+      "n_features": 3,
+      "n_train": 10000,
+      "n_eval": 2500,
+      "seeds": [
+        43074,
+        12214,
+        80208,
+        65534,
+        77346,
+        53490,
+        20035
+      ]
+    },
+    {
+      "id": "i2",
+      "file": "i2.npz",
+      "source": "house_16H",
+      "n_rows": 20000,
+      "n_features": 16,
+      "n_train": 10000,
+      "n_eval": 2500,
+      "seeds": [
+        62882,
+        71288,
+        87104,
+        63866,
+        45874,
+        19984,
+        58949
+      ]
+    },
+    {
+      "id": "i3",
+      "file": "i3.npz",
+      "source": "yprop_4_1",
+      "n_rows": 8774,
+      "n_features": 42,
+      "n_train": 7020,
+      "n_eval": 1754,
+      "seeds": [
+        86168,
+        81716,
+        69856,
+        87336,
+        15985,
+        69662,
+        83425
+      ]
+    },
+    {
+      "id": "i4",
+      "file": "i4.npz",
+      "source": "MiamiHousing2016",
+      "n_rows": 13922,
+      "n_features": 13,
+      "n_train": 10000,
+      "n_eval": 2500,
+      "seeds": [
+        73817,
+        86396,
+        68746,
+        83796,
+        16590,
+        19037,
+        66359
+      ]
+    }
+  ]
+}

tasks/causalrivers-heldout-station-graph-auroc/environment/Dockerfile

@@ -0,0 +1,51 @@
+FROM --platform=linux/amd64 public.ecr.aws/docker/library/python:3.13-slim-bookworm@sha256:01f42367a0a94ad4bc17111776fd66e3500c1d87c15bbd6055b7371d39c124fb
+
+RUN set -eu; \
+    if [ ! -s /etc/ssl/certs/ca-certificates.crt ]; then \
+      apt-get update && apt-get install -y --no-install-recommends ca-certificates=20230311+deb12u1; \
+    fi; \
+    rm -f /tmp/hz-prereq.list; \
+    echo 'deb [signed-by=/usr/share/keyrings/debian-archive-keyring.gpg] https://snapshot.debian.org/archive/debian/20260801T000000Z bookworm main' >> /tmp/hz-prereq.list; \
+    echo 'deb [signed-by=/usr/share/keyrings/debian-archive-keyring.gpg] https://snapshot.debian.org/archive/debian-security/20260801T000000Z bookworm-security main' >> /tmp/hz-prereq.list; \
+    apt-get -o Dir::Etc::sourcelist=/tmp/hz-prereq.list -o Dir::Etc::sourceparts=/dev/null -o Acquire::Check-Valid-Until=false update; \
+    apt-get -o Dir::Etc::sourcelist=/tmp/hz-prereq.list -o Dir::Etc::sourceparts=/dev/null -o Acquire::Check-Valid-Until=false install -y --no-install-recommends tmux=3.3a-3 asciinema=2.2.0-1 git=1:2.39.5-0+deb12u3; \
+    rm -f /tmp/hz-prereq.list; rm -rf /var/lib/apt/lists/*
+
+
+ENTRYPOINT []
+ENV DEBIAN_FRONTEND=noninteractive \
+    PYTHONHASHSEED=0 \
+    PIP_NO_CACHE_DIR=1 \
+    PIP_DISABLE_PIP_VERSION_CHECK=1 \
+    OMP_NUM_THREADS=1 \
+    MKL_NUM_THREADS=1 \
+    OPENBLAS_NUM_THREADS=1 \
+    NUMEXPR_NUM_THREADS=1
+
+RUN git config --system user.email "agent@localhost" \
+    && git config --system user.name "agent"
+
+RUN useradd --create-home --uid 1001 agent
+
+COPY ./requirements.txt /tmp/requirements.txt
+RUN /usr/local/bin/python3 -m pip install --no-cache-dir -r /tmp/requirements.txt \
+    && rm /tmp/requirements.txt
+
+COPY ./materialize_data.py /tmp/materialize_data.py
+COPY ./shards /tmp/shards
+RUN /usr/local/bin/python3 /tmp/materialize_data.py /tmp/shards /app/data \
+    && chown -R agent:agent /app \
+    && find /app -exec touch -d @1700000000 {} + \
+    && rm -rf /tmp/shards /tmp/materialize_data.py
+
+WORKDIR /app
+COPY --chown=agent:agent ./app /app
+
+RUN mkdir -p /app/output \
+    && chown agent:agent /app /app/output \
+    && find /app ! -name '*.npy' -exec touch -d @1700000000 {} +
+
+ENV HF_HUB_OFFLINE=1
+ENV TRANSFORMERS_OFFLINE=1
+
+USER agent

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+cause,effect
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