| |
| """Download + prepare MLE-bench competitions on the cluster, one Job per comp. |
| |
| gke/.venv/bin/python gke/prep_datasets.py submit --split both # the 5/15 split (20 comps) |
| gke/.venv/bin/python gke/prep_datasets.py submit --lite # MLE-bench Lite (22 comps) |
| gke/.venv/bin/python gke/prep_datasets.py submit --competitions spooky-author-identification |
| gke/.venv/bin/python gke/prep_datasets.py status |
| |
| Each Job runs `mlebench prepare` from Kaggle (kaggle-json secret + rules you |
| accepted on kaggle.com), tars prepared/, and uploads it to |
| gs://<bucket>/data/<comp>/prepared.tar.gz. Already-uploaded comps are skipped |
| unless --force. Prep pods run on spot by default (cheap; a preempted prep just |
| reruns). |
| """ |
| from __future__ import annotations |
|
|
| import argparse |
| import json |
| import sys |
| import urllib.request |
| from pathlib import Path |
|
|
| sys.path.insert(0, str(Path(__file__).resolve().parent)) |
| import k8s as K |
| import settings as S |
|
|
| REPO = Path(__file__).resolve().parents[1] |
| LITE_URL = ("https://raw.githubusercontent.com/openai/mle-bench/main/" |
| "experiments/splits/low.txt") |
|
|
| |
| |
| SIZE_GB = { |
| "spooky-author-identification": 0.1, "tabular-playground-series-dec-2021": 1, |
| "lmsys-chatbot-arena": 1, "dog-breed-identification": 1, |
| "dogs-vs-cats-redux-kernels-edition": 1, "random-acts-of-pizza": 0.1, |
| "tweet-sentiment-extraction": 0.1, "new-york-city-taxi-fare-prediction": 6, |
| "plant-pathology-2020-fgvc7": 3, "facebook-recruiting-iii-keyword-extraction": 4, |
| "ventilator-pressure-prediction": 1, "smartphone-decimeter-2022": 20, |
| "AI4Code": 4, "billion-word-imputation": 5, |
| "cassava-leaf-disease-classification": 7, "osic-pulmonary-fibrosis-progression": 20, |
| "petfinder-pawpularity-score": 2, "statoil-iceberg-classifier-challenge": 1, |
| "bms-molecular-translation": 10, "nfl-player-contact-detection": 10, |
| "stanford-covid-vaccine": 3, "whale-categorization-playground": 1, |
| "aerial-cactus-identification": 0.1, "denoising-dirty-documents": 0.1, |
| "detecting-insults-in-social-commentary": 0.1, "leaf-classification": 0.1, |
| "mlsp-2013-birds": 1, "text-normalization-challenge-english-language": 1, |
| "text-normalization-challenge-russian-language": 1, "nomad2018-predict-transparent-conductors": 0.1, |
| "jigsaw-toxic-comment-classification-challenge": 1, "kuzushiji-recognition": 6, |
| "histopathologic-cancer-detection": 8, "aptos2019-blindness-detection": 10, |
| "siim-isic-melanoma-classification": 30, "tgs-salt-identification-challenge": 1, |
| "the-icml-2013-whale-challenge-right-whale-redux": 1, |
| |
| |
| |
| |
| "3d-object-detection-for-autonomous-vehicles": 120, |
| "cdiscount-image-classification-challenge": 60, |
| "google-research-identify-contrails-reduce-global-warming": 450, |
| "h-and-m-personalized-fashion-recommendations": 40, |
| "herbarium-2020-fgvc7": 130, |
| "herbarium-2021-fgvc8": 180, |
| "herbarium-2022-fgvc9": 160, |
| "icecube-neutrinos-in-deep-ice": 150, |
| "inaturalist-2019-fgvc6": 230, |
| "iwildcam-2019-fgvc6": 50, |
| "iwildcam-2020-fgvc7": 110, |
| "rsna-miccai-brain-tumor-radiogenomic-classification": 140, |
| "seti-breakthrough-listen": 160, |
| "siim-covid19-detection": 130, |
| |
| "plant-pathology-2021-fgvc8": 20, |
| "predict-volcanic-eruptions-ingv-oe": 35, |
| "rsna-2022-cervical-spine-fracture-detection": 350, |
| "rsna-breast-cancer-detection": 280, |
| "tensorflow-speech-recognition-challenge": 5, |
| "tensorflow2-question-answering": 20, |
| "us-patent-phrase-to-phrase-matching": 1, |
| "uw-madison-gi-tract-image-segmentation": 5, |
| "vesuvius-challenge-ink-detection": 40, |
| "vinbigdata-chest-xray-abnormalities-detection": 210, |
| } |
|
|
| |
| |
| |
| SCRATCH_OVERRIDE_GI = { |
| "bms-molecular-translation": 400, |
| |
| "siim-isic-melanoma-classification": 500, |
| } |
|
|
|
|
| def split_sides() -> dict[str, str]: |
| """comp -> train|eval from the frozen 5/15 manifest (also feeds SIZE_GB).""" |
| mf = json.loads((REPO / "rubric_gen/splits/error_side_5_15.json").read_text()) |
| sides = {} |
| for key, side in (("source_competitions", "train"), |
| ("evaluation_competitions", "eval")): |
| for c in mf[key]: |
| sides[c["id"]] = side |
| SIZE_GB[c["id"]] = float(c.get("dataset_size_gb") or 25) |
| return sides |
|
|
|
|
| def wanted(args) -> list[str]: |
| sides = split_sides() |
| comps: list[str] = [] |
| if args.competitions: |
| comps += [c.strip() for c in args.competitions.split(",") if c.strip()] |
| if args.split in ("train", "both"): |
| comps += [c for c, s in sides.items() if s == "train"] |
| if args.split in ("eval", "both"): |
| comps += [c for c, s in sides.items() if s == "eval"] |
| if args.lite: |
| with urllib.request.urlopen(LITE_URL, timeout=30) as r: |
| comps += [l.strip() for l in r.read().decode().splitlines() if l.strip()] |
| seen, out = set(), [] |
| for c in comps: |
| if c not in seen: |
| seen.add(c) |
| out.append(c) |
| return out |
|
|
|
|
| def in_bucket(comp: str) -> bool: |
| return S.bucket().blob(f"data/{comp}/meta.json").exists() |
|
|
|
|
| def cmd_submit(args) -> None: |
| sides = split_sides() |
| comps = wanted(args) |
| if not comps: |
| raise SystemExit("nothing selected — use --split/--lite/--competitions") |
| K.upload_code("prep") |
| launched = skipped = 0 |
| for comp in comps: |
| if not args.force and in_bucket(comp): |
| print(f"[have] {comp}") |
| skipped += 1 |
| continue |
| scratch = SCRATCH_OVERRIDE_GI.get( |
| comp, int(min(max(4 * SIZE_GB.get(comp, 25) + 20, 50), 2000))) |
| slug = comp.lower().replace("_", "-")[:40].strip("-") |
| K.delete_job(f"prep-{slug}") |
| job = K.make_prep_job(comp, sides.get(comp, "benchmark"), scratch, |
| force=args.force, spot=not args.no_spot) |
| K.submit_job(job) |
| print(f"[job] {job.metadata.name} (scratch {scratch}Gi, " |
| f"side {sides.get(comp, 'benchmark')})") |
| launched += 1 |
| print(f"\nlaunched {launched}, already in bucket {skipped}. " |
| f"Watch: gke/.venv/bin/python gke/prep_datasets.py status") |
|
|
|
|
| def cmd_status(args) -> None: |
| comps = wanted(args) or sorted(split_sides()) |
| have = 0 |
| for comp in comps: |
| if in_bucket(comp): |
| meta = json.loads(S.bucket().blob(f"data/{comp}/meta.json") |
| .download_as_text()) |
| print(f"[have] {comp} ({meta['tar_bytes']/1e9:.2f} GB tar, " |
| f"{meta['prep_minutes']} min prep)") |
| have += 1 |
| else: |
| slug = comp.lower().replace("_", "-")[:40].strip("-") |
| print(f"[....] {comp} job: {K.job_status(f'prep-{slug}')}") |
| print(f"\n{have}/{len(comps)} in gs://{S.BUCKET}/data/") |
|
|
|
|
| def cmd_logs(args) -> None: |
| slug = args.competitions.lower().replace("_", "-")[:40].strip("-") |
| print(K.pod_logs(f"prep-{slug}", tail=args.tail)) |
|
|
|
|
| def main() -> None: |
| ap = argparse.ArgumentParser() |
| sub = ap.add_subparsers(dest="cmd", required=True) |
| for name in ("submit", "status", "logs"): |
| p = sub.add_parser(name) |
| p.add_argument("--competitions", default="") |
| p.add_argument("--split", choices=("train", "eval", "both", "none"), |
| default="none") |
| p.add_argument("--lite", action="store_true") |
| p.add_argument("--force", action="store_true") |
| p.add_argument("--no-spot", action="store_true") |
| p.add_argument("--tail", type=int, default=60) |
| args = ap.parse_args() |
| {"submit": cmd_submit, "status": cmd_status, "logs": cmd_logs}[args.cmd](args) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
