| """Re-run a pull request on MacroSwan/pilot-tasks in simulation and post the score on the PR page. |
| |
| python scripts/score_pr.py 7 # PR number; needs `pip install -e <cablepilot>[sim,hub]` |
| python scripts/score_pr.py 7 --seeds 0 1 2 --worlds bedroom playroom --post |
| |
| The contributor's own result is a claim; this is the check. It downloads the PR's card and tuning (falling back to the |
| task's merged ones), runs each (world, seed), and compares with the merged best on the same worlds and seeds when the |
| board has them. Designed to run on the Mac or as a Hugging Face Job (CPU is enough: ~25 min per world and seed). |
| Nothing from the PR is executed as code: cards and tunings are YAML, validated before the run. |
| """ |
| from __future__ import annotations |
|
|
| import argparse |
| import json |
| import subprocess |
| import sys |
| import tempfile |
| from pathlib import Path |
|
|
| REPO = "MacroSwan/pilot-tasks" |
|
|
|
|
| def main(): |
| ap = argparse.ArgumentParser() |
| ap.add_argument("pr", type=int) |
| ap.add_argument("--seeds", type=int, nargs="+", default=[0]) |
| ap.add_argument("--worlds", nargs="+", default=["bedroom"]) |
| ap.add_argument("--post", action="store_true", help="comment the result on the PR (else print it)") |
| a = ap.parse_args() |
|
|
| from huggingface_hub import HfApi, snapshot_download |
| api = HfApi() |
| rev = f"refs/pr/{a.pr}" |
| details = api.get_discussion_details(REPO, a.pr, repo_type="dataset") |
| work = Path(tempfile.mkdtemp(prefix=f"pr{a.pr}_")) |
| snap = Path(snapshot_download(REPO, repo_type="dataset", revision=rev, local_dir=work / "pr")) |
| tasks = sorted(p.parent.name for p in snap.glob("tasks/*/card.yaml")) |
| title = details.title |
| task = next((t for t in tasks if f"[{t}]" in title), None) |
| if task is None: |
| sys.exit(f"PR title must start with [<task>]: {title!r}") |
| tdir = snap / "tasks" / task |
| card, tuning = tdir / "card.yaml", tdir / "tuning" / "cable.yaml" |
| lines = [f"**Re-run of PR #{a.pr}** (`{task}`, cable body, simulation)", "", |
| "| world | seed | done | s / item | furniture ticks | max tension | plan ms |", "|---|---|---|---|---|---|---|"] |
| rows = [] |
| for w in a.worlds: |
| for s in a.seeds: |
| out = work / f"run_{w}_s{s}" |
| cmd = ["cablepilot", "run", str(card), "--world", w, "--seed", str(s), "--out", str(out), "--by", "pilot-scorer"] |
| if tuning.exists(): |
| cmd += ["--tuning", str(tuning)] |
| r = subprocess.run(cmd, capture_output=True, text=True) |
| if r.returncode != 0 or not (out / "result.json").exists(): |
| lines.append(f"| {w} | {s} | **failed** | | | | |") |
| print(r.stdout[-2000:], r.stderr[-2000:]) |
| continue |
| res = json.loads((out / "result.json").read_text()) |
| sc = res["score"] |
| rows.append(res) |
| lines.append(f"| {w} | {s} | {sc['done']}/{sc['of']} | {sc['s_per_item']:.0f} | {sc['furniture_contact_ticks']} | " |
| f"{sc['max_tension_N']:.0f} N | {res['compute']['plan_ms_mean']:.0f} |") |
| done = sum(r["score"]["done"] for r in rows) |
| of = sum(r["score"]["of"] for r in rows) |
| lines += ["", f"Total: **{done}/{of}**. card `{rows[0]['card_sha'] if rows else '?'}`, tuning " |
| f"`{rows[0]['tuning_sha'] if rows else '?'}`. Same worlds and seeds as the board's best are the fair comparison."] |
| text = "\n".join(lines) |
| print(text) |
| if a.post: |
| api.comment_discussion(REPO, a.pr, text, repo_type="dataset") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
