Buckets:
| """Pull the headline numbers from the job artifacts into one compact summary, | |
| so the logbook can cite exact values.""" | |
| import glob | |
| import json | |
| import os | |
| import sys | |
| OUT = sys.argv[1] if len(sys.argv) > 1 else "outputs" | |
| def g(d, *ks, default=None): | |
| for k in ks: | |
| d = d.get(k, {}) if isinstance(d, dict) else {} | |
| return d if d != {} else default | |
| ev = json.load(open(f"{OUT}/eval.json")) if os.path.exists(f"{OUT}/eval.json") else {} | |
| print("=" * 60) | |
| if "newton_alignment" in ev: | |
| na = ev["newton_alignment"] | |
| print("NEWTON ALIGNMENT (cosine to Newton dir, quadratics N=%s)" % na.get("N")) | |
| for k in ("lsr1_proj", "lsr1_noproj", "lbfgs"): | |
| v = na[k] | |
| print(f" {k:12s}: iter1={v[0]:+.3f} mid={v[len(v)//2]:+.3f} " | |
| f"final={v[-1]:+.3f} mean={sum(v)/len(v):+.3f}") | |
| if "performance" in ev: | |
| pf = ev["performance"] | |
| print("\nPERFORMANCE PROFILES (AUC over log-tau, higher=better)") | |
| for s, a in sorted(pf["aucs"].items(), key=lambda x: -x[1]): | |
| rho1 = pf["profiles"][s][0] | |
| print(f" {s:12s}: AUC={a:.3f} rho(tau=1)={rho1:.3f}") | |
| print("\n per-type median iters-to-target (L-SR1 vs best baseline):") | |
| for t in pf["per_type"]: | |
| med = t["median"]; solved = t["solved"] | |
| base = {k: v for k, v in med.items() if k != "L-SR1" and v is not None} | |
| bb = min(base, key=base.get) if base else None | |
| print(f" {t['family']:11s} N={str(t['N']):>4s} cond={str(t['cond']):>7s}" | |
| f" | L-SR1 med={med['L-SR1']} solved={solved['L-SR1']}" | |
| f" | best base={bb}={med.get(bb) if bb else None}") | |
| # wall-clock from histories | |
| print("\nWALL-CLOCK (meta-training)") | |
| for h in sorted(glob.glob(f"{OUT}/*_hist.json")): | |
| d = json.load(open(h)) | |
| name = os.path.basename(h).replace("_hist.json", "") | |
| it = d["history"][-1]["iter"] if d["history"] else 0 | |
| print(f" {name:20s}: {it} iters in {d.get('wall_time_s', 0)/60:.1f} min") | |
| if os.path.exists(f"{OUT}/efficiency.json"): | |
| e = json.load(open(f"{OUT}/efficiency.json")) | |
| print("\nEFFICIENCY (synthetic proxy -- NOT a Claim-6 reproduction)") | |
| print(f" L-SR1 update params={e['lsr1_update_params']:,} " | |
| f"lgd-style params={e['lgd_update_params']:,}") | |
| print(f" lsr1={e['lsr1_ms']:.1f}ms lgd={e['lgd_ms']:.1f}ms " | |
| f"mem lsr1={e['lsr1_mem_gib']} lgd={e['lgd_mem_gib']}") | |
| print("=" * 60) | |
Xet Storage Details
- Size:
- 2.39 kB
- Xet hash:
- 219b971dd368dc36aef123d619068f218394870648d55fdd04f4e09e07337358
·
Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.