time_s/time_h summed per-prediction latency_s, which includes RESTORED
predictions' original generation time -- days old and from a slower
setup, it once reported 15.9h for a one-hour aime25 run. All rows now
report the bench's actual wall clock; token totals stay as the true
cost of the predictions used.
n for repeats>1 is num_samples x repeats (12 runs over 30 problems is
360 generations, not 30).
Co-Authored-By: Claude <noreply@anthropic.com>
The checkpoint stores predictions, not scores -- reruns re-executed the
whole docker scoring pass even when nothing could change. When the
saved report matches (same model, same sample count) and every
prediction is checkpointed, reuse it; --rescore forces evaluation
(recipe/judge changed).
Also fixes EvalReport.load: metric_groups declared Dict[str, float]
rejected the None perf values (and the repeats scores list) the file
format actually contains -- every load raised ValidationError, which
is what silently killed reuse in testing.
Repeats>1 benches never reuse: their whole point is fresh sampling.
Co-Authored-By: Claude <noreply@anthropic.com>
Previously both were written only after ALL benches finished -- a
multi-hour mmlu_pro left the summary stale for hours, and a crashed
run left the PREVIOUS run's files in place. Now each completed bench
(including failed ones) rewrites the summaries with everything so far.
Co-Authored-By: Claude <noreply@anthropic.com>
Each file carries that run's own score and samples; the top-level
report.jsonl stays the mean-summary view (mean + repeats group).
Co-Authored-By: Claude <noreply@anthropic.com>
12 repeats each scored on the terminal but the artifacts kept only the
final repeat's report; the run list scrolled away. Now the last report
carries metric_groups['repeats'] = {n_runs, scores[12], mean, min, max,
std} -- lands in report.jsonl's header line, the xlsx categories sheet,
and summary.csv's categories column (numeric entries); the console
summary line also prints the full runs=[...] list.
Co-Authored-By: Claude <noreply@anthropic.com>
- new flag wraps even a single endpoint as a one-member pool so the
per-endpoint AdaptiveGate takes over; --concurrency becomes the gate's
STARTING point (global semaphore lifted to the gate ceiling of 96)
- demand-driven AIMD fallback for endpoints without /metrics (404,
gateway-stripped, non-sglang): ramp +1 while callers wait on acquire
and the interval is failure-free; the old code early-returned on
fetch errors and never adapted at all. Real GLM endpoint verified:
/metrics is 404, so this fallback is the live path there
- probe fetch moved to a thread: a blocked urlopen parked the whole
event loop for its 4s timeout
- current limit surfaces on the progress bar as a magenta 'gate N'
field (pushed on every change)
Co-Authored-By: Claude <noreply@anthropic.com>
The bar kept its stale 100% generation state during scoring with only
the description counter moving. Now set_scoring() refills the bar with
judged samples (0->100%, fresh clock/rate/eta for the phase), clears
the generation '+N new' marker, and the next bench retargets back to
generating cleanly.
Co-Authored-By: Claude <noreply@anthropic.com>
The bar's 'scoring N/164' counter and 21 milestone log lines were both
active on terminals -- the lines are noise there. Emit them only for
pipes/redirects where no bar exists (every ~10% now).
Co-Authored-By: Claude <noreply@anthropic.com>
- docker exec: named containers; a timed-out/killed 'docker run' only
kills the CLI client while the container lives on (--rm fires on
EXIT) -- rm -f the name on timeout/interrupt so runs stop leaking
- exit 125 = daemon-side failure, not model failure: retry up to 2x
(a bloated daemon was turning healthy samples into pass=0)
- scoring milestones: first completion logs immediately, then every
~5% (10% was too sparse when docker is slow: minutes of silence
right after the 'scoring' phase starts, looks hung)
Co-Authored-By: Claude <noreply@anthropic.com>
Scoring progress: docker-exec benches (humaneval etc.) score for
minutes with zero feedback -- the bar sat at 'generating 100%' and
looked hung. evaluate() now takes on_scored(i, n) (atomic counter,
fires from worker threads), run_eval passes it through, and the CLI
shows 'scoring 42/164' on the bar + milestone log lines every 10%
(also fixes the phase match: 'scoring' never matched the capitalized
'Scoring predictions...' status message, so the bar never even
switched its label).
PooledAdapter:
- one release per acquire: the exception path released True (inner
finally) AND False (except handler), double-decrementing _inflight
(over-admission) and applying the x0.7 backoff twice
- AdaptiveGate: rebuild the Condition + probe task when the event loop
changes -- pools are cached across benchmarks and the CLI runs
asyncio.run() per bench/repeat; a loop-bound Condition from a closed
loop raises 'bound to a different event loop' under contention
Co-Authored-By: Claude <noreply@anthropic.com>
Samples row said 'full dataset (counted when each loads)' -- now it
counts cached samples.jsonl entries (never touches the network, plan
stays instant on cold machines) and multiplies by YAML repeats:
12,957 samples (cached) → 13,617 generations (repeats)
--limit caps each bench before summing; uncached benches are flagged.
YAML config loading extracted to _load_bench_cfg, shared by the run
loop and the plan so repeats can't disagree between the two.
Co-Authored-By: Claude <noreply@anthropic.com>
- perf_stats aggregator lives in eval/, not model/: the import failed
silently and EVERY perf column was empty (not just ttft). Now warns
on stderr instead of swallowing.
- repeats > 1 get their own checkpoint key (:rep2, :rep3, ...): repeat 2
previously restored repeat 1's predictions and finished instantly with
identical scores. rep1 keeps the legacy key (existing checkpoints still
resume).
- repeats summary: report the MEAN score and aggregate time/tokens over
ALL runs (was: last run only).
- README: six-benchmark command as the primary example.
Co-Authored-By: Claude <noreply@anthropic.com>
