- AdaptiveGate rewritten (Netflix Gradient2): window-vs-window per-stream
speed gradient, count-driven windows with admission stamps, no thresholds
or mode state machine; failures x0.7 + 30s drain pause
- session-level admission for multi-turn agents (_SessionGate): in-progress
sessions hold slots until done, newcomers queue at the door; capacity
follows the model gate's discovered limit (CONCUR-style continuity)
- image service: memory-first register (zero docker calls for known
images), TTL-cached docker images listing, optimistic ready when the
daemon is unreachable (docker save contention no longer kills runs);
es tar loading removed in favor of ModelScope shipping (ms_images.py
per-image tar upload/pull with round-trip verification)
- runner: circuit breaker (12 consecutive failures abort the bench),
first-failure error printed immediately
- swe_agentic: image wait / docker run / rm off the event loop; exec
timeout becomes an observation the agent can react to; container gets
curlrc + git low-speed aborts (stalled github downloads fail fast)
- eval run excludes its own endpoints from http_proxy (a sick personal
proxy read as 'endpoint dead' and killed whole runs)
- progress bar shows failed count; swe agentic exec_workers 2 -> 4
Co-Authored-By: Claude <noreply@anthropic.com>
One ImageService per process. When tasks arrive (runner) or a sample
starts (env), its images are REGISTERED; a background worker pool
delivers them -- local tar shipments first (es's swebench_v 500-image
batch set, disk-cached index), network mirror chain second. Sample
execution waits on a readiness barrier instead of the old failing
timings (docker-run implicit pull killed at 120s; score-phase batch
pull ran after generation had already failed).
- runner registers every pending sample's image up front (pull-ahead
overlaps generation)
- env blocks on wait_ready(1800s) before docker run -- a slow pull
delays that sample, never fails it
- tar index cached at /tmp/evalharness_tar_index.json (full scan costs
minutes; only the first process pays)
- images the service loaded are released at exit (atexit; opt out with
EVALHARNESS_KEEP_SWE_IMAGES); pre-existing local images never touched
- EVALHARNESS_IMAGE_WORKERS (default 2) tunes the pool
Verified E2E: register -> background load from swebench_batch_001.tar.gz
-> image present locally (matplotlib-14623); wait barrier semantics
confirmed (blocks until load completes).
Co-Authored-By: Claude <noreply@anthropic.com>
