Instrument the job pipeline so we can see where wall-clock actually goes and
decide — on data, not theory — whether a download/compute split is worth
building. Each stage is timed per job and a rolling breakdown is logged every
30s to the agent console, e.g.:
timing/30s — lease 8ms · download 310ms · decode 40ms · gpu 165ms · submit 70ms | wall/job 585ms (214 jobs)
- lease timed around client.lease() in the slot loop (per batch).
- download = fetch_image; decode = image/frame decode; gpu = detect + CCIP +
batched embed; submit = the results POST. One-time model load is excluded
from the gpu figure.
- Thread-safe accumulator (stage -> [sum, count]) summarised + reset by a small
daemon reporter thread; logs only when there was work.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
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