Stopwatch the Cold Start, Not the Model

Описание и применение

В новых AI‑рабочих пространствах задержка запуска определяется не моделью, а «холодным стартом». Автор создал скрипт, измеряющий миллисекундные таймстампы каждого шага, выявил узкие места и оптимизировал их, сократив холодный старт до одной минуты.

Задача

In new AI‑assisted workspaces, the model is rarely the bottleneck; the invisible cold start dominates onboarding latency.

Результат

By measuring and bucketing cold start steps, the author reduced the cold start to a single warm step under one minute, improving onboarding latency and enabling focused debugging.

Рабочий процесс

• 1. Run cold‑start script to timestamp each step of workspace bootstrap.
• 2. Generate a TSV log of timestamps.
• 3. Use awk to compute delta and total times per step.
• 4. Execute the script three times in fresh containers to capture variance.
• 5. Analyze deltas against bucket thresholds to identify bottlenecks.
• 6. Apply targeted fixes (e.g., pin versions, cache layers, fail loudly on auth).
• 7. Create a warm target in Makefile to pre‑warm environment before real work.

Доказательства

in a brand-new AI-assisted workspace, the model is almost never your bottleneck. The cold start is, and it stays invisible because nobody writes timestamps down.

Источник

The script below is deliberately boring: it records a millisecond timestamp before and after each step you already run, then prints the deltas.

Источник

If run one and run three differ by more than about thirty percent on the same step, you are not measuring your setup, you are measuring the network.

Источник

The one fix that mattered for me was not faster inference; it was turning the cold start into a single warm step that either succeeds or fails in under a minute.

Источник