---
title: "Precompile repeated imports in disposable test copies"
description: "Compile repeated imports once per test shard when every test subprocess runs with bytecode writes off."
url: https://starsling.dev/ci-playbooks/techniques/precompile-test-imports
canonicalUrl: https://starsling.dev/ci-playbooks/techniques/precompile-test-imports
---

# Precompile repeated imports in disposable test copies

Compile repeated imports once per test shard when every test subprocess runs with bytecode writes off.

## How to choose an approach

This technique currently has one documented company example. Validate its prerequisites, tradeoffs and original CI environment against your repository before adapting it.

- **[OpenAI](https://starsling.dev/ci-playbooks/openai#precompile-test-imports)**: Use it when Tests launch many isolated interpreters with bytecode writes disabled. Check before adopting: Generated bytecode stays out of release artifacts. Hash-based invalidation, interpreter compatibility and the real no-write tests remain necessary.

## Apply this technique

````text
Evaluate and apply this CI technique to my repository: Precompile repeated imports in disposable test copies.

Compile repeated imports once per test shard when every test subprocess runs with bytecode writes off.

Read the technique and its source-backed company examples: https://starsling.dev/ci-playbooks/techniques/precompile-test-imports.md

Inspect this repository’s CI workflows, build and test commands, dependencies, and recent run timings. Identify where this technique applies and explain the expected benefit using this repository’s evidence.
Measure a baseline from recent comparable runs: elapsed time, queue time, runner minutes and failures where available. If data is unavailable, say what cannot be measured.
Compare relevant company approaches, checking prerequisites, platform differences and tradeoffs. Rank applicable options by evidence, fit and measurable opportunity; do not present company results as predictions for this repo.
Recommend the smallest safe change, show what checks and fallback paths remain, and ASK ME WHICH CHANGE TO APPROVE BEFORE EDITING ANY FILES.
Only after approval, work on a new feature branch, make one reversible change, and open a reviewable pull request. Never merge or push to a default or protected branch.
Keep required checks, test coverage, deployment and release behavior intact. Validate the change and compare equivalent runs where possible; report limitations and what should be monitored after merge.
````

## How OpenAI used this technique

Read what OpenAI changed and follow the original sources.

### OpenAI: Precompile repeated imports in disposable test copies

Codex Security runs compileall with checked hashes on each shard's disposable copy of the package. Release artifacts keep the original archive, and the isolated subprocesses keep their no-write flags.

Evidence: Source PR/commit evidence

Paired local Linux run of the 43 scan-resume cases: 451.7s to 381.7s, plus 1.1s of preparation ([Source](https://github.com/openai/codex-security/pull/2036))

Use it when: Tests launch many isolated interpreters with bytecode writes disabled.

Before applying it: Generated bytecode stays out of release artifacts. Hash-based invalidation, interpreter compatibility and the real no-write tests remain necessary.

[Read more in the OpenAI playbook](https://starsling.dev/ci-playbooks/openai#precompile-test-imports)

[Primary source](https://github.com/openai/codex-security/pull/2036)

## CI optimization is continuous work.

Apply this technique yourself, or use StarSling Runners for faster GitHub Actions and optional agent-driven optimization PRs, which teams on eligible paid plans can turn on.

[Get self-improving CI](https://starsling.dev/products/runners)

## Company Playbooks

### [Anthropic](https://starsling.dev/ci-playbooks/anthropic)

Run only affected tests

### [DoorDash](https://starsling.dev/ci-playbooks/doordash)

Run build and test in parallel, sharded across CI agents

### [Linear](https://starsling.dev/ci-playbooks/linear)

Benchmark the same job on faster hardware

### [Mastra](https://starsling.dev/ci-playbooks/mastra)

StarSling runners for the slowest test workflow

### [OpenAI](https://starsling.dev/ci-playbooks/openai)

Rank CI fixes by the minutes they save

### [Ramp](https://starsling.dev/ci-playbooks/ramp)

Move a CPU-bound test suite to a faster CPU

### [Shopify](https://starsling.dev/ci-playbooks/shopify)

Let an agent iterate against one CI metric

### [Spotify](https://starsling.dev/ci-playbooks/spotify)

Build arm64 on native arm64 runners

### [Stripe](https://starsling.dev/ci-playbooks/stripe)

Run only the tests a change can reach

### [Uber](https://starsling.dev/ci-playbooks/uber)

Build the merge-queue speculations most likely to be needed