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Vercel Python 函數套件大小限制提升至 500MB

Vercel Python 函數套件大小限制提升至 500MB
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閱讀原文: Vercel News
#serverless#deployment#bundle-limitvercelvercelpythonfastapiflask

💡Easier Vercel deploys for bigger Python ML apps—bundle limit doubled to 500MB!

⚡ 30-Second TL;DR

有什麼變化

Python 執行階段套件限制提升至 500MB 未壓縮

為什麼重要

這讓 AI 從業人員能部署更大的 Python 基礎 ML 模型或資料處理應用程式,而無需分割套件,降低部署複雜度。有助於 Vercel 上無伺服器 AI API 開發。

下一步行動

Deploy a larger Python FastAPI app on Vercel to test the new 500MB bundle limit.

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關鍵要點

  • Python 執行階段套件限制提升至 500MB 未壓縮
  • 先前限制為 250MB
  • 適用於 Vercel Functions 部署
  • 支援 FastAPI 和 Flask 入門套件

🧠 深度解析

背景與延伸:來自公開資料,非原文內容。引用 8 個來源。

🔑 增強重點摘要

  • Vercel's Python runtime lacks automatic tree-shaking, bundling all reachable project files by default, which often leads to oversized deployments requiring manual exclusions via configuration.[3]
  • The 250MB limit stemmed from AWS Lambda constraints minus Vercel overhead, prompting users to tree-shake dependencies or switch to custom Lambda images for larger needs.[1]
  • Community reports showed Python apps with libraries like Plotly and pandas frequently exceeding 250MB (e.g., 360MB), causing deployment failures on Hobby plans.[6]

🛠️ 技術深入

  • Python Functions bundle all project files reachable at build time without Node.js-style tree-shaking; developers must limit pyproject.toml or requirements.txt to runtime essentials and exclude files like tests or static assets.[3]
  • Unzipped bundle must not exceed the limit post-build (previously ~50MB compressed equating to 250MB uncompressed); dynamic imports or large deps like Puppeteer historically caused issues across runtimes.[4][5]
  • Runtime memory defaults to 1024MB on Hobby but is separate from bundle size; total env vars limited to 64KB combined for Python deployments.[1][2]

🔮 前景展望AI analysis grounded in cited sources

Enables deployment of ML/data apps with heavy deps like TensorFlow subsets without custom workarounds.
Doubling to 500MB directly addresses common community complaints of 300-360MB failures with pandas/Plotly, reducing need for AWS Lambda alternatives.[1][6]
May increase cold start times and costs for Python Functions due to larger bundles.
Larger uncompressed sizes lead to slower initialization on serverless platforms, as noted in historical bundle optimization guides.[4]

時間線

2021-07
Vercel community discusses exceeding 50MB function limits with large deps like Puppeteer.
2023-12
Python runtime docs specify 250MB uncompressed bundle limit without tree-shaking.
2024-01
Community reports Python apps hitting 360MB with Plotly/pandas on 250MB limit.
2025-11
Hobby plan users report 300MB deployment errors despite 1024MB memory availability.
2026-02
Vercel raises Python Functions bundle limit from 250MB to 500MB uncompressed.
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原始來源: Vercel News

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