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modal

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Use when running Python or GPU workloads serverlessly on Modal — modal.App, inline container Images, gpu= on @app.function, Volumes for weight caching, Cron schedules, ASGI endpoints, modal run vs serve vs deploy. NOT managed prediction APIs with no container of your own (that is replicate); NOT SSH-able GPU boxes rented by the hour (that is runpod).

skills/modal/SKILL.md

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574
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Nov 9, 2026
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MIT

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# Modal — serverless Python & GPU as decorators

Modal runs your Python on remote containers without you ever writing a Dockerfile or a YAML
file. The mental model: **infrastructure is declared inline as Python decorators.** A
`modal.App` is the deployable unit; each `@app.function` runs in its own container built from
a `modal.Image` you describe in code; you attach a GPU, a Volume, or a Secret as keyword
arguments and the platform provisions, scales to zero, and tears down for you. There is no
control plane to babysit — the source file *is* the infra.

Pinned stack: **modal 1.4.3** (released 2026-05-18), Python **3.10–3.14** (`>=3.10,<3.15`).
Install with `pip install modal` then `modal setup` to authenticate. Everything below uses
the Modal 1.0+ API; several pre-1.0 forms were removed and are called out as Bad→Good.

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