Individual skill
serverless-modal
Run GPU workloads on Modal — training, fine-tuning, inference, batch processing. Zero-config serverless: no SSH, no Docker, auto scale-to-zero. Use when user says "modal run", "modal training", "modal inference", "deploy to modal", "need a GPU", "run on modal", "serverless GPU", or needs remote GPU compute.
skills/skills-codex/serverless-modal/SKILL.md
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- ai-research Repository topic
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- codex Repository topic
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Read excerpt
# Modal Cloud GPU — Training & Inference
Task: $ARGUMENTS
## Overview
**Modal** is a serverless GPU cloud. Key advantages over SSH-based platforms (vast.ai, remote servers):
- **Zero config**: no SSH, no Docker, no port forwarding. Write Python → `modal run` → done.
- **Auto scale-to-zero**: billing stops the instant your code finishes. No idle instances.
- **Local-first**: run `modal run` from your laptop. Code, data, and results stay local; only the GPU function runs remotely.
- **Reproducible environments**: dependencies declared in code via `modal.Image`, not system-level packages.
Treat the `modal.Image` chain as the RENDERED form of the declarative env spec in
`../shared-references/compute-env-contract.md` — same spec fields (base, ordered
pip phases, env vars, smoke probes), same `env:<name>@<specHash>` ledger entry in