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huggingface-zerogpu

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AI demos and GPU compute with Gradio Spaces and Hugging Face Spaces ZeroGPU. Use when writing or reviewing code that uses `@spaces.GPU`, configuring `python_version` or `requirements.txt` for a ZeroGPU Space, or handling ZeroGPU-specific code constraints — pickle-based process isolation, `gr.State` semantics across the worker boundary, no `torch.compile` (use AoTI instead), CUDA wheel-only builds (no `nvcc` at build or runtime), large vs xlarge sizing, and dynamic duration callables. Make sure to use this skill whenever the user mentions ZeroGPU, `@spaces.GPU`, or the `spaces` Python package, or hits ZeroGPU-specific code errors like `PicklingError` across the worker boundary, `illegal duration`, or `flash-attn` wheel-build failures — even when the user does not explicitly ask for ZeroGPU coding guidance. Trigger on `import spaces` or `@spaces.GPU` in code.

skills/huggingface-zerogpu/SKILL.md

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Oct 30, 2026
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# Hugging Face ZeroGPU

Rules and patterns for ML demos on Hugging Face Spaces with **ZeroGPU** hardware. Covers `@spaces.GPU`, duration and quota tuning, process isolation, the CUDA availability model, concurrency safety, and CUDA build constraints.

## Scope

This skill is for **Gradio SDK Spaces using ZeroGPU hardware**. Docker and Static Spaces cannot schedule onto ZeroGPU, and Streamlit apps now run as Docker Spaces — so this skill applies only to Gradio. For general Gradio coding (components, layouts, event listeners), see the `huggingface-gradio` skill in this repo. The authoritative ZeroGPU docs live at https://huggingface.co/docs/hub/spaces-zerogpu — refer to them for the current backing GPU, runtime version lists, and tier thresholds, all of which change over time.

## Reference Files

| Reference | When to read |
|-----------|--------------|
| `references/concurrency.md` | Always read alongside SKILL.md when writing ZeroGPU code — handlers run in parallel by default |
| `references/how-zerogpu-works.md` | When reasoning about cold-starts, worker reuse, why module-scope warmup does not carry to requests, or why returning CUDA tensors hangs |