huggingface-zerogpu Skill
AI demos and GPU compute with Gradio Spaces and Hugging Face Spaces ZeroGPU. Use when writing or reviewing code that uses @spaces.GPU, configuring pythonversion or requirements.txt for a ZeroGPU Space, or handling ZeroGPU-specific code constraints — pickle-based process isolation, gr.State semantics across the worker b Published by huggingface in skills.
What is huggingface-zerogpu Skill?
AI demos and GPU compute with Gradio Spaces and Hugging Face Spaces ZeroGPU. Use when writing or reviewing code that uses @spaces.GPU, configuring pythonversion or requirements.txt for a ZeroGPU Space, or handling ZeroGPU-specific code constraints — pickle-based process isolation, gr.State semantics across the worker b Published by huggingface in skills. This profile combines repository metadata with install, compatibility, and usage signals so developers can quickly decide whether it fits their agent workflow before opening the source repository.
Automated repository signals based on public metadata such as recency, license, installation evidence, and adoption. These are not a security audit or endorsement.
Key capabilities
- Includes SKILL.md support
- Reusable instructions support
- Deployment
- Documentation
- Writing
- Deployment use cases
- Documentation use cases
Technical details
- Install or run with Copy skill directory
When to use huggingface-zerogpu Skill
- Use it for deployment.
- Use it for documentation.
- Use it for writing.
Built with
Editorial notes
Source
- Creator: huggingface
- Repository: huggingface/skills
- Skill file: skills/huggingface-zerogpu/SKILL.md
What it does
AI demos and GPU compute with Gradio Spaces and Hugging Face Spaces ZeroGPU. Use when writing or reviewing code that uses @spaces.GPU, configuring pythonversion or requirements.txt for a ZeroGPU Space, or handling ZeroGPU-specific code constraints — pickle-based process isolation, gr.State semantics across the worker b
Skill instructions
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
Explore related resources
Frequently asked questions
What is huggingface-zerogpu?
huggingface-zerogpu is a open-source AI agent skill with Copy skill directory. AI demos and GPU compute with Gradio Spaces and Hugging Face Spaces ZeroGPU. Use when writing or reviewing code that uses @spaces.GPU, configuring pythonversion or requirements.txt for a ZeroGPU.
Who is huggingface-zerogpu best for?
huggingface-zerogpu is best for reusing agent instructions, scripts, and references, deployment workflows, documentation workflows, writing workflows.
How do I install huggingface-zerogpu?
Install or run huggingface-zerogpu using Copy skill directory. Check huggingface-zerogpu for the latest setup command.
Is huggingface-zerogpu actively maintained?
huggingface-zerogpu may need a closer maintenance check before production use.
Auto-fetched from GitHub.
