hf-spaces

name	hf-spaces
description	Build and deploy ML demo apps on Hugging Face Spaces: Gradio, Docker, or Static SDKs, ZeroGPU vs paid hardware, and debugging a Space that won't build or run. Use to host a public demo or app for a model.
stack	ml

Hugging Face Spaces

A Space is a git repo hosting an app. Three SDKs:

SDK	Use	Hardware
Gradio	default for ML demos, Python, ZeroGPU	compute
Docker	non-Python stack (Streamlit, etc.)	compute, no ZeroGPU
Static	plain HTML / Svelte / React, in-browser ML (transformers.js)	free, none

Auth + hardware reality

hf auth whoami    # note canPay and isPro

• Static Spaces are free for everyone, no hardware.
• Gradio/Docker need a paid plan, except a free account in good standing can host up to 2 ZeroGPU Spaces.
• ZeroGPU (zero-a10g) is dynamic per-request GPU (RTX PRO 6000). Gradio + PyTorch only. Free to create; visitors spend their own quota.
• Dedicated GPU (T4/L4/A10G/A100/H200) is hourly-billed, canPay=True only. Check: hf spaces hardware.

Workflow

1. Search prior art first: hf spaces search "<model or task>" --sdk gradio. Read an existing app.py + requirements.txt for the working pattern.
2. Decide SDK + hardware (Gradio + ZeroGPU for a public demo by default).
3. Create the Space, then write app.py, requirements.txt, and a README with YAML frontmatter (title, sdk, app_file, pinned).
4. Deploy by pushing to the Space repo (or hf upload ... --type space).
5. Debug: hf spaces logs <ns>/<space> and hf spaces restart <ns>/<space>.

When to use

Hosting a demo, a public app, or an interactive report for a model.

Pitfalls

• ZeroGPU is Gradio + PyTorch only; a non-PyTorch main model needs dedicated GPU (or a Static Space with in-browser inference).
• Free accounts: 2 ZeroGPU Spaces max. Beyond that needs PRO or a community grant.

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Adapted from huggingface/skills (Apache-2.0).