huggingface-spaces

# Hugging Face Spaces Hugging Face Spaces host machine-learning applications. There are 1M+ today; each Space is a git repo. This skill covers creating, building, debugging, and maintaining them. ## 0. Getting ready Before anything else: 1. Check the `hf` CLI is installed: `which hf`. If not, `pip install -U huggingface_hub`. 2. Check the user is logged in: `hf auth whoami`. If not, ask them to run `! hf auth login` in this session — they'll need a write-scoped token from https://huggingface.co/settings/tokens. 3. Note `whoami`'s `canPay` and `isPro` flags — they gate hardware choices below. The `hf-cli` skill teaches an agent every `hf` command and is the recommended companion to this one. Install it with `hf skills add hf-cli` (add `--claude --global` to install for Claude Code as well, user-level). ## 1. What a Space is A Space is a git repo with three possible SDKs: - **Gradio** — most Spaces. Python, fast iteration, supports ZeroGPU. - **Docker** — arbitrary container. Use when you need a non-Python stack or a pre-built template (Streamlit, Argilla, Shiny, etc. — full list at https://huggingface.co/docs/hub/spaces-sdks-docker). Does **not** support ZeroGPU. - **Static** — plain HTML, or a React/Svelte/Vue project built at deploy time. Use for in-browser ML (transformers.js / WebGPU / WebAssembly / onnxruntime-web), project pages, interactive reports, or Spaces that orchestrate other Spaces. No hardware needed. ### Hardware tiers Free, no creator cost: **`cpu-basic`** and **`zero-a10g`** (ZeroGPU). Static Spaces are also free and don't need hardware. **`cpu-basic`** — 2 vCPU / 16 GB. For data viz, API-proxy Spaces, small CPU-bound models. **ZeroGPU (`zero-a10g`)** — dynamic, per-request GPU allocation on NVIDIA RTX PRO 6000 Blackwell (sm_120). Two sizes: `large` (half MIG, 48 GB, 1× quota) and `xlarge` (full, 96 GB, 2× quota). Free for the Space creator; Space visitors consume their own daily quota (~5 min free / 40 min Pro / 60 min Enterprise). **Gradio-only**, **PyTorch-first**. Requires the creator to be on a PRO / Team / Enterprise plan. **Dedicated GPU** (T4, L4, A10G, L40S, A100, H200) — billed to the Space creator by the hour. List + pricing: `hf spaces hardware`. Only the creator can attach these, and only if `canPay=True`. Use when ZeroGPU genuinely doesn't fit — non-PyTorch main model with heavy init, very-large-model long-context inference, etc. If a non-PRO user has a use case that wants ZeroGPU, you can still build it: create a `cpu-basic` Space, code the app for ZeroGPU, push, then request a community grant. See [`references/grants.md`](references/grants.md). For the authoritative reference: https://huggingface.co/docs/hub/spaces-overview ## 2. Look for an existing demo first Before deciding how to build anything, search for prior art: ```bash hf spaces search "" --sdk gradio --limit 10 ``` If someone has built a similar Space, read its `app.py` and `requirements.txt` — that gives you the w