Build, deploy, and maintain applications on Hugging Face Spaces — Gradio / Docker / Static SDKs, ZeroGPU and dedicated hardware, model loading, debugging, buckets, inference providers, community grants. Use whenever the user asks to create or host an app on Hugging Face, port
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Build, deploy, and maintain applications on Hugging Face Spaces — Gradio / Docker / Static SDKs, ZeroGPU and dedicated hardware, model loading, debugging, buckets, inference providers, community grants. Use whenever the user asks to create or host an app on Hugging Face, port
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huggingface-spaces.SKILL.md
---name: huggingface-spaces
description: Build, deploy, and maintain applications on Hugging Face Spaces — Gradio / Docker / Static SDKs, ZeroGPU and dedicated hardware, model loading, debugging, buckets, inference providers, community grants. Use whenever the user asks to create or host an app on Hugging Face, port code onto ZeroGPU, fix a Space that won't build or run, or otherwise work with `hf spaces …`, `@spaces.GPU`, Space README frontmatter, or the `spaces` Python package.
---# 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, run `hf auth login` — it prints a URL and a one-time code; ask the user to open the URL and enter the code, then login completes automatically (OAuth, no token needed). Alternatively, pass a write-scoped token from https://huggingface.co/settings/tokens with `--token`.
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 "<model name or task>" --sdk gradio --limit 10
```
If someone has built a similar Space, read its `app.py` and `requirements.txt` — that gives you the working pattern. Saves a lot of blind iteration. Mention to the user what you found before committing to an approach.
## 3. Decide SDK and hardware
Follow the user's explicit request first. If they were vague:
- **Default for a public ML demo**: Gradio + ZeroGPU. Use this unless something below applies.
- **The model's only inference path is non-PyTorch** (ONNX / TF / JAX / vLLM as the MAIN model, with heavy init): dedicated GPU.
- But: marginal non-torch tools (a small ONNX preprocessor, a TF utility) inside a torch-main pipeline are fine on ZeroGPU. The hijack only patches torch; init the non-torch lib inside `@spaces.GPU` and pay the short per-call init cost.
- **Tiny / CPU-bound model, or API-proxy Space**: `cpu-basic` (`hardware`-free isn't applicable to Gradio).
- **Browser-side ML or project page**: Static.
- **Container with non-Python stack**: Docker.
### Sourcing the model
- **GitHub repo** — clone locally to read structure. If it already has a Gradio demo, the minimal viable path is to adapt it onto ZeroGPU (see [`references/zerogpu.md`](references/zerogpu.md)). Otherwise: read the README + inference code, prefer the PyTorch path, estimate VRAM (bf16 ≈ `params_B × 2` GB; 48 GB fits ≤24B params at bf16, or much larger with quantization — see [`references/zerogpu.md`](references/zerogpu.md) for quantization on ZeroGPU).
- **HF model repo** — read its README, follow any linked GitHub.
- **Paper / blog post** — look for an official or unofficial implementation. Don't reimplement unless trivial or the user explicitly asks.
If the model genuinely won't fit, check **Inference Providers** as an alternative: see [`references/inference-providers.md`](references/inference-providers.md). This avoids hosting the model at all.
## 4. Create the Space
```bash
hf repos create <namespace>/<name> --type space --space-sdk <gradio|docker|static> \
[--flavor zero-a10g|cpu-basic|<paid-flavor>] \
[--secrets KEY=val] [--env KEY=val] \
--public|--private|--protected \
--exist-ok
```
- `--space-sdk` is required.
- `--flavor` selects hardware. `zero-a10g` is the (legacy) identifier for ZeroGPU. Omit for `cpu-basic`. Run `hf spaces hardware` for the full paid list and pricing.
- Visibility: `--public` (anyone can view), `--private` (only you), `--protected` (app is reachable but git repo / Files tab is private).
- `--secrets KEY=val` becomes an environment variable inside the Space and is **not** visible to visitors. Use for API keys, gated-repo tokens (`HF_TOKEN=hf_…`), etc. Can also be set later via `hf spaces secrets set <id> KEY=val`.
- `--env KEY=val` is **visible to visitors** — use only for non-sensitive config (`GRADIO_SSR_MODE=false`, `PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True`, etc.).
> Note: `hardware:` in the README YAML is silently ignored — hardware is only set via `--flavor` at creation, or later via `hf spaces settings <id> --hardware <name>`.
## 5. Build the app
The Space now exists at `https://huggingface.co/spaces/<namespace>/<name>` but is empty.
### README.md frontmatter
Always required:
```yaml
---
title: ...
emoji: 🚀 # pick something representative
colorFrom: blue # red|yellow|green|blue|indigo|purple|pink|gray (only these)
colorTo: indigo
sdk: gradio # gradio | docker | static
sdk_version: 6.15.1 # latest stable unless you have a reason*
app_file: app.py # gradio only (docker / static use Dockerfile / index.html)
python_version: "3.12" # ZeroGPU officially supports 3.10.13 and 3.12.12
startup_duration_timeout: 30m # default; bump to 1h for big LLMs / heavy downloads
---
```
\* Default to the current latest stable, and **look up what that is** (`pip index versions gradio`, or the version a freshly-created Space defaults to) — the number above is a placeholder that goes stale, don't reuse it. Only pin older when the latest genuinely doesn't work for this Space: a custom component pins it, or you're adapting an existing demo and don't want to rewrite for 5.x→6.x breaking changes. If you need a 5.x, pick `5.50.0` (latest of the series; still supports custom components).
All frontmatter options: https://huggingface.co/docs/hub/spaces-config-reference
### Minimal ZeroGPU Gradio app
```python
import spaces # MUST come before torch / diffusers / transformers
Three rules — full treatment in [`references/zerogpu.md`](references/zerogpu.md):
1. **`import spaces` before torch / any CUDA-touching import.** It monkey-patches `torch.cuda.*`; once CUDA is initialized in the main process, it's too late.
2. **Load the model at module scope, `.to("cuda")` eagerly.** ZeroGPU intercepts the call, packs weights to disk, and streams them into VRAM on the first `@spaces.GPU` entry. Lazy loading inside the decorator costs every user.
3. **Decorate the function Gradio binds.** Estimate `duration` to the realistic worst case (smaller = higher queue priority and tighter quota check). For input-dependent runtime, pass a callable.
### Examples, docstrings, and MCP
- **Add `gr.Examples` whenever it makes sense** (the app takes input and representative inputs exist) — prefer the model/repo's own official examples. Keep example rows to the few inputs a user actually varies (prompt, image) and give the handler defaults for the rest (steps, seed, guidance) so a row is `["a prompt"]`, not a wall of knobs. Use `cache_examples=True, cache_mode="lazy"`. See [`references/gradio.md`](references/gradio.md).
- **Give every API-triggered function a docstring and type hints.** Each Gradio event handler is exposed over the API; the docstring + signature are what a caller — and the MCP tool schema — sees.
- **Launch with `demo.launch(mcp_server=True)`** (Gradio 5+) so the Space doubles as an MCP server: each API function becomes an MCP tool described by its docstring and hints.
### requirements.txt
Short version:
- **Do NOT list**: `gradio`, `spaces`, `huggingface_hub` (preinstalled and platform-managed; pinning them causes resolution failures or silently breaks the ZeroGPU runtime).
- **Do list if you use them**: `torchvision`, `torchaudio` (not preinstalled), plus everything else (`diffusers`, `transformers`, `accelerate`, `sentencepiece`, …).
- ZeroGPU only accepts torch `2.8.0`, `2.9.1`, `2.10.0`, `2.11.0`. Default to leaving torch unpinned (the runtime preinstalls the latest). Only pin when a dep forces it.
- For prebuilt CUDA-extension wheels (`flash_attn`, `xformers`, `pytorch3d`, `nvdiffrast`, `diff_gaussian_rasterization`, `torchmcubes`): use the prebuilt Blackwell wheels at `https://huggingface.co/datasets/multimodalart/zerogpu-blackwell-wheels/tree/main/wheels`. Full mapping + caveats in [`references/requirements.md`](references/requirements.md).
- **Docker**: https://huggingface.co/docs/hub/spaces-sdks-docker. Examples: `hf spaces list --filter docker`.
- **Static**: https://huggingface.co/docs/hub/spaces-sdks-static. For built SPAs, set `app_build_command: npm run build` and `app_file: dist/index.html` in frontmatter.
- **ZeroGPU specifics** (decorator semantics, sizing, AoTI, generators, concurrency, pickle / `gr.State` across the worker boundary): [`references/zerogpu.md`](references/zerogpu.md) — read this whenever the Space targets ZeroGPU.
## 6. Iterate on the Space, not locally
Try to build a release candidate from the user quest locally and push it — then use the live URL as your test loop. The Space environment is the only one that matters; do not try to test locally. `python3 -m py_compile app.py` is the maximum local check worth doing before pushing.
Push files with `hf upload <namespace>/<name> . --repo-type space`. **`--repo-type space` is required** — `hf upload` defaults to a *model* repo and will otherwise upload to (and silently create) a model repo of the same name. Add `--exclude "**/__pycache__/**"` so local bytecode caches aren't committed into the Space.
Once pushed, pick the cheapest update mechanism for each change — hot-reload for pure Python edits, `hf upload` for code-only files hot-reload can't touch, full rebuild only when `requirements.txt` / `Dockerfile` / README frontmatter actually changed. Full ladder + footguns (hot-reload poisoning factory reboot, runtime.sha lag, etc.) in [`references/debugging.md`](references/debugging.md).
## 7. Verify
Don't trust `RUNNING` alone — the app can be running but broken. Four steps, in order:
**A. Alive?** Stage + hardware:
```bash
hf spaces info <ns>/<name> --expand runtime
```
**B. Logs clean post-boot?** Read the run log to confirm startup finished without warnings or silent fallbacks:
```bash
hf spaces logs <ns>/<name> --tail 200
```
Look for model-load completion, no import warnings, no "falling back to CPU" / dtype downgrade messages, no `RUNNING` masking a half-broken app.
**C. API actually responds.** With logs still tailing in another terminal (`hf spaces logs <ns>/<name> --follow`), call the endpoint:
```python
from gradio_client import Client, handle_file
import os
c = Client("<ns>/<name>", token=os.environ["HF_TOKEN"], httpx_kwargs={"timeout": 600})
And look at the run log emitted during the call — silent fallbacks (model snapping to a different size, missing optional dep, dtype downgrade) only show up there.
Spaces are stateless — `/data` is wiped on restart. If the Space needs to persist user uploads, generations, logs, or interact with a long-lived store, mount a **bucket**:
hf spaces volumes set <ns>/<space> -v hf://buckets/<ns>/<bucket-name>:/data # read-write at /data
```
Buckets are paid storage; check `canPay` and confirm with the user. Full patterns (read-fast / write-durable, public bucket URLs, model-cache anti-pattern): [`references/buckets.md`](references/buckets.md).
## 9. When things break
Order of operations:
1. Read the logs: `hf spaces logs <id> --build --follow` (build error) or `hf spaces logs <id> --follow` (runtime error). Find the **first** error, not the last.
2. Grep [`references/known-errors.md`](references/known-errors.md) for the error string. Check if this is a known issue before trying your own fix — most common ZeroGPU / Gradio / dependency errors have a 1–2 line fix there.
3. Iterate using the cheapest rung from [`references/debugging.md`](references/debugging.md). The vast majority of issues resolve with log-reading + smoke-test loops; interactive dev mode + SSH is a heavy-hammer last resort.
If you solve an error that wasn't in the known-errors list, suggest the user PR it back to this skill so future runs benefit.
| **Iterate + debug**: logs, rung ladder, smoke testing (and dev mode + SSH as a last resort) | [`references/debugging.md`](references/debugging.md) |
| **Error-string lookup** — the single place for all error symptoms (Spaces, ZeroGPU, Gradio, deps) | [`references/known-errors.md`](references/known-errors.md) |
| `gr.Examples` (add when it makes sense), themes, custom HTML components, `gr.Server`, MCP server (`mcp_server=True`) | [`references/gradio.md`](references/gradio.md) |
| Persistent storage, public bucket URLs | [`references/buckets.md`](references/buckets.md) |
| Community grant requests (non-PRO needing ZeroGPU) | [`references/grants.md`](references/grants.md) |
| Provider proxy (zero-VRAM big LLM via Cerebras / Fireworks / Together / etc.) | [`references/inference-providers.md`](references/inference-providers.md) |
