huggingface-spaces

原文涵盖的主题

• Hugging Face Spaces
• 0. Getting ready
• 1. What a Space is
• Hardware tiers
• 2. Look for an existing demo first
• 3. Decide SDK and hardware
• Sourcing the model
• 4. Create the Space

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. Before anything else: 1. Check the hf CLI is installed: which hf. If not, pip install -U huggingfacehub. 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. A free (isPro=False) account can only host Static Spaces and up to 2 ZeroGPU Spaces. 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). 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, e

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