HF Spaces Best Practices Advanced

Build production-quality ML demos that load fast, handle concurrent users, and run reliably. This lesson covers performance optimization, secrets management, GPU best practices, and tips for creating engaging Spaces.

Performance Optimization

• Use model caching: Decorate model loading functions with @st.cache_resource (Streamlit) or load models at module level (Gradio) to avoid reloading on every request
• Use quantized models: Load 4-bit or 8-bit quantized models to reduce memory usage and speed up inference
• Use the Inference API: For large models, use the HF Inference API instead of loading models locally - no GPU needed
• Lazy loading: Only load models when needed, not at startup time
• Cache examples: Pre-compute example outputs to give users instant results
• Optimize images: Resize input images before processing to reduce inference time

Secrets Management

GPU Management

• Start with CPU: Develop and test on free CPU tier before upgrading to GPU
• Use sleep settings: Configure auto-sleep timeouts to reduce GPU costs when not in use
• Monitor VRAM: Check GPU memory usage and optimize batch sizes accordingly
• Use ZeroGPU: Hugging Face offers ZeroGPU for free GPU access on popular Spaces - apply through the Spaces settings
• Offload to CPU: Keep model weights on CPU and only move to GPU during inference

Building Engaging Demos

• Add a clear title and description explaining what the demo does
• Provide examples so users can try the demo immediately without preparing inputs
• Show loading states - use progress bars or spinners during inference
• Handle errors gracefully with user-friendly error messages
• Add a README with screenshots, usage instructions, and links to the model card
• Pin your best Spaces to your profile to showcase your work

Common Issues & Solutions