huggingface-zerogpu

What problem does it solve?

This Skill provides guidelines and best practices for developing and running Gradio Spaces on Hugging Face Spaces ZeroGPU hardware, optimizing performance and managing resource usage effectively.

Core Features & Use Cases

• ZeroGPU Best Practices: Offers detailed rules and patterns for ZeroGPU hardware, including CUDA usage, memory bandwidth, and compute constraints.
• ML Demo Guidelines: Assists in coding ML demos for ZeroGPU hardware, covering @spaces.GPU decorator usage, model instantiation, and GPU-bound function decorations.
• Concurrency and Error Handling: Provides guidance on handling concurrency, process isolation, and common errors related to ZeroGPU, ensuring smooth execution.
• Use Case: A developer wants to create a Gradio Space for a text generation model that runs on ZeroGPU. This Skill helps them understand how to instantiate models, manage durations and quotas, and handle potential concurrency issues.

Quick Start

Implement ZeroGPU optimizations by adding @spaces.GPU to your model inference function and configure your duration based on expected workload.