Run Hugging Face spaces on Runpod!

Hugging Face Spaces offer interactive demos for AI models, but users seeking more computing power or to run models in their own environment can now deploy these spaces using Docker, enabling them to be hosted on platforms like Runpod to utilize powerful GPUs. This guide details the process of deploying Kokoro TTS, a text-to-speech model, from Hugging Face to Runpod using Gradio, a Python library for creating user-friendly interfaces for machine learning models. It walks through setting up a Docker image, generating an access token from Hugging Face, configuring a template on Runpod, and deploying a pod with GPU support. This deployment allows users to run models with greater flexibility and access to more robust hardware, demonstrating that this method can be applied to virtually any Hugging Face Space, making it possible to operate more demanding models than is feasible directly on Hugging Face.