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Introduction Deploying a Gradio application on a server allows you to share your web-based machine learning demos with a wider audience. In this guide, we'll walk you through the process of deploying a Gradio app using Render, a popular platform for hosting web applications. Prerequisites Before we begin, make sure you have the following: A Gradio application developed and tested locally. A GitHub account with your Gradio app code pushed to a repository. A Render account. Steps to Deploy 1. Prepare Your Gradio App First, ensure your Gradio app runs correctly on your local machine. Use the requirements.txt file to manage your dependencies and a virtual environment to isolate your app. # Activate virtual environment and install dependencies source venv/bin/activate pip install -r requirements.txt # Run your Gradio app locally uvicorn run:app --reload Check the local deployment to confirm everything works as expected. 2. Push Your Code to GitHub Commit your code changes and push...

In the world of serverless computing, AWS Lambda stands out as a powerful platform for deploying applications quickly and efficiently. FastAPI, with its high-performance capabilities and easy-to-use framework, is a perfect match for building robust APIs. In this article, we'll walk through the process of deploying a FastAPI application on AWS Lambda, step by step. Setting Up the Environment First, ensure you have Visual Studio Code open. Begin by installing the necessary dependencies: FastAPI, uvicorn, and Mangum, which serves as the handler for AWS Lambda. In your terminal, execute: pip install fastapi uvicorn mangum Next, generate a requirements.txt file to manage dependencies: pip freeze > requirements.txt Now, create the main application file, main.py , where we'll define our FastAPI application along with the necessary handlers: from fastapi import FastAPI from mangum import Mangum app = FastAPI() handler = Mangum(app) @app.get("/") async def hello...

A Collaboration to Empower AI Builders Hugging Face, a leading language AI company, has announced a significant integration with Google Cloud, making it easier for developers to deploy foundation models securely and reliably. The new feature, "Deploy on Google Cloud," offers a seamless experience for developers to harness the power of open LLMs (Large Language Models) within their own Google Cloud environment. This collaboration simplifies the process of building production-ready Generative AI applications, removing the burden of managing infrastructure and servers. With just a few clicks, developers can now deploy thousands of open models as API Endpoints on Vertex AI or Google Kubernetes Engine (GKE), benefiting from dedicated configurations and assets provided by Hugging Face. How it Works: A Seamless Deployment Journey The deployment process is straightforward and user-friendly, whether starting from the Hugging Face Hub or directly within the Google Cloud Console. Fr...