fast.ai v3

Why deploy Fastai v3 on Render?

Fastai-v3 is a starter template for deploying fast.ai deep learning models as web applications on Render. It solves the problem of getting trained fast.ai models into production by providing a Docker-based deployment setup with a simple web interface for image classification.

This template provides a production-ready Docker configuration for serving fast.ai models, with the Starlette web server, async model downloading, and proper dependency management already wired together. Instead of debugging CUDA dependencies, configuring async inference endpoints, or setting up model caching logic yourself, you get a working deployment in one click. Render's native Docker support means your container builds and deploys automatically on every git push, with zero infrastructure configuration required.

What you can build

After deploying, you'll have a web service that can classify images using a fast.ai model, with a simple interface for uploading and testing predictions. The included example identifies bear species from photos, but you can swap in your own trained model. You get a working inference endpoint without configuring servers or containers yourself.

Key features

• fast.ai model deployment: Pre-configured to deploy trained fast.ai models as a web service with minimal setup.
• Docker containerization: Includes Dockerfile for local testing and consistent deployment environment across development and production.
• Render platform integration: Designed specifically for one-click deployment to Render's hosting platform with accompanying documentation.
• Image classification ready: Ships with a working bear image classifier example demonstrating end-to-end model serving.
• Local development server: Runs on port 5000 locally via Docker for testing inference before deployment.

Use cases

• Student deploys bear classifier from fast.ai course to share online
• Data scientist quickly hosts trained image model for client demo
• Developer tests fast.ai model locally with Docker before production
• Hobbyist launches pet breed identifier without managing server infrastructure

Next steps

1. Open https://fastai-v3.onrender.com in your browser and upload a bear image — You should see a classification result showing the bear type (grizzly, black, or teddy) with a confidence percentage within a few seconds
2. Test the model with a non-bear image to verify error handling — You should see either a low confidence score or an incorrect classification, confirming the model only recognizes bears
3. Upload 3-5 different bear images of varying quality and angles — You should see consistent, accurate classifications proving the model is responding reliably under normal usage

Resources

• fast.ai official site