Deploying a Large Image Classification Model on Heroku

Discover how to successfully deploy a large image classification model on Heroku despite slug size restrictions by using TensorFlow Lite for model optimization.
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Deploying a Large Image Classification Model on Heroku

Deploying large machine learning models can be tricky, especially when using platforms like Heroku that have limitations on slug size. You may find yourself facing challenges while trying to deploy your model, just like one user encountered. In this guide, we will explore the steps to take when your model size exceeds the allowed limit and discuss an effective solution using TensorFlow Lite (TFLite).

The Challenge

Imagine you’ve built a sophisticated image classification model using FastAPI and you're ready to share it with others via Heroku. Everything seems perfect until you hit a roadblock: your model is too large to deploy! Our example model weighs in at 859 MB, and Heroku has a slug size limit of 500 MB. Additionally, GitHub LFS (Large File Storage) cannot be used on Heroku by default, complicating the deployment further.

At this point, many developers would feel stuck. Fortunately, there are ways to tackle this problem head-on!

Initial Approach to Load the Model

Initially, the solution was to simply download the model from a GitHub release each time the app starts. Here’s a quick look at the code that was used to try and accomplish this:

[[See Video to Reveal this Text or Code Snippet]]

While this approach worked to some extent, it resulted in a 503 Service Unavailable error. This error indicates that the model was not being loaded properly, causing the application to fail. The old axiom "you never get a second chance to make a first impression" also rings true in technology: a smooth initial operation is critical for user experience.

A More Effective Solution: Model Size Reduction

To avoid deployment issues caused by model size, the alternative solution is to reduce your model size effectively. This is where TensorFlow Lite comes in handy. It allows you to convert your Keras model to a more compact format, which saves valuable space. Here’s how you can do it:

Step-by-Step Guide

Convert the Model: Use the TFLiteConverter to convert your Keras model into TensorFlow Lite format optimized for size.

[[See Video to Reveal this Text or Code Snippet]]

Successful Deployment: The size of the model was dramatically reduced to 72 MB. This smaller model fits comfortably within Heroku’s slug size constraints, enabling a smooth deployment of the FastAPI application.

Conclusion

By transforming your large model with TensorFlow Lite, you not only conquer the slug size limitations of Heroku but also enhance the overall performance of your application. This approach ensures that your API is ready and capable of handling user requests without running into errors.

Next time you face limitations with large models, remember this effective strategy: reduce the size and optimize for deployment. Happy coding!

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