Adrian Rosebrock tutorial in which he will present a simple method to take a Keras model and deploy it as a REST API. The examples covered in this post will serve as a template/starting point for building your own deep learning APIs.
The Keras library is an amazing tool that allows us to launch deep learning models with relative ease. This Python’s high-level package can be used on top of a GPU installation of either TensorFlow or Theano.
The post’s focus is on:
- How to (and how not to) load a Keras model into memory so it can be efficiently used for inference
- How to use the Flask web framework to create an endpoint for our API
- How to make predictions using our model, JSON-ify them, and return the results to the client
- How to call our Keras REST API using both cURL and Python
Detailed code is provided in the repository with explanation and charts. It is meant to be used as a template for your own Keras REST API. ßGood read!
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