In this repository there are a number of tutorials in Jupyter notebooks that have step-by-step instructions on how to deploy a pretrained deep learning model on a GPU enabled Kubernetes cluster. The tutorials cover how to deploy models from the following deep learning frameworks:

• TensorFlow
• Keras (TensorFlow backend)
• Pytorch (Coming soon)

For each framework we go through 7 steps:

• Model development where we load the pretrained model and test it by using it to score images
• Developing the interface our Flask app will use to load and call the model
• Building the Docker Image with our Flask REST API and model
• Testing our Docker image before deployment
• Creating our Kubernetes cluster and deploying our application to it
• Testing the deployed model
• Testing the throughput of our model

The application we will develop is a simple image classification service, where we will submit an image and get back what class the image belongs to.

If you already have a Docker image that you would like to deploy or you simply want to use the image we built you can skip the first four notebooks.

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