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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