Authors: Yan Zhang, Mathew Salvaris, and Fidan Boylu Uz

Deploy Deep Learning CNN on Kubernetes Cluster with GPUs - AML version

Overview

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 throught Azure Machine Learning (AML). The tutorials cover how to deploy models from the following deep learning frameworks:

• Keras (TensorFlow backend)
• Pytorch

For each framework, we go through the following steps:

• Create an AML Workspace
• Model development where we load the pretrained model and test it by using it to score images
• Develop the API that will call our model
• Building the Docker Image with our REST API and model and testing the image
• Creating our Kubernetes cluster and deploying our application to it
• Testing the deployed model
• Testing the throughput of our model
• Cleaning up resources

Design

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. The application flow for the deep learning model is as follows:

1. Deep learning model is registered to AML model registry.
2. AML creates a docker image including the model and scoring script.
3. AML deploys the scoring image on Azure Kubernetes Service (AKS) as a web service.
4. The client sends a HTTP POST request with the encoded image data.
5. The web service created by AML preprocesses the image data and sends it to the model for scoring.
6. The predicted categories with their scores are then returned to the client.

NOTE: The tutorial goes through step by step how to deploy a deep learning model on Azure; it does not include enterprise best practices such as securing the endpoints and setting up remote logging etc.

Deploying with GPUS: For a detailed comparison of the deployments of various deep learning models, see the blog post here which provides evidence that, at least in the scenarios tested, GPUs provide better throughput and stability at a lower cost.

Setup

Please find out the Prerequisites and Setup procedures for different networks:

• Keras (TensorFlow backend)
• Pytorch

Contributing

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