Let’s define the requirements for this little project: Transfer learning of a convolutional neural network (CNN).Some simple docker, docker-compose and docker-machine commands.Simple data inserting and querying with MongoDB.Setup of a MongoDB as Persistence container for training meta-data and file storage for models.Setup of a multi-container Docker application for training a neural network with docker-compose.Usage of a tensorflow docker image in your Dockerfile.Setup of GPU empowered cloud instance on AWS from your command line with docker-machine.The source code for this project is available on Github. I am using a standard technology stack for this project with Python, Tensorflow and Keras. In this case study, I want to show you how to train a shallow neural network on top of a deep InceptionV3 model on CIFAR-10 images within a Docker container on AWS. This time I want to get my hands dirty with a practical example.
My last article Example Use Cases of Docker in the Data Science Process was about Docker in Data Science in general. How to train a shallow neural network on top of an InceptionV3 model on CIFAR-10 within Docker on an AWS GPU-instance
Practical example of Training a Neural Network in the AWS cloud with Docker
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