This article we will do a bit of a review of the technology stack required to enable this as well as the logistics behind setting it all up and operating against it. The solution uses Azure Container Services, Docker, Kubernetes, Azure Storage, Jupyter, Tensor Flow and Tensorboard. WOW! That is a lot of technology; so we won’t do a deep dive how to but rather some pointers on how to get provisioned and then the high level process on how to use it.
So this blog post is to get you operational with Docker, image and volume management with a pivot towards scientific computing and tensor flow. So I am working on building a Jupyter Notebook for the local mahcine learning meetup to learn the ins and outs of Tensor Flow and deploy this thing up to Azure. Part of getting this to work is not only managing the Docker Containers, but also the data on the volumes so when we deploy up to Azure and somebody opens up the notebook it comes pre-loaded with all the necessary tutorial data.