Kubernetes Best Practices

Chapter 14. Running Machine Learning in Kubernetes

The age of microservices, distributed systems, and the cloud has provided the perfect environmental conditions for the democratization of machine learning models and tooling. Infrastructure at scale has now become commoditized, and the tooling around the machine learning ecosystem is maturing. It just so happens that Kubernetes is one of the platforms that has become increasingly popular among data scientists and the wider open source community as the perfect environment to enable the machine learning workflow and life cycle. In this chapter, we will cover why Kubernetes is a great place for machine learning and provide best practices for both cluster administrators and data scientists alike on how to get the most out of Kubernetes when running machine learning workloads. Specifically, we focus on deep learning rather than traditional machine learning because deep learning has fast become the area of innovation on platforms like Kubernetes.

Why Is Kubernetes Great for Machine Learning?

Kubernetes has quickly become the home for rapid innovation in deep learning. The confluence of tooling and libraries such as TensorFlow make this technology more accessible to a large audience of data scientists. What makes Kubernetes such a great place to run your deep learning workloads? Let’s cover what Kubernetes provides:

Ubiquitous

Kubernetes is everywhere. All of the major public clouds support it, and there are distributions for private ...