If you spend any of your time dealing with the cloud native world, you’ve probably already heard about Kubeflow. It’s something we’ve been playing with since we first began to explore the possibility of running Tensorflow in a distributed way. That was quite some time ago. Since then, Kubeflow has rapidly evolved, so that it now includes dozens of machine learning (ML) frameworks. The frameworks allow for the training and serving of all kinds of machine learning models.

In our last last entry in the distributed TensorFlow series, we used a research example for distributed training of an Inception model. In this post we’ll showcase how to do the same thing on GPU instances, this time on Azure managed Kubernetes - AKS deployed with Pipeline. As you may remember from our previous post that the first thing to consider when running distributed Tensorflow models is whether you have shared storage space available.

Last time we discussed how our Pipeline PaaS deploys and provisions an AWS EFS filesystem on Kubernetes and what the performance benefits are for Spark or TensorFlow. This post is gives: An introduction to TensorFlow on Kubernetes The benefits of EFS for TensorFlow (image data storage for TensorFlow jobs) Pipeline uses the kubeflow framework to deploy: A JupyterHub to create & manage interactive Jupyter notebooks A TensorFlow Training Controller that can be configured to use CPUs or GPUs A TensorFlow Serving container Note that Pipeline also has default Spotguides for Spark and Zeppelin to help support your datascience experience

At Banzai Cloud we provision different frameworks and tools like Spark, Zeppelin and, most recently, Tensorflow, all of which run on our Pipeline PaaS (built on Kubernetes). One of Pipeline’s early adopters runs a Tensorflow Training Controller using GPUs on AWS EC2, wired into our CI/CD pipeline, which needs significant parallelization for reading training data. We’ve introduced support for Amazon Elastic File System and made it publicly available in the forthcoming release of Pipeline.