Using Kubernetes for Machine Learning Frameworks
Kubernetes provides isolation, auto-scaling, load balancing, flexibility and GPU support. These features are critical to run computationally, data intensive and hard to parallelize machine learning models. Declarative syntax of Kubernetes deployment descriptors make it easy for non-operationally focused engineers to easily train machine learning models on Kubernetes. This talk will explain why and how Kubernetes is well suited for single and multi node distributed training, deploying your machine learning models in production and setting up visualization tools like TensorBoard for monitoring. Specifically it will show how to setup a variety of open source machine learning frameworks such as TensorFlow, Apache MXNet and Pytorch on a Kubernetes cluster. The attendees will learn distributed training, massaging and inference phases of setting up a Machine Learning framework on Kubernetes. Attendees will leave with a GitHub repo of fully working samples.
Arun Gupta is a Principal Open Source Technologist at Amazon Web Services. He has built and led developer communities for 12+ years at Sun, Oracle, Red Hat and Couchbase. He has deep expertise in leading cross-functional teams to develop and execute strategy, planning and execution of content, marketing campaigns, and programs. Prior to that he led engineering teams at Sun and is a founding member of the Java EE team. He has extensive speaking experience in more than 40 countries on myriad topics and is a JavaOne Rock Star for four years in a row. Gupta also founded the Devoxx4Kids chapter in the US and continues to promote technology education among children. A prolific blogger, author of several books, an avid runner, a globe trotter, a Docker Captain, a Java Champion, a JUG leader, NetBeans Dream Team member, he is easily accessible at @arungupta.
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