This presentation was recorded at GOTO Chicago 2019. #gotocon #gotochgo
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Arun Gupta - Principal Open Source Technologist at AWS and CNCF Board Member
ABSTRACT
Kubernetes provides isolation, auto-scaling, load balancing, flexibility and GPU support. These features are critical to run computationally and 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 training and running your machine learning models in production. 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.
Attendees will learn training, massaging and inference phases of setting up a Machine Learning framework on Kubernetes. Attendees [...]
Download slides and read the full abstract here:
gotochgo.com/2019/sessions/696
RECOMMENDED BOOKS
Holden Karau, Trevor Grant, Boris Lublinsky, Richard Liu & Ilan Filonenko • Kubeflow for Machine Learning • amzn.to/3JVngcx
Liz Rice • Container Security • amzn.to/3oU4iJe
Liz Rice • Kubernetes Security • www.oreilly.com/library/view/...
Brendan Burns, Joe Beda & Kelsey Hightower • Kubernetes: Up and Running • amzn.to/3wrtwlp
John Arundel & Justin Domingus • Cloud Native DevOps with Kubernetes • amzn.to/3hKZvI5
Kasun Indrasiri & Sriskandarajah Suhothayan • Design Patterns for Cloud Native Applications • amzn.to/3yCFxWE
Michael Hausenblas & Stefan Schimanski • Programming Kubernetes • amzn.to/3qTvKch
Alexander Raul • Cloud Native with Kubernetes • amzn.to/3yw9ckc
Nigel Poulton • The Kubernetes Book • amzn.to/3dW8ViU
Marko Luksa • Kubernetes in Action • amzn.to/3dXk2Im
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#MachineLearning #k8s #kubernetes #TensorFlow #ApacheMXNet #Pytorch
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