When
10:45 AM Sunday
Where
Town Square A
Silicon Valley Code Camp : October 19 & 20, 2019session
Using Kubernetes for Machine Learning Frameworks
This talk will explain why and how Kubernetes is well suited for Machine Learing, deploying your ML 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.
About This Session
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.
Time: 10:45 AM Sunday Room: Town Square A
The Speaker(s)
Arun Gupta
Vice President, Developer Advocacy , Couchbase
Arun Gupta is a Principal Open Source Technologist at Amazon Web Services.