Using Kubernetes for Machine Learning Frameworks
Arun Gupta at GOTO Chicago 2019
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 ...
Arun Gupta - Principal Open Source Technologist at AWS and CNCF Board Member
Download slides and read the full abstract here:
https://gotochgo.com/2019/sessions/696
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 ...
Arun Gupta - Principal Open Source Technologist at AWS and CNCF Board Member
Download slides and read the full abstract here:
https://gotochgo.com/2019/sessions/696