Scaling Up Pytorch with GPU Cluster Computing
In this session, attendees will get a short overview of GPU computing and why it is valuable for deep learning tasks, and then will be walked through an example of using GPU cluster computing with parallelization to conduct a high volume image classification task with Resnet50 in Pytorch. We'll discuss the advantages and drawbacks to using GPU clusters, including speed, cost, and ease of use.
Stephanie Kirmer is a Senior Data Scientist at Saturn Cloud, a platform enabling easy to use parallelization and scaling for Python with Dask. Previously she worked as a DS Tech Lead at Journera, a travel data startup, and Senior Data Scientist at Uptake, where she developed predictive models for diagnosing and preventing mechanical failure. Before joining Uptake, she worked on data science for social policy research at the University of Chicago and taught sociology and health policy at DePaul University.