This session is part of a long research program aimed at scrutinizing how different components of server hardware and software stack affect deep learning model’s training time and cost. Even a very simple set up where one needs to choose a cloud VM with a single GPU secondary parameters (like RAM, CPU performance, disks, etc.) might have a significant impact on the cost and duration of the training and this impact will vary from one neural network architecture to another.
Speaker
Eugene Protasenko
CEO and Founder, RocketCompute
Redwood City, California United States
Track
Modern Enterprise IT
Performance Engineering and DevOps
IMPACT Session Video:
To view the video you must have a CMG membership. Sign up today!For existing members sign in here.
In his talk on "Impact of Sentiment Analysis on Improving Fake News Detection," Sanjaikanth E. Vadakkethil Somanathan Pillai addresses a critical issue exacerbated...
Find out moreSoftware for Humanity is a thought-provoking and immersive event that brings together software engineers, developers,...
Find out moreWhat is the traditional approach to performance monitoring on the Mainframe? Industry professionals know that...
Find out more