Machine Learning and Deep Learning on GPUs
Booth #203



SESSION S7652

TUESDAY, MAY 9
4:00 - 4:25 PM
ROOM 211A

Blending the Worlds of Machine Learning and Deep Learning to Make the Fastest AI Platform on GPUs

SriSatish Ambati

Arno Candel

Deep learning algorithms have benefited greatly from the recent performance gains of GPUs. However, it has been unclear whether GPUs can speed up data manipulations such as joins and aggregations and machine learning algorithms such as generalized linear modeling, random forests, gradient boosting machines, and clustering. H2O.ai, the leading open source AI company, is bringing the best-of-breed data science and machine learning algorithms to GPUs, not just deep learning. In addition, H2O.ai is porting data.table to GPUs, already the fastest open-source columnar data frame library and the world's fastest implementation of the sort algorithm. This powerful combination will enable the fastest data science and machine learning pipelines for AI transformations for applications such as IoT time series, fraud prevention, anomaly detection, and many more. We'll demonstrate benchmarks for the most common algorithms relevant to enterprise AI and showcase performance gains as compared to running on CPUs.

LAB L7113

THURSDAY, MAY 11
2:00 - 4:00 PM
ROOM LL21A

Train Deep Learning Models Using H2O Deep Water

Arno Candel

Magnus Stensmo

Wen Phan

In this lab, you will learn how to train deep learning models for with supervised (image classification) and unsupervised (retail similarity) learning using H2O Deep Water platform on a GPUs. This hands-on lab will be done primarily in Python and H2O's native interface Flow and will use TensorFlow, MXNet, and Caffe. You will use pre-defined networks such as ResNet V2 along with custom networks. You will also compare and ensemble deep learning with other machine learning algorithms such as GLM and GBM and deploy models for inference.

Prerequisites: Attendees should be familiar with machine learning and neural network basics.

This lab utilizes GPU resources in the cloud, you are required to bring your own laptop.