No
Yes
View More
View Less
Working...
Close
OK
Cancel
Confirm
System Message
Delete
My Schedule
An unknown error has occurred and your request could not be completed. Please contact support.
Scheduled
Scheduled
Wait Listed
Personal Calendar
Speaking
Conference Event
Meeting
Interest
There aren't any available sessions at this time.
Conflict Found
This session is already scheduled at another time. Would you like to...
Loading...
Please enter a maximum of {0} characters.
{0} remaining of {1} character maximum.
Please enter a maximum of {0} words.
{0} remaining of {1} word maximum.
must be 50 characters or less.
must be 40 characters or less.
Session Summary
We were unable to load the map image.
This has not yet been assigned to a map.
Search Catalog
Reply
Replies ()
Search
New Post
Microblog
Microblog Thread
Post Reply
Post
Your session timed out.
This web page is not optimized for viewing on a mobile device. Visit this site in a desktop browser to access the full set of features.
2019 GTC San Jose

L9136 - NASA GeneLab Repository Deep-learning Based Accelerated Workflow and Data Curation

Session Speakers
Session Description

Prerequisites:
Attendees should be able to connect to the DLI / GTC Platform Site from their personal laptop. Our training Jupyter notebook illustrates the code along with the steps mentioned for the dataset, driven by Docker so that you don't have to install everything from scratch. An understanding of data science will be needed to gain the most out of the lab.

In addition, it will be helpful but not required to have some prior knowledge of Jupyter notebooks, and it will be a plus if they have knowledge of the GOAi platform to perform analysis and data extraction from MapD/OmniSci, understand preprocessing it in Pygdf/Pandas, analyze nodes in Graphistry, train the model to make clusters with H2O's KMeans, and store the results back in MapD/OmniSci Core.


How To Prepare: All attendees must bring their own laptop and charger. We recommend using a current version of Chrome, Firefox, or Safari for an optimal experience. Create an account at http://courses.nvidia.com/join before you arrive.

Learn how we accelerated the NASA GeneLab dataset in the Kansas University/Loma Linda projects. The dataset, available at NASA Genelab Data Repository, contains current research on mice subjected to a physiological model of spaceflight. You’ll learn how to load, analyze, pre-process, and visualize data, as well as how to do predictive modeling. By accelerating machine learning and deep learning research for NASA and KSU, GOAi provides an open source alternative that reduces research clock time and computational cost. Utilizing a GPU-Accelerated pipeline, scientists can focus on reviewing more data from the research. We’ll explain why we believe that combining different datasets in one analytics platform shifts the focus towards further analysis and deeper insights. We prepared a blog and Jupyter notebook (tested by NVIDIA’s DLI) for this lab.


Additional Information
Accelerated Data Science
AI/Deep Learning Research, Accelerated Data Science, Genomics/Bioinformatics
Healthcare & Life Sciences, Higher Education / Research, Software
Beginner technical
Instructor-Led Training
2 Hours
Session Schedule