Panel: Building the NRP Ecosystem with the Regional Networks on their Campuses;

1. Slide 1
February 9, 2023
Slide 1
February 9, 2023
Building the NRP Ecosystem with the Regional
Networks on their Campuses
4th National Research Platform Workshop
February 9, 2023

2. Slide 2
February 9, 2023
Slide 2
February 9, 2023
www.thequilt.net

3. Slide 3
February 9, 2023
www.thequilt.net
Regional Networks and Campus Partners with Nautilus
Nodes Connected to NRP
• CENIC
• NYSERNet
• OneNet
• GPN
• C-Light
• MREN
• OneNet
• FRGP
• PNWGP
• Eastern Research Platform - Edge,
OSHEAN, CEN, NYSERNet
• Merit
• Florida LambaRail
• AREON
• MOREnet
• Albuquerque GigaPoP
• KanREN
• Network Nebraska
• SD-REED

4. Slide 4
February 9, 2023
www.thequilt.net
Panelists
• Grant Scott, Director, Data Science and Analytics Masters Program,
University of Missouri
• Scotty Strachan, Principal Research Engineer, NevadaNet
• Eric Buckhalt, Senior Director, Technology Strategy, Architecture, &
Governance, Georgia Tech

5. Using NRP @ University of Missouri (Mizzou)
Grant Scott
Assistant Professor
• Computer Science, Computer
Engineering
• College of Engineering
Director,
• Data Science and Analytics, MS
Program
• Institute for Data Science and
Informatics
Provided Data Science and Computer
Science Learning Outreach using MU & NRP
Jupyter
• US Government Intelligence Agency
• USDA ARS Long-Term Agroecosystem
Research (LTAR) Data Managers
• Tutorials at Regional and International
conferences
https://scottgs.mufaculty.umsystem.edu/
Evangelizing the use of Nautilus / NRP for research
computing and STEM education

6. Using Nautilus For Research @ MU
Grant Scott with mentees Alex Hurt, Anes Ouadou
Case Study in Deep Learning for
Computer Vision
• Journal Publication Results in Weeks or
days
• Using State-of-the-Art Deep Learning
Computer Vision Models for Satellite
Imagery Data Sets
Assisting numerous other researchers
using ML & Deep Learning
• Protein Structure Prediction
• Robotics
• UAS Navigation & Vision
Tutorials & Workshops
• Great Plains Network Annual Meeting 2022
• Getting Started on Nautilus and Kubernetes
• SC 22 – GPN Booth
• Scaling Deep Learning with Nautilus
• Great Plains Network Annual Meeting 2023
• Getting Started on Nautilus and Kubernetes
• Scaling Deep Learning with Nautilus

7. Scaling Deep Learning with Kubernetes on Nautilus
• Using containerized model definition and list of
jobs
• Mounted persistent data storage to each pod
• Each GPU job produces an associated trained
model
• Automation currently performed via environment
variables and bash, but more sophisticated
methods in development
• Models are sync’d to Nautilus S3 bucket for later
use in evaluation or other ML applications
Dr. Alex Hurt
Nautilus
for
Accelerated
Research
Computing

8. Deep Learning on Nautilus: By the Numbers
Compute Intensive
• Containerized deep neural architectures: 9
• Datasets trained on: 3
• PyTorch Models Trained: 27
• Training Epochs Completed: 8,100
• Iterations of Training Completed: 30,088,125
• Number of Images Processed: 240,705,000
• Trainable Parameters Optimized:
1,730,368,875 (1.73 Billion)
Data Intensive
• Data loading: 415.8GB
• Neural Model Loading:
124,740 GB
• Wall-Clock: ~77 days
• Human Effort: <3 hours
Dr. Alex Hurt

9. Deep Learning on Nautilus: By the Numbers
Compute Intensive
• Containerized deep neural architectures: 4
• Datasets trained on: 1 – Forest Fire Mapping
• PyTorch Models Trained: 144
• Training Epochs Completed: 14,400
• Iterations of Training Completed: 515,550
• Number of Images Processed: 7,070,400
• Trainable Parameters Optimized: ~23 million
Optimizing time to science
discoveries
• Wall-Clock: ~22 days
• Human Effort: ~13 hours
Anes Ouadou (PhD cand.)

10. Nautilus Supports Jupyter Hub & Jupyter Lab
GEER Excels
• Basic Python Programming
• Used Nautilus Jupyter Lab
• Trainees authenticated with
GitHub accounts
Basic Programming Course
Parallel Programming for High-
Performance Computing
• Multi-Proc, Multi-Thread
• MPI, CUDA
Design and Analysis of Algorithms
• C/C++ Programming
• Analysis with Symbolic Python
• Run-time empirical analysis with Python
Computer Science Courses
Parallel Data Analytics
• Kubernetes
Data Science Courses

11. CC* Team: Great Plains Regional CyberTeam
• PI NSF Award OAC #1925681
• Helping the Great Plains region better leverage
collective cyberinfrastructure resources
• GPN Contributions to Nautilus and the NRP
• GPN developing a CC* Regional Compute proposal,
GP-ENGINE, to deploy NRP nodes into 8 locations
across 6 states
• Continuing our regional training and outreach at
GPN and member REN conferences
Proposed
GP-Engine Sites

12. Contributions to NRP @ MU
Grant Scott & Derek Anderson, non-NSF resources
Four - 4x A100 Servers
• 16x A100 GPUs
• 4x 1TB System RAM
• 4x 128 CPU Cores
• 110,592 CUDA Cores
• 1.2TB GPU RAM
• Online Feb 3, 2023
gpn-fiona-mizzou-1
gpn-fiona-mizzou-2
gpn-fiona-mizzou-3
gpn-fiona-mizzou-4
Working with Materials Scientists
• Physics constrained neural models, flat optics
development
• Automated characterization of nanoparticle
energetic reactions
Computer Vision for Geospatial & Remote
Sensing Analytics
• Wildfire Mapping from Space
• Drought and Flood Mapping from Space
• Illicit Deforestation Detection
• Indigenous peoples mapping for healthcare
delivery

13. Regional Perspective from NevadaNet
4NRP Workshop, La Jolla CA
RENs & NRPs Ecosystem Panel
February 2023
Scotty Strachan, Ph.D.
Nevada System of Higher Education
Principal Research Engineer
Mountain Systems Scientist
Co-PI, NSF-EPSCoR Track 1 + CC*’s
@ScottySci
sstrachan@nshe.nevada.edu

14. CLASSIC RESEARCH TECHNOLOGY USE & ADOPTION CURVE:
- Why is this the case? [match of immediate need+capability]
- How to make solutions widely useful? [culture shifts imminent]
LAB GROUP
PARETO
CURVE
RESOURCE
UNITS

15. CAMPUS
DISCOVERY
current state of Nevada
researchers?
[national platform
engagement not found]

16. Does it service a wide range of research
workflows?
Is it easy to get access? [onboarding flow]
What is the learning curve to productivity?
[grads]
What is the support model? [facilitation]
Is it a service commitment? [iteration
rate/maint req]
ECOSYSTEM COMPONENTS: VALUE PROPOSITION

17. Nevada example:
one way to overcome
friction is standardizing on
federated access,
(semi)automated
onboarding/provisioning…
…freeing up distributed
research engineers for
more high-touch
project/data facilitation
Coming now: a new
Science/Teaching DMZ
architecture
more UNLV, UNR, DRI
computing & data
infrastructure goes here
National computing & data
infrastructure goes here

18. MANY OF OUR REGIONAL CHALLENGES ARE GEOGRAPHIC
- I think a lot about the future of Wide-Area-Science [trust, community]
- Roles of nrp components in the IoWT [data-to-action, workflows]
- This will be driven by the regional networks [new mission areas]
RESEARCH FUTURES IN THE WEST

19. Georgia Tech / Southern Crossroads (SoX)
 Relatively new to the NRP….
 SoX is deploying nodes as part of CC* Area 2 regional awards that include several
HBCUs in Alabama and Georgia
 FIONA nodes purchased as DTNs or perfsonar test points and are considering
redeploying as nautilus nodes
 Goal is to enable research within our community and looking to assist faculty
unfamiliar with the platform
 SoX is looking for toolkits, demos, training we can share, invite, host, etc. for our
participants to help take advantage of the NRP
 SoX offers connectivity and space for those interested in hosting nodes