AI LAB and its use cases

1. © 2017 IBM Corporation
AI Lab using IBM Power 9 Systems
 Fastest AI Supercomputer built
using Power 9 systems
 95 AI Use cases around 24
Industries
 Workshops/Bootcamp
 Big Data, AI , HPC , Cloud and
Block Chain
Curriculums/Courses

2. © 2017 IBM Corporation
In the future, all
communication
between machines
and humans will be
powered by
enterprise systems
and operational AI

3. © 2017 IBM Corporation

4. © 2017 IBM Corporation
IBM is leading the way
IBM is teaming with universities, startups,
ISV’s and industries to help develop further
the impact of artificial intelligence for
solutions for real-world opportunities

5. © 2017 IBM Corporation5
Background and Motivation
The IBM AI Lab will play a major role in the research and
development commercial and industrial development of
emerging AI technologies
There is a strong need for research and development activity
in these domains:
– Encouraging academic-industry partnerships
– Cross-disciplinary and collaborative research
– Making AI accessible to non-technical business students
– Enabling faculty-technologist interaction and learning
– Enabling startups , ISVs and industries to use the platform
to innovate in ways that improve the World condition
.

6. © 2017 IBM Corporation
Technologies
and Partners
The AI Lab will include IBM
and other corporate
sponsors, coupled with open
source technologies to
accelerate results
6

7. © 2017 IBM Corporation7
CoE Charter and Objectives
1. Conduct research on rapidly advancing AI technologies
2. Enable and facilitate industry-academia partnerships in research and
development, and foster relationships through collaborative projects
3. Encourage cross-disciplinary research in applied computing, in critical
scientific and industrial domains, via research proposal submissions to
funding agencies
4. Provide a state-of-the-art R&D facility for students, faculty and
collaborators
5. Offer a comprehensive and meaningful computing environment for
education by:
1. complementing the theoretical coursework in CC with appropriate laboratory
coursework for students, and
2. encouraging team participation and cross-disciplinary problem solving

8. © 2017 IBM Corporation
IBM’s AI Lab
OpenPOWER System for
Data Analytics with
Accelerators (GPU)
Collaborative technical projects
Access to IBM Academic Initiative
Toolkit
Graduate, Ph.D. and Post-Doctoral
research
Webinars and Technical Workshops
Projects related to make smart cities
and smart villages

9. © 2017 IBM Corporation
OpenPOWER System for
Data Analytics with
Accelerators (GPU)
Collaborative technical projects
Access to IBM Academic Initiative
Toolkit
Graduate, Ph.D. and Post-Doctoral
research
Webinars and Technical Workshops
Projects related to make smart cities
and smart villages

10. © 2017 IBM Corporation10
Proposed AI cloud setup and specifications - Hardware
College Ethernet Network
4
4
College Lan Network
College Ethernet Network
10 Desktops/Laptops
2 Jetson Nano Edge Devices

11. © 2017 IBM Corporation11
AI Lab users
AI Lab Software Components

12. University Use Cases and Scenarios of
Proposed AI Lab
AI Cloud at Universities

13. © 2017 IBM Corporation13
Use Case 1 : Students (daily use) requests for compute resource
Basic ML/DL exercises
Login to web portal
with “Student”
profile; browse
service catalog.
Select and request
for desired image,
and usage period
eg. MS Office with
Windows for 2
hours.
Login and access
Docker Container
(Remote Desktop)
AI Cloud Portal AI Cloud
Infrastructure
User / profile
authentication
Service request
processing &
approval
VM & storage
created according to
request
OS deployed into
Docker Container
Application image
deployed into Docker
Container
Login info sent to
user via email
Docker Container
with PowerAI image f
Downloads
completed work
into laptop and logs
off.
Resources made available to
students for daily use will be
restricted. The restriction will
be enforced through profile
management on the cloud
portal.
Students
Students login from
anywhere within the
UM LAN. Cloud portal
is accessed via a web
browser.
Application and OS
images have to be
preconfigured by the
cloud admin before
use.

14. © 2017 IBM Corporation14
Use Case 2 : Final Year Students requests for compute resource for
AI Projects
Login to webportal
with “FY” profile;
browse service
catalog.
Select and request
for desired image,
and usage period e
Login and access
Docker Container
(Remote Desktop)
AI Cloud Portal Cloud Infrastructure
User / profile
authentication
Service request
processing &
approval
VM & storage
created according to
request
OS deployed into VM
Application image
deployed into VM
Login info sent to
user via email
VM deprovisioned
back into the cloud
Downloads
completed work
into laptop and logs
off.
Resources made available to
final year students will be
restricted. The restriction will
be enforced through profile
management on the cloud
portal.
FY Students
Students login from
anywhere within the
UM LAN. Cloud portal
is accessed via a web
browser.
IP address for VM
deployed within same
subnet. Students
access from laptop.
VM and Storage size :
2-4 cores, 4GB RAM, 10GB
storage
RHEL
Jetson Nano
VM for the FY student will be
operational until the expiration
date stated in his request.

15. © 2017 IBM Corporation15
Use Case 3 : Final Year Students creates own application image,
and shares image with other FY students.
Student to seek
approval from
Cloud Admin to
create new app
image in cloud infra
New image is
displayed in the
service catalog
Other FY students
proceed to request,
access and use
new application (as
per Use Case 2)
Ai Cloud Admin AI Cloud
Infrastructure
To ensure proper cloud
operations, only the cloud
administrator is allowed to
manage image offereings in
the cloud.
FY Students
In order to allow other
FY students to have
access to the new
application image for
their own project, the
originator of the
application has to work
with the cloud admin to
package the app as an
image offering in the
cloud.
Cloud Portal
Provisioning
manager packages
app image with OS
Image is registered
with service
automation
manager and portal
User / profile
authentication
Service request
processing
VM & storage
created according to
request
OS deployed into VM
Application image
deployed into VM
Login info sent to
user via email
VM deprovisioned
back into the cloud

16. © 2017 IBM Corporation16
During 2 hr class,
provides VM login
information to 40
students in class /
exam
Use Case 4 : Lecturers prebooking seats for AI/ML/DL class or exam
AI Cloud Portal AI Cloud Infrastructure
User / profile
authentication
Service request
processing
VM & storage
created according to
request
OS deployed into VM
Application image
deployed into VM
Login info sent to
lecturer via email
VMs deprovisioned
back into the cloud
Resources made available to
students for daily use will be
restricted. The restriction will
be enforced through profile
management on the cloud
portal.
Lecturers
VM and Storage size :
40 VMs
2 core, 4GB RAM, 5GB
storage
RHEL
PowerAI Vision
Watson Machine Learning
Accelerator
Lecturer proceed to
request for VMs
with “Lecturer”
profile.
Select and request
for desired image,
and future usage
period eg. 40 VMs
of SPSS with LInux
for 2 hours.
Students access
VMs from laptop /
PC / workstations
Students download
work at end of class
Application and OS
images have to be
preconfigured by the
cloud admin before
use.
IP address for VM
deployed within same
subnet.

17. © 2017 IBM Corporation17
Use Case 5 : Researchers adding compute capacity with own
applications through the AI cloud
AI Cloud Portal Ai Cloud InfrastructureResearchers
Researchers proceed
to request, access VM
and install own
application (as per
Use Case 2)
User / profile
authentication
Service request
processing
VM & storage
created according to
request
OS deployed into VM
Application image
deployed into VM
Login info sent to
user via email
VM deprovisioned
back into the cloud
VM and Storage size :
8 cores, 16GB RAM, 250GB
storage
RHEL

18. © 2017 IBM Corporation
2 Year Developmental Timeline
a) IBM POWER Academic Initiative
partnership
b) OpenPOWER system and
Accelerator for Deep Learning
and Machine Learning
c) Technical Projects deployment
d) Review of progress in technical
projects, lab coursework
e) Big data and AI curriculums

19. © 2017 IBM Corporation
AI Cloud (On
Premise)
PowerAI makes deep
learning, machine learning
and AI more accessible and
more performant
By combining this software
platform for deep learning with
IBM Power Systems,
enterprises and Institutions can
rapidly deploy a fully optimized
and supported platform for
machine learning frameworks
and their dependencies. And it
is built for easy and rapid
deployment
PowerAI runs on the IBM Power
System AC922 for High
Performance Computer server
infrastructure

20. © 2017 IBM Corporation
Advantages for Your Faculty and
Students
 Talent and Skills: (Remote Interns; Skills and Training)
Students and Research scholars will start working on the
advanced technologies will enable them to work on
many applications
Publications and Mindshare: (Press releases, Articles,
and Publications; Conferences and Events)
1. Conference Paper on software-based application
research /development in 6 months
 Intellectual Capital: (Patents, Open source; Prototypes,
Demos; Curriculum; Student projects, Theses)
1. Prototype building of many research problems using
software-centric approach (hardware-centric baseline
implementation almost getting completed)
2. Potential to file disclosures
 Opportunities: (Seed revenue; Leverage other funding;
Build ecosystems; Build government/client relationships)
1. Once software-centric solution available with
comparable performance using latest technologies ,
your team would create prototypes which can be
demonstrated to several colleges

21. © 2017 IBM Corporation
21
Ganesan Narayanasamy
ganesana@in.ibm.com
OpenPOWER leader in
Education and Research WW
IBM Systems
Thank
you!