1. The document proposes setting up an AI cloud at a university to provide compute resources for students, researchers, and lecturers. 2. It outlines various use cases for how different user groups would access and use the cloud, including students doing basic ML exercises, final year students using it for projects, and lecturers booking resources for classes. 3. It also describes the roles and responsibilities of an AI cloud administrator who would manage user access, configure images, and monitor the infrastructure.

1. Proposed
Collaboration with
your University
AI Center of Excellence

2. © 2017 IBM Corporation2
Proposed AI cloud setup and specifications - Hardware
College Ethernet Network
4
4
College Lan Network
College Ethernet Network
CPU: 2*LaGrange 18 core
Memory: 16*8 G DDR4
RDIMM
Hard Drive: 2*2T SATA
3.5’’
Network: 10G
T4 GPU Card
Jetson Nano
Xilinx U96 CARD

3. © 2017 IBM Corporation3
AI Lab users
AI Lab Software Components

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

5. © 2017 IBM Corporation5
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.

6. © 2017 IBM Corporation6
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.

7. © 2017 IBM Corporation7
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

8. © 2017 IBM Corporation8
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.

9. © 2017 IBM Corporation9
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

10. © 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

11. © 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

12. © 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

13. © 2017 IBM Corporation
13
Ganesan Narayanasamy
ganesana@in.ibm.com
OpenPOWER leader in
Education and Research WW
IBM Systems
Thank
you!

14. © 2017 IBM Corporation14
AI Cloud Administrator’s tasks and to-dos.
1. Register and create user profiles for various user groups that will utilize the cloud. The
administrator must also register the members in each group. Members can be specific
(for long term usage eg. Final year students) or generic (eg. daily usage). This would
determine the access rights and rights to resources of each user.
2. Preconfigure and prepackage images for OS and application prior to going live. These
images will be used for automated provisioning and deployment in the cloud.
3. Monitoring and managing the cloud infrastructure – performance, utilization,
availability, energy consumption, configuration. This includes servers, storage, networks,
virtualization layer.
4. Configure and package images for new applications.
5. Level 1 support for user and operational issues.
The list of AI cloud administrator’s task is not limited to only the above. The list above is meant to highlight
the key roles as a start.

15. © 2017 IBM Corporation15
Proposed Roles Mapping
AI Cloud Administrator
Responsible for the configurations, operations and maintenance of the cloud
infrastructure. Responsible for granting access to user groups within the campus.
AI Team Administrators
Researchers – with rights to request for uncapped amount of
resources. Restricted according to profile.
Final Year Students – Rights to request for resources for final year
project (6 – 12 months). Accountable to lecturers. Restricted according
to profile.
Student (daily use) – Rights to request for only the resources assigned
to them. Restricted according to profile.
Ai Cloud Managers
Lecturers – with class / exam responsibilities. Responsible to request
for resources for class / labs / exams. Responsible to approve service
requests from their students and teams. Responsible to determine
image configuration for classes / exams.
Class
Exam
FY StudentsDaily use