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