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©	IBM	Corporation,	2011	|	14	April	2020		
IBM	Confidential	 	 0	
Proposal	for		Center	–	POWER9	
based	OpenPOWER		AI/Cloud		
System	for	Organization
Table	of	Contents	
1. Executive	Summary	
1.1 Our	understanding	of	your	goals	
1.2 Our	Approach	to	meet	your	goals	
1.3 Points	to	note	
2. A	Brief	Description	to	OpenPOWER		offered	Solution	
2.1 IBM	Power	9	Inside	Raptor	system	
2.2 Education	Bundle	
2.3 Technical	Details	
2.4 Curriculum	for	data	science	
2.5 Complete	AI	stack	and	Case	study	references
1.	Executive	Summary	
1.1 Our	understanding	of	your	goals	
The	YOUR	INSTITUTE/	Organization	has	been	the	early	adopter		of	advanced	technology.	It	is	one	of	
the	 premier	 technology	 institute/organizations	 in	 India	 and	 in	 the	 World.	 YOUR	 INSTITUTE/	
Organization	continues	to	provide	advanced	research	in	the	area	of	applied	&	advanced	science,	
engineering	and	technology.	
	
YOUR	 INSTITUTE/	 Organization	 intends	 to	 establish	 the	 state	 of	 art	 High	 Performance	 AI	
infrastructure	 capable	 by	 delivering	 a	 good	 amount	 of	 computational	 power	 to	 solve	 high	 end	
Analytics	problems..		These	systems	will	help	the	institute	/Organization	to	achieve	the	same.	
		
	
1.2 Our	approach	to	meet	your	goals	
	
	
The	proposed	IBM	hardware	and	software	technologies	as	well	as	its	predecessors	have	an	extensive	
proven	track	record	within	the	industry	and	are	fully	developed	and	integrated	within	IBM.	
	
	
In	terms	of	Software,	the	proposed	stack	uses	the	IBM’s	hardware	infrastructure	effectively.		We	have	
taken	care	to	use	the	proven	commercially	available	software	to	ensure	ISI	derives	the	benefit	of	
reliability	and	assurance.	
n POWER	AI	Package	which	runs	currently	on	Ubuntu	.		
n Together	 with	 our	 hardware	 and	 OS,	 we	 can	 deliver	 unprecedented	 availability	 and	
reliability.		
	
	
1.3 Points	to	note	
n This	solution	documented	here	pertains	to	System	1	referred	in	the	document.			
n IBM	solution	is	more	power	efficient	than	any	other	offering	in	the	market
2.	A	Brief	description	to	IBM	offered	solution	
	
2.1 IBM	Power	9	Inside	Raptor	System		
Artificial Intelligence (AI) and Deep Learning (DL) are the buzz words in the tech.
industry today.
No wonder the Education industry has realized the huge potential of AI and is actively
embracing AI and DL in their stream to help groom students for the future of Technology.
Here is an exciting offer from IBM and Nvidia the front-runners in AI technology for
Education
Blackbird AI Desktop
Talos™ II Development System for IBM PowerAI™ including:
• EATX chassis with 500W ATX power supply
• A single Talos™ II Lite EATX mainboard
• One 4-core IBM POWER9 CPU
o 4 cores per package
o SMT4 capable
o POWER IOMMU
o 90W TDP
• One 3U POWER9 heatsink / fan (HSF) assembly
• 32GB DDR4 ECC registered memory
• 2 front panel USB 2.0 ports
• One NVIDIA® RTX 2070 GPU
• 128GB internal NVMe storage
• Recovery DVD
The Blackbird™ mainboard is an affordable, owner-controllable, desktop and entry server level mainboard. Built
around the IBM POWER9 processor, and leveraging Linux and OpenPOWER™ technology, Blackbird™ allows
you to secure your data without sacrificing performance. Designed with a fully owner-controlled CPU domain, you
can audit and modify any portion of the open source firmware on the Blackbird™ mainboard, all the way down to
the CPU microcode. This is an unprecedented level of access for any modern desktop-class machine, and one that is
increasingly needed to assure safety and compliance with new regulations, such as the EU's GDPR.
2.2 The Education Bundle
PowerAI Vision - Auto Deep Learning Tool
IBM® PowerAI Vision is a video and image analysis platform that is built for IBM Power
Systems servers. PowerAI Vision uses GPUs to optimize performance.
PowerAI Software
H2o.ai Auto Machine Learning Tool
Data science Curriculum and many more 	
	
2.3 Technical Details
2.4 Curriculum for Data Science:
Introduction to Data Science
1. Data, Why Data, Types of Data, Data Quality
2. Law of Diminishing Returns, Design for Scalability
3. Data Collection and Preparation, Regression and Classification Models
4. Data and decision making, Understanding cognitive bias
5. Data for persuasion and action, Integrating data and domain knowledge, storytelling with data
Fundamentals of Statistics
1. Probability and Sampling Theory
2. R Programming – Setting up R Studio and its packages
3. Statistical Thinking and Statistical Models
4. Descriptive Statistics and Visualization
5. Bayesian Modeling
Linux & Python Basics
1. Linux commands to navigate File Systems
2. Basics of GIT and Notebooks.
3. Introduction to Python Programming, Setup
4. Data structures, List, Dictionaries, Tuples, Functions, Namespaces, Scope, Recursive
Functions and I/O –Operations
Advanced Python Concepts
1. File and Formatting, Error Handling
2. Interactive Programming – Jupyter Notebooks
3. Advance Data Science Libraries – Numpy, Pandas, Scikit
Big Data
1. Introduction to Hadoop – Motivation, BigData, MapReduce
2. HDFS. Hadoop Evolution – v1.0 vs v2.0, YARN and Other Distributions – Cloudera
3. Deployment Modes, Standalone, Pseudo and Full distributions use cases
Introduction to Apache Flume
1. Introduction to Flume and its architecture
2. Data Transfer between local and HDFS and some use cases
3. Introduction to Hive Programming, its architecture, data types and models, operators and UDF
4. Introduction to Pig, its architecture, components, models and operators.
Introduction to Clustered Computing
1. Introduction to Spark – Why, installation and configuration
2. Spark vs. Hadoop, Spark Basics
3. Spark RDDs, pySpark, Spark Streaming
4. Spark SQL, DataFrames, UDF
5. Using Hive, MLIB, use cases
Fundamentals of Machine Learning
1. Setting up the programming environment
2. Learning Models – Supervised Models, Regression
3. Unsupervised Models- Clustering
4. Recommender Systems – Collaborative Filtering
5. Other Machine Learning Techniques, Apache SystemML
Fundamentals of Deep Learning
1. Setting up the programming environment.
2. Deep Learning Libraries – Caffe, Tensorflow and others
3. GPU-based Processing
4. Deep Learning Models – CNN,RNN
5. Introduction to Convolution Neural Networks (CNN) and its components and implementation
6. Introduction to Recurrent Neural Networks (RNN) and its components and implementation
7. Use cases - Image Classifications
	
2.5			Complete	AI	Stack	and	Case	studies
Case studies and Other details on POWER AI : https://developer.ibm.com/linuxonpower/deep-learning-
powerai/stories/

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Ibm coe openpowerailabdubaiwithraptor

  • 2. Table of Contents 1. Executive Summary 1.1 Our understanding of your goals 1.2 Our Approach to meet your goals 1.3 Points to note 2. A Brief Description to OpenPOWER offered Solution 2.1 IBM Power 9 Inside Raptor system 2.2 Education Bundle 2.3 Technical Details 2.4 Curriculum for data science 2.5 Complete AI stack and Case study references
  • 3. 1. Executive Summary 1.1 Our understanding of your goals The YOUR INSTITUTE/ Organization has been the early adopter of advanced technology. It is one of the premier technology institute/organizations in India and in the World. YOUR INSTITUTE/ Organization continues to provide advanced research in the area of applied & advanced science, engineering and technology. YOUR INSTITUTE/ Organization intends to establish the state of art High Performance AI infrastructure capable by delivering a good amount of computational power to solve high end Analytics problems.. These systems will help the institute /Organization to achieve the same. 1.2 Our approach to meet your goals The proposed IBM hardware and software technologies as well as its predecessors have an extensive proven track record within the industry and are fully developed and integrated within IBM. In terms of Software, the proposed stack uses the IBM’s hardware infrastructure effectively. We have taken care to use the proven commercially available software to ensure ISI derives the benefit of reliability and assurance. n POWER AI Package which runs currently on Ubuntu . n Together with our hardware and OS, we can deliver unprecedented availability and reliability. 1.3 Points to note n This solution documented here pertains to System 1 referred in the document. n IBM solution is more power efficient than any other offering in the market
  • 4. 2. A Brief description to IBM offered solution 2.1 IBM Power 9 Inside Raptor System Artificial Intelligence (AI) and Deep Learning (DL) are the buzz words in the tech. industry today. No wonder the Education industry has realized the huge potential of AI and is actively embracing AI and DL in their stream to help groom students for the future of Technology. Here is an exciting offer from IBM and Nvidia the front-runners in AI technology for Education Blackbird AI Desktop Talos™ II Development System for IBM PowerAI™ including: • EATX chassis with 500W ATX power supply • A single Talos™ II Lite EATX mainboard • One 4-core IBM POWER9 CPU o 4 cores per package o SMT4 capable o POWER IOMMU o 90W TDP • One 3U POWER9 heatsink / fan (HSF) assembly • 32GB DDR4 ECC registered memory • 2 front panel USB 2.0 ports • One NVIDIA® RTX 2070 GPU • 128GB internal NVMe storage • Recovery DVD The Blackbird™ mainboard is an affordable, owner-controllable, desktop and entry server level mainboard. Built around the IBM POWER9 processor, and leveraging Linux and OpenPOWER™ technology, Blackbird™ allows you to secure your data without sacrificing performance. Designed with a fully owner-controlled CPU domain, you can audit and modify any portion of the open source firmware on the Blackbird™ mainboard, all the way down to the CPU microcode. This is an unprecedented level of access for any modern desktop-class machine, and one that is increasingly needed to assure safety and compliance with new regulations, such as the EU's GDPR.
  • 5. 2.2 The Education Bundle PowerAI Vision - Auto Deep Learning Tool IBM® PowerAI Vision is a video and image analysis platform that is built for IBM Power Systems servers. PowerAI Vision uses GPUs to optimize performance. PowerAI Software H2o.ai Auto Machine Learning Tool Data science Curriculum and many more 2.3 Technical Details
  • 6. 2.4 Curriculum for Data Science: Introduction to Data Science 1. Data, Why Data, Types of Data, Data Quality 2. Law of Diminishing Returns, Design for Scalability 3. Data Collection and Preparation, Regression and Classification Models 4. Data and decision making, Understanding cognitive bias 5. Data for persuasion and action, Integrating data and domain knowledge, storytelling with data Fundamentals of Statistics 1. Probability and Sampling Theory 2. R Programming – Setting up R Studio and its packages 3. Statistical Thinking and Statistical Models 4. Descriptive Statistics and Visualization 5. Bayesian Modeling Linux & Python Basics 1. Linux commands to navigate File Systems 2. Basics of GIT and Notebooks. 3. Introduction to Python Programming, Setup 4. Data structures, List, Dictionaries, Tuples, Functions, Namespaces, Scope, Recursive Functions and I/O –Operations Advanced Python Concepts 1. File and Formatting, Error Handling 2. Interactive Programming – Jupyter Notebooks 3. Advance Data Science Libraries – Numpy, Pandas, Scikit Big Data 1. Introduction to Hadoop – Motivation, BigData, MapReduce 2. HDFS. Hadoop Evolution – v1.0 vs v2.0, YARN and Other Distributions – Cloudera 3. Deployment Modes, Standalone, Pseudo and Full distributions use cases Introduction to Apache Flume 1. Introduction to Flume and its architecture 2. Data Transfer between local and HDFS and some use cases 3. Introduction to Hive Programming, its architecture, data types and models, operators and UDF 4. Introduction to Pig, its architecture, components, models and operators. Introduction to Clustered Computing
  • 7. 1. Introduction to Spark – Why, installation and configuration 2. Spark vs. Hadoop, Spark Basics 3. Spark RDDs, pySpark, Spark Streaming 4. Spark SQL, DataFrames, UDF 5. Using Hive, MLIB, use cases Fundamentals of Machine Learning 1. Setting up the programming environment 2. Learning Models – Supervised Models, Regression 3. Unsupervised Models- Clustering 4. Recommender Systems – Collaborative Filtering 5. Other Machine Learning Techniques, Apache SystemML Fundamentals of Deep Learning 1. Setting up the programming environment. 2. Deep Learning Libraries – Caffe, Tensorflow and others 3. GPU-based Processing 4. Deep Learning Models – CNN,RNN 5. Introduction to Convolution Neural Networks (CNN) and its components and implementation 6. Introduction to Recurrent Neural Networks (RNN) and its components and implementation 7. Use cases - Image Classifications 2.5 Complete AI Stack and Case studies
  • 8. Case studies and Other details on POWER AI : https://developer.ibm.com/linuxonpower/deep-learning- powerai/stories/