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Generative AI for IBM Power
Course Description
Overview
The goal of this course is to provide the student with a tangible understanding and a real hands-on experience with generative AI applications deployed and optimized for IBM Power systems. This self-paced virtual course uses video lectures, review questions, and virtual lab machine exercises to provide the student with foundational knowledge and experience about the topics covered in the course. In the lecture, the student begins by learning the basics of generative AI, and the basic components of a generative AI application, then applies these concepts to real-world examples on Power where the student learns Power offerings for AI workloads, package management fundamentals in Python, Operating System deployment strategies on Power, and Power hardware use cases.
The lab exercises will start with viewing and manipulating files in a basic AI application running on IBM Power Red Hat Enterprise Linux. They will have hands-on experience identifying the components of that AI application and their connection to other components, and will add functionalities to the application that demonstrate more advanced AI application techniques like frameworks, prompt tuning, and conversation memory. Then, the students will go through the process of setting up a Python virtual environment, investigating Power hardware resources, and using the Hardware Management Console (HMC) to ensure memory and other resources are optimized for AI workloads.
Objectives
- Understand foundational genAI concepts and terms
- Familiarize with the larger genAI ecosystem for AI applications
- Distinguish between basic genAI math and hardware terms
- Understand IBM Power's current offerings for AI workloads
- Set up package channels and repositories for AI libraries on Power
- Identify AI application components in an example implementation of AI inferencing on Power
- Describe Power-specific optimizations and recommendations for GenAI
- Refer to peripheral applications and services that help to develop AI apps on Power
Audience
This course is open to all interested learners, regardless of their experience. Typical students may include customers, IBM technical personnel, Business Partner technical personnel, computer engineering students, IT consultants and architects.
Topics
- Unit 0: Introduction
- Video 0-1: Introduction
- Unit 1: Generative AI applications
- Video 1-1: Generative AI concepts
- Video 1-2: Inferencing
- Video 1-3: Code
- Unit 1 review questions
- Exercise 1: Generative AI applications
- EX01 Section 1: Working through a Jupyter Notebook of an AI application
- EX01 Section 2: Adding and managing advanced generative AI app features
- EX01 Section 3: Adding conversation memory to a generative AI application
- EX01 Section 4: Implementing an AI framework
- Unit 2: Math, hardware, and Power offerings for generative AI
- Video 2-1: Math and hardware terms
- Video 2-2: Power offerings for AI
- Unit 2 review questions
- Unit 3: Implementing generative AI on Power
- Video 3-1: Package management
- Video 3-2: Retrieval-augmented generation
- Video 3-3: Implementing genAI on Power
- Unit 3 review questions
- Unit 4: Deploying AI on Power
- Video 4-1: Performance considerations
- Video 4-2: AI deployment options
- Unit 4 review questions
- Exercise 2: Deploying AI on Power Red Hat Enterprise Linux (RHEL)
- EX02 Section 1: Viewing a virtual environment and installed packages
- EX02 Section 2: Viewing hardware resources
- EX02 Section 3: Creating a virtual environment and investigating HMC hardware resources
Self-Paced Training Info
Learn at your own pace with anytime, anywhere training
- Same in-demand topics as instructor-led public and private classes.
- Standalone learning or supplemental reinforcement.
- e-Learning content varies by course and technology.
- View the Self-Paced version of this outline and what is included in the SPVC course.
- Learn more about e-Learning
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Self-Paced Training Terms & Conditions
This is a Self-Paced virtual class; it is intended for students who do not need the support of a classroom instructor.
If you feel you would better benefit from having access to a Subject Matter Expert, please enroll in the Instructor-Led version instead. Minimal technical support is provided to address issues with accessing the platform or problems within the lab environment.
Before you enroll, review the system requirements to ensure that your system meets the minimum requirements for this course. AFTER YOU ARE ENROLLED IN THIS COURSE, YOU WILL NOT BE ABLE TO CANCEL YOUR ENROLLMENT. You are billed for the course when you submit the enrollment form. Self-Paced Virtual Classes are non-refundable. Once you purchase a Self-Paced Virtual Class, you will be charged the full price.
After you receive payment confirmation from LearnQuest, you will be sent further access instructions and time limits for your course from IBM.
IMPORTANT!!! If your course provides access to a hands-on lab (Virtual Lab Environment), you will have a specific number of days (varies course by course) on the remote lab platform to complete your hands-on labs. Do not start your lab until you are ready to use your lab time effectively. Time allotted in the virtual lab environment will be indicated once you log into your course. The self-paced format gives you the opportunity to complete the course at your convenience, at any location, and at your own pace. The course is available 24 hours a day.
If the course requires a remote lab system, the lab system access is allocated on a first-come, first-served basis. When you are not using the elab system, ensure that you suspend your elab to maximize your hours available to use the elab system. Note: This does not add additional days to your Lab Environment time frame.
Click the Skytap Connectivity Test button to ensure this computer's hardware, software and internet connection works with the SPVC Lab Environment.
Click the Skytap Connectivity Documentation button to read about the hardware, software and internet connection requirements.
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