IBM PowerAI: Deep Learning Unleashed on IBM Power Systems
A draft IBM Redbooks publication
Updated 22 December 2017
IBM Form #: SG24-8409-00
Authors: Dino Quintero, Bing He, Bruno C. Faria, Alfonso Jara, Chris Parsons, Shota Tsukamoto, Richard Wale
This IBM Redbooks publication delivers a guide about the IBM PowerAI Deep Learning solution. This book includes topics for example, introduction to artificial intelligence (AI) and deep learning (DL), introduction to PowerAI, components of PowerAI, deploying PowerAI, guidelines for working with data and creating models, an introduction to IBM Spectrum Conductor Deep Learning Impact (DLI), and case scenarios.
PowerAI started off as a package of software distributions of many of the major deep learning software frameworks for model training like TensorFlow, Caffe, Torch, Theano, and the associated libraries such as cuDNN. The PowerAI software has always been optimized for performance using the NVLink-based Power Systems servers. The AI stack foundation starts with the proper hardware: servers with accelerators. GPU accelerators are extremely well suited for the compute-intensive nature of deep learning training, and servers with the highest CPU to GPU bandwidth such as IBM Power Systems NVLink servers enable the high-performance data transfer required for larger and more complex deep learning models.
This publication targets technical readers including developers, IT specialist, systems architects, brand specialist, sales team, and anyone looking for a guide on how to understand IBM PowerAI Deep Learning ecosystem architecture, framework configuration, application and workload configuration, and user infrastructure.
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Table of contents
Chapter 1. Introduction to artificial intelligence (AI) and deep learning (DL)
Chapter 2. Introduction and overview of IBM PowerAI
Chapter 3. IBM PowerAI components
Chapter 4. Deploying IBM PowerAI
Chapter 5. Working with data and creating models in PowerAI
Chapter 6. Introduction to IBM Spectrum Conductor Deep Learning Impact (DLI)
Chapter 7. Case scenarios: Leveraging IBM PowerAI
Appendix A. Sentiment analysis code
Appendix B. Problem determination tools
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