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IBM PowerAI: Deep Learning Unleashed on IBM Power Systems Servers

An IBM Redbooks publication


Published on 07 March 2018, updated 05 June 2019

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ISBN-10: 0738442941
ISBN-13: 9780738442945
IBM Form #: SG24-8409-00

Authors: Dino Quintero, Bing He, Bruno C. Faria, Alfonso Jara, Chris Parsons, Shota Tsukamoto and Richard Wale

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    This IBM® Redbooks® publication is a guide about the IBM PowerAI Deep Learning solution. This book provides an introduction to artificial intelligence (AI) and deep learning (DL), IBM PowerAI, and components of IBM PowerAI, deploying IBM PowerAI, guidelines for working with data and creating models, an introduction to IBM Spectrum™ Conductor Deep Learning Impact (DLI), and case scenarios.

    IBM PowerAI started as a package of software distributions of many of the major DL software frameworks for model training, such as TensorFlow, Caffe, Torch, Theano, and the associated libraries, such as CUDA Deep Neural Network (cuDNN). The IBM PowerAI software is optimized for performance by using the IBM Power Systems™ servers that are integrated with NVLink. The AI stack foundation starts with servers with accelerators. graphical processing unit (GPU) accelerators are well-suited for the compute-intensive nature of DL training, and servers with the highest CPU to GPU bandwidth, such as IBM Power Systems servers, enable the high-performance data transfer that is required for larger and more complex DL models.

    This publication targets technical readers, including developers, IT specialists, systems architects, brand specialist, sales team, and anyone looking for a guide about how to understand the IBM PowerAI Deep Learning architecture, framework configuration, application and workload configuration, and user infrastructure.

    Table of Contents

    Chapter 1. Introduction to artificial intelligence and deep learning

    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 IBM PowerAI

    Chapter 6. Introduction to IBM Spectrum Conductor Deep Learning Impact

    Chapter 7. Case scenarios: Using IBM PowerAI

    Appendix A. Sentiment analysis code

    Appendix B. Problem determination tools


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