IBM PowerAI: Deep Learning Unleashed on IBM Power Systems Servers

Dino Quintero, Bing He, Bruno C. Faria, Alfonso Jara, Chris Parsons, Shota Tsukamoto, Richard Wale, IBM Redbooks

IBM Redbooks, Jun 5, 2019 - Computers - 278 pages

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 SpectrumTM 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 SystemsTM 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.

Contents

• First Edition March 2018: 1
• Introduction and overview of IBM PowerAI: 21
• IBM PowerAI components: 33
• Berkeley Vision and Learning Center BVLC Caffe V1 0 0: 34
• NVIDIA fork of Caffe V0 15 14: 49
• Deploying IBM PowerAI: 59
• Chainer V1 23 0: 99
• IBM Spectrum MPI V10 1 1 0: 116
• Working with data and creating models in IBM PowerAI: 121
• Anaconda2V5 0: 129
• Introduction to IBM Spectrum Conductor Deep Learning Impact: 143
• Using IBM PowerAI: 231
• Appendix A Sentiment analysis code: 241
• Using the model: 247
• Appendix B Problem determination tools: 249

Bibliographic information

• Title: IBM PowerAI: Deep Learning Unleashed on IBM Power Systems Servers
• Authors: Dino Quintero, Bing He, Bruno C. Faria, Alfonso Jara, Chris Parsons, Shota Tsukamoto, Richard Wale, IBM Redbooks
• Publisher: IBM Redbooks, 2019
• ISBN: 0738442941, 9780738442945
• Length: 278 pages
• Subjects: Computers / Distributed Systems / General