Book description
Abstract
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
- Front cover
- Notices
- Preface
-
Chapter 1. Introduction to artificial intelligence and deep learning
- 1.1 Deep learning
-
1.2 Neural networks overview
- 1.2.1 A brief history about neural networks
- 1.2.2 Why neural networks are an important subject
- 1.2.3 Types of neural networks and their usage
- 1.2.4 Neural network architectures
- 1.2.5 Difference between a classical and deep neural networks
- 1.2.6 Neural networks versus classical machine learning algorithms
- 1.3 Deep learning frameworks
- Chapter 2. Introduction and overview of IBM PowerAI
-
Chapter 3. IBM PowerAI components
-
3.1 IBM PowerAI components
- 3.1.1 IBM PowerAI support and extra services from IBM
- 3.1.2 IBM Power Systems for deep learning
- 3.1.3 Linux on Power for deep learning
- 3.1.4 NVIDIA GPUs
- 3.1.5 NVIDIA components
- 3.1.6 NVIDIA drivers
- 3.1.7 IBM PowerAI deep learning package
- 3.1.8 Libraries
- 3.1.9 Frameworks
- 3.1.10 Other software and functions
- 3.2 IBM PowerAI compatibility matrix
-
3.1 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
- 6.1 Definitions, acronyms, buzzwords, and abbreviations
- 6.2 Benefits of IBM Spectrum Conductor Deep Learning Impact
- 6.3 Key features of Deep Learning Impact
-
6.4 DLI deployment
- 6.4.1 Deployment consideration
- 6.4.2 DLI single-node mode
- 6.4.3 DLI cluster without a high availability function
- 6.4.4 DLI cluster with a high availability function
- 6.4.5 Binary files installation for the high availability enabled cluster
- 6.4.6 A DLI cluster with a high availability function installation guide
- 6.5 Master node crashed when a workload is running
- 6.6 Introduction to DLI graphic user interface
- 6.7 Supported deep learning network and training engine in DLI
- 6.8 Use case: Using a Caffe Cifar-10 network with DLI
- Chapter 7. Case scenarios: Using IBM PowerAI
- Appendix A. Sentiment analysis code
- Appendix B. Problem determination tools
- Related publications
- Back cover
Product information
- Title: IBM PowerAI: Deep Learning Unleashed on IBM Power Systems Servers
- Author(s):
- Release date: March 2018
- Publisher(s): IBM Redbooks
- ISBN: 9780738442945
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