Lenovo AI Validated Design for Model Training on ThinkSystem ServersReference Architecture

8 Jul 2018
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32 pages, 778 KB


This document describes the reference architecture for Artificial Intelligence (AI) Model Training on Lenovo ThinkSystem servers. It provides a predefined and optimized hardware infrastructure for the model training under various usage scenarios. The reference architecture provides planning, design considerations, and best practices for implementing model training with Lenovo products.

One key step in the AI adoption journey is exploration and selection of models for deep learning (DL). Typical models are based on deep neural networks (DNNs) and require a significant amount of computational resources for training. Using hardware infrastructure designed as a scale-out cluster for such model training use cases is a key requirement for enabling DL adoption.

The intended audience for this reference architecture is IT professionals, technical architects, sales engineers, and consultants to assist in planning, designing, and implementing advanced analytics solutions with Lenovo hardware.

Table of Contents

1 Introduction
2 Business problem and business value
3 Requirements
4 Architectural overview
5 Component model
6 Operational model
7 Deployment considerations
8 Appendix: Bill of Material
9 Appendix: Example Training Workload
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Change History

Changes in the July 8 update:

  • Updated Chinese version of the document

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