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IBM PowerAI: Deep Learning Unleashed on IBM Power Systems Servers
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 I...
Limerick
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IBM Power Systems Performance Guide: Implementing and Optimizing
This IBM® Redbooks® publication addresses performance tuning topics to help leverage the virtualization strengths of the POWER® platform to solve clients' system resource utilization challenges, and maximize system throughput and capacity. We examine the performance monitoring tools, utilities, documentation, and other resources available to help technical teams provide optimized business solutions and support for applications running on IBM POWER systems' virtualized environments. The book offers application performance examples deployed on IBM Power SystemsTM utilizing performance monitoring tools to leverage the comprehensive set of POWER virtualization features: Logical Partitions (LPARs), micro-partitioning, active memory sharing, workload partitions, and more. We provide a well-defined and documented performance tuning model in a POWER system virtualized environment to help you plan a foundation for scaling, capacity, and optimization . This book targets technical professionals (technical consultants, technical support staff, IT Architects, and IT Specialists) responsible for providing solutions and support on IBM POWER systems, including performance tuning.
Implementing IBM Spectrum Scale
This IBM® RedpaperTM publication describes IBM SpectrumTM Scale, which is a scalable, high-performance data and file management solution, built on proven IBM General Parallel File System (GPFSTM) technology. Providing reliability, performance and scalability, IBM Spectrum ScaleTM can be implemented for a range of diverse requirements. This publication can help you install, tailor, and configure the environment, which is created from a combination of physical and logical components: hardware, operating system, storage, network, and applications. Knowledge of these components is key for planning an environment. However, to appreciate potential benefit first requires a simpler understanding of...
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Red Hat OpenShift V4.3 on IBM Power Systems Reference Guide
This IBM® Redpaper publication describes how to deploy Red Hat OpenShift V4.3 on IBM Power Systems servers. This book presents reference architectures for deployment, initial sizing guidelines for server, storage, and IBM Cloud® Paks. Moreover, this publication delivers information about initial supported Power System configurations for Red Hat OpenShift V4.3 deployment (bare metal, IBM PowerVM® LE LPARs, and others). This book serves as a guide for how to deploy Red Hat OpenShift V4.3 and provide start guidelines and recommended practices for implementing it on Power Systems and completing it with the supported IBM Cloud Paks. The publication addresses topics for developers, IT architects, IT specialists, sellers, and anyone who wants to implement a Red Hat OpenShift V4.3 and IBM Cloud Paks on IBM Power Systems. This book also provides technical content to transfer how-to skills to the support teams, and solution guidance to the sales team. This book compliments the documentation that is available at IBM Knowledge Center, and also aligns with the educational offerings that are provided by the IBM Systems Technical Education (SSE).
Reports
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Final Concord by the Court of Common Pleas Between Joesph Wale V. Richard and Sara Austen and Others
For land in Staplehurst, Kent.
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