HUAWEI CLOUD Microservice Tool Improves Development Efficiency Department: Application Platform Service Author: Wang Qijun Date: 2019-09-20 Security Level: Contents 1. Tool for Splitting Monolithic Process-level Overall availability Low High Continuous evolution Difficult Easy Communication efficiency Low High Technology stack selection Restricted Flexible Scalable Restricted Flexible Reusability verification Tool for Splitting Monolithic Applications into Microservices Improves Development Efficiency Supported processes Methodology • ThoughtWorks 5 Steps and 1 Phase • DDD aggregation • Event

rapid growth. We will establish our motivation behind seeking efficiency in deep learning models. We will also introduce core areas of efficiency techniques (compression techniques, learning techniques, automation Our hope is that even if you just read this chapter, you would be able to appreciate why we need efficiency in deep learning models today, how to think about it in terms of metrics that you care about, and models is rate-limited by their efficiency. While efficiency can be an overloaded term, let us investigate two primary aspects: Training Efficiency Training Efficiency involves benchmarking the model

bring significant efficiency gains during the training phase, which is the focus of this chapter. We start this chapter with an introduction to sample efficiency and label efficiency, the two criteria Our journey of learning techniques also continues in the later chapters. Learning Techniques and Efficiency Data Augmentation and Distillation are widely different learning techniques. While data augmentation breadth as efficiency? To answer this question, let’s break down the two prominent ways to benchmark the model in the training phase namely sample efficiency and label efficiency. Sample Efficiency Sample

deployment tuning assistant that significantly reduces deployment costs and improves optimization efficiency. Project Introduction HPCRunner is composed of two parts: HPC dependency management and HPC application applications. HPC Deployment Tuning Assistant: HPCRunner Objectives 20%↓ deployment cost 20%↑ tuning efficiency Unified deployment Flexible operation Template-based tuning One-click deployment One-click compilation communication library, and porting and tuning tool chain, significantly improving the application running efficiency of the Kunpeng platform. Repositories https://gitee.com/openeuler/hpcrunner Features • HPCRunner

quality with a small number of labels. As we described in chapter 3’s ‘Learning Techniques and Efficiency’ section, labeling of training data is an expensive undertaking. Factoring in the costs of training a new task: 1. Data Efficiency: It relies heavily on labeled data, and hence achieving a high performance on a new task requires a large number of labels. 2. Compute Efficiency: Training for new tasks Model reuse by itself also is a powerful attribute of this scheme, and lends itself to compute efficiency since only have to train the model on a small number of examples, saving training time compute

JP Morgan End-to-End AI Modernization – 2023-2025E, per JP Morgan We have high hopes for the efficiency gains we might get [from AI]… …Certain key subsets of the users tell us they are gaining several alerts. It leverages machine learning to improve decision-making at the restaurant level, enhancing efficiency, reducing waste, and supporting staff productivity. ‘Traditional’ Enterprise AI Adoption = Rising – one that builds on recent exponential gains in model scale, training data, and computational efficiency. Timelines for AGI remain uncertain, but expert expectations have shifted forward meaningfully

costs and inference efficiency of DeepSeek 67B (Dense) and DeepSeek-V2. Contents 1 Introduction 4 2 Architecture 6 2.1 Multi-Head Latent Attention: Boosting Inference Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.2.3 Training and Inference Efficiency . . . . . . . . . . . . . . . . . . . . . . . . 16 4 Alignment 16 4.1 Supervised Fine-Tuning Multi-Head Attention (MHA) (Vaswani et al., 2017) poses a significant obstacle to the inference efficiency of LLMs. Various approaches have been explored to address this issue, including Grouped-Query Attention

18What are types used for in programming? • Types are used for: • Abstraction • Documentation • Efficiency • Expressivity • Detecting errors • Safety 19What are types used for in programming? • Types Documentation • Efficiency • Carefully chosen type can lead to more efficient code. 22What are types used for in programming? • Types are used for: • Abstraction • Documentation • Efficiency • Expressivity 23What are types used for in programming? • Types are used for: • Abstraction • Documentation • Efficiency • Expressivity • Detecting errors • Doing something (by accident) that does not “fit” the type