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

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

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 can achieve while retaining the same labeling costs i.e., training data-efficient (specifically, label efficient) models. We will describe the general principles of Self-Supervised learning which are applicable 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

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

· · · · · · · · · · · · · · · · · · · · · · · · · · 5240 14.17.4 PD schedules based on topology label· · · · · · · · · · · · · · · · · · · · · · · · · · · · · 5241 14.18 URI Formats of External Storage indexes">Global indexes for partitioned tables (GA) Global indexes can effectively improve the efficiency of retrieving non- �→ partitioned columns, and remove the restriction that a unique key �→ must scanning tasks based on node scale and hardware specifications. This �→ improves statistics collection efficiency by fully utilizing system �→ resources, reduces manual tuning, and ensures stable cluster �→ performance

· · · · · · · · · · · · · · · · · · · · · · · · · · 5219 14.17.4 PD schedules based on topology label· · · · · · · · · · · · · · · · · · · · · · · · · · · · · 5220 14.18 URI Formats of External Storage indexes">Global indexes for partitioned tables (GA) Global indexes can effectively improve the efficiency of retrieving non- �→ partitioned columns, and remove the restriction that a unique key �→ must scanning tasks based on node scale and hardware specifications. This �→ improves statistics collection efficiency by fully utilizing system �→ resources, reduces manual tuning, and ensures stable cluster �→ performance

· · · · · · · · · · · · · · · · · · · · · · · · · · 5167 14.17.4 PD schedules based on topology label· · · · · · · · · · · · · · · · · · · · · · · · · · · · · 5168 21 14.18 URI Formats of External Storage partitioned tables (experimental) Global indexes can effectively improve the efficiency of retrieving non- �→ partitioned columns, and remove the restriction that a unique key �→ must mance for high NDV data #9196 @guo-shaoge Before v8.3.0, TiFlash has low aggregation calculation efficiency during the first stage of HashAgg aggregation when handling data with high NDV (number of distinct

· · · · · · · · · · · · · · · · · · · · · · · · · · 5138 14.17.4 PD schedules based on topology label· · · · · · · · · · · · · · · · · · · · · · · · · · · · · 5139 21 14.18 URI Formats of External Storage loading �→ efficiency by up to 10 times For clusters with a large number of tables and partitions, such as SaaS �→ or PaaS services, improvement in statistics loading efficiency can �→ solve release. For more information, see documentation. 2.2.1.2 Reliability • Improve statistics loading efficiency by up to 10 times #52831 @hawkingrei SaaS or PaaS applications can have a large number of data