集群的增强型自动机器管理功能，另一些任务则要手动完成。本文所述的任务并非对所有安装类型都适用。 ## 目录 第1章 机器管理概述 ..... 4 1.1. MACHINE API 概述 ..... 4 1.2. 管理计算机器 ..... 5 1.3. 管理 CONTROL PLANE 机器 ..... 6 1.4. 将自动扩展应用到 OPENSHIFT CONTAINER PLATFORM 集群 ..... 6 6 1.5. 在用户置备的基础架构上添加计算机器 ..... 6 1.6. 在集群中添加 RHEL 计算机器 ..... 6 第2章 使用 MACHINE API 管理计算机器 ..... 8 2.1. 在 ALIBABA CLOUD 上创建计算机器设置 ..... 8 2.2. 在 AWS 上创建计算机器集 ..... 13 2.3. 在 AZURE 上创建计算机器设置 第13章 管理 CONTROL PLANE 机器 216 13.1. 关于 CONTROL PLANE 机器集 216 13.2. CONTROL PLANE 机器集入门 217 13.3. CONTROL PLANE 机器集配置 221 13.4. 使用 CONTROL PLANE 机器集管理 CONTROL PLANE 机器 234 13.5. CONTROL PLANE 弹性和恢复

# Machine Learning Lecture 10: Neural Networks and Deep Learning Feng Li fli@sdu.edu.cn https://funglee.github.io School of Computer Science and Technology Shandong University Fall 2018 ## Deep Feedforward $ is usually a highly non-linear function • Feedforward networks are of extreme importance to machine learning practitioners - The conventional neural networks (CNN) used for object recognition from backpropagation equations provide us with a way of computing the gradient of the cost function • Input: Set the corresponding activation $ a^{[1]} $ for the input layer • Feedforward: For each $ l = 2, 3

# Optimization for number of goroutines using feedback control Yusuke MIYAKE / Pepabo R&D Institute, GMO Pepabo, Inc. 2019.07.25 GopherCon 2019  ## Performance metrics • The metrics upper limit is calculated on running. 1. Set the target

## Set Sail for a Ship-Shape Istio Release Brian Avery / twitter: @briansvgs / Red Hat Senior Software Engineer Eric Van Norman / twitter: @kf0s / IBM Senior Software Engineer ## First a Quick Note Istio upgrades ## Upgrade Working Group - Stability • Standards and processes ☐ Control plane behavior ☐ Data plane communication - Promote revision-based upgrades to stable and support skip-level

## Machine Learning Pytorch Tutorial TA：曾元(Yuan Tseng) 2022.02.18 ## Outline Background: Prerequisites & What is Pytorch? • Training & Testing Neural Networks in Pytorch • Dataset & Dataloader [Image](/uploads/documents/7/6/e/1/76e1a67e96719ae74c41b110fe07bfe6/p3_2.jpg) ## What is PyTorch? - An machine learning framework in Python. • Two main features: ○ N-dimensional Tensor computation (like NumPy) Testing Step 5. Entire Procedure ## Neural Network Training Setup dataset = MyDataset(file) tr_set = DataLoader(dataset, 16, shuffle=True) model = MyModel().to(device) criterion = nn.MSELoss() optimizer

## Debugging the BPF Virtual Machine eBPF Summit ## Why? ● Debugging is useful to understand how things work ● Sometimes, eBPF programs can’t even load - I couldn’t find good resources on this, so so, here I am ● I break lots of eBPF programs - The BPF Virtual machine is not easy to understand ## The approach ## The BPF subsystem lives in the kernel ![Image](/uploads/documents/7/1/6/1/7161fc3

Back to Basics: The Abstract Machine Bob Steagall CppCon 2020 ## KEWB COMPUTING ## Overview/Goals • Describe abstract machines in general • Describe the C++ abstract machine specifically • Language goals • Important components of the abstract machine, and their relationships • Provide a useful overview of the C++ abstract machine ## What is an Abstract Machine? “Abstract machines are machines because between the high level of a programming language and the low level of a real machine. The instructions of an abstract machine are tailored to the particular operations required to implement operations of

## How HP set up secure and wise platform with Istio John Zheng/ john.zheng@hp.com IstioCon HORIZON ## Agenda HP Horizon platform design with Istio ➤ Secure Platform Wise Platform Excellent Observability Different namespace • Project runs as tenant, need control rights Solution cluster connect core cluster with Istio multi-cluster - Replicated control planes Some standalone cluster without Istio can access For request from ingressgateway, need verify token ## Using Authorization Policy enables access control on workloads in the mesh. ![Image](/uploads/documents/b/c/2/3/bc2358dba914e88d399d9dd7a776e9a1/p10_1

# Lecture Notes on Support Vector Machine Feng Li fli@sdu.edu.cn Shandong University, China ## 1 Hyperplane and Margin In a n-dimensional space, a hyper plane is defined by $$ \omega^{T}x+b=0 $$ so-called margin of $ x_{0} $ (with respect to the hyperplane $ \omega^{T}x + b = 0 $ ). Now, given a set of m training data $ \{(x^{(i)}, y^{(i)})\}_{i=1,\ldots,m} $ , we first assume that they are linearly \right)^{T}x^{(i)}+\frac{b}{\left\|\omega\right\|}\right) $$ With respect to the whole training set, the margin is defined as $$ \gamma=\min_{i}\gamma^{(i)} $$ ![Image](/uploads/documents/c/a/7/4

## Lecture 6: Support Vector Machine Feng Li Shandong University fli@sdu.edu.cn December 28, 2021 ## Outline  1 SVM: moving it parallelly along $ \omega $ (b < 0 means in opposite direction) ## Support Vector Machine • A hyperplane based linear classifier defined by $ \omega $ and b • Prediction rule: $ y = (ω/||ω||)T x(i) + b/||ω||) • Scaling (ω, b) does not change γ(i) • With respect to the whole training set, the margin is written as $$ \gamma=\min_{i}\gamma^{(i)} $$ ![Image](/uploads/documents/4/1/3/