#IstioCon Is Your Virtual Machine Really Ready-to-go with Istio? Kailun Qin, Intel Haoyuan Ge #IstioCon Quick Summary (from Google Cloud Next ’19 [1]) VM works on Istio! [1] Istio Service Mesh and Multi Clouds #IstioCon Istio VM Integration is? A Tumultuous Odyssey… [1] Istio 1.8: A Virtual Machine Integration Odyssey, Jimmy Song #IstioCon V0.2 Mesh Expansion ● Prerequisites ○ IP connectivity workloads themselves #IstioCon V1.6-1.8 Better VM Workload Abstraction Item Kubernetes Virtual Machine Basic schedule unit Pod WorkloadEntry Component Deployment WorkloadGroup Service registry

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 f(x) is usually a highly non-linear function • Feedforward networks are of extreme importance to machine learning practioners • The conventional neural networks (CNN) used for object recognition from photos

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 ● Tensors year ■ ref: link1, link2 Some knowledge of NumPy will also be useful! What is PyTorch? ● An machine learning framework in Python. ● Two main features: ○ N-dimensional Tensor computation (like NumPy) translation, synthesis, ...) ○ Most implementations of recent deep learning papers ○ ... References ● Machine Learning 2021 Spring Pytorch Tutorial ● Official Pytorch Tutorials ● https://numpy.org/ Any questions

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 ωT x + b = 0 (1) where ω ∈ Rn defined as γ = min i γ(i) (6) 1 ? ? ! ? ! Figure 1: Margin and hyperplane. 2 Support Vector Machine 2.1 Formulation The hyperplane actually serves as a decision boundary to differentiating positive we can construct a infinite number of hyperplanes, but which one is the best? Supported Vector Machine (SVM) answers the above question by maximizing γ (see Eq. (6)) as follows max γ,ω,b γ s.t. y(i)(ωT

Lecture 6: Support Vector Machine Feng Li Shandong University fli@sdu.edu.cn December 28, 2021 Feng Li (SDU) SVM December 28, 2021 1 / 82 Outline 1 SVM: A Primal Form 2 Convex Optimization Review (b < 0 means in opposite direction) Feng Li (SDU) SVM December 28, 2021 3 / 82 Support Vector Machine A hyperplane based linear classifier defined by ω and b Prediction rule: y = sign(ωTx + b) Given: " such that min& ' & !() & + " = 1 Feng Li (SDU) SVM December 28, 2021 14 / 82 Support Vector Machine (Primal Form) Maximizing 1/∥ω∥ is equivalent to minimizing ∥ω∥2 = ωTω min ω,b ωTω s.t. y(i)(ωTx(i)

Building resilient systems inside the mesh: abstraction and automation of Virtual Service generation Vladimir Georgiev, Thought Machine #IstioCon Sync calls failures inside the mesh ● Everyone says to implement this to be language agnostic? #IstioCon Virtual Services API ● Solves our problems, but… ● All Service Owners must be aware of the Virtual Services API in order to define their SLOs. ● Potential errors when dealing with YAMLs. ● Potential drift between the state of the service API and the Virtual Service config. ● Hard to manage when having hundreds of services. #IstioCon Abstracting to proto

A Day in the Life of a Data Scientist Conquer Machine Learning Lifecycle on Kubernetes Brian Redmond • Cloud Architect @ Microsoft (18 years) • Azure Global Black Belt Team • Live in Pittsburgh, PA idle • Parallel training instead of sequential: huge time saver for large trainings Kubeflow • Machine Learning Toolkit for Kubernetes • To make ML workflows on Kubernetes simple, portable, and scalable

first virtual machine . . . . . . . . . . . . . . . . . . . . . 17 1.7 Running your virtual machine . . . . . . . . . . . . . . . . . . . . . . . . 21 1.7.1 Keyboard and mouse support in virtual machines Saving the state of the machine . . . . . . . . . . . . . . . . . . 24 1.8 Snapshots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 1.9 Virtual machine configuration . . . . . . . . . . . . . . . . . . 28 1.10 Deleting virtual machines . . . . . . . . . . . . . . . . . . . . . . . . . . 29 1.11 Importing and exporting virtual machines . . . . . . . . . . . . . . . . . 29

. . . . . . . . . . 14 1.7 Creating your first virtual machine . . . . . . . . . . . . . . . . . . . . . . . . . 16 1.8 Running your virtual machine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 1.8.5 Resizing the machine’s window . . . . . . . . . . . . . . . . . . . . . . 21 1.8.6 Saving the state of the machine . . . . . . . . . . . . . . . . . . . . . . 22 1 . . . . . . . . . . . . . . . . . 25 1.10 Virtual machine configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 1.11 Removing virtual machines . . . . . . . . . . . . . . . . . .

. . . . . . . . . . 14 1.7 Creating your first virtual machine . . . . . . . . . . . . . . . . . . . . . . . . . 16 1.8 Running your virtual machine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 1.8.5 Resizing the machine’s window . . . . . . . . . . . . . . . . . . . . . . 21 1.8.6 Saving the state of the machine . . . . . . . . . . . . . . . . . . . . . . 22 1 . . . . . . . . . . . . . . . . . 25 1.10 Virtual machine configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 1.11 Removing virtual machines . . . . . . . . . . . . . . . . . .