#IstioCon Secure your microservices with istio step by step JianFeng Ding, LuYao Zhong #IstioCon Agenda ● Istio identity ● mTLS in Isito ● Secure ingress traffic ● Authorize ingress traffic ● Authorize

1October 3, 2023 2 Libraries: A First Step Toward Standard C++ Dependency Management Bret Brown, C++ Infrastructure Lead, Bloomberg Bill Hoffman, CTO, KitwareHello! Welcome! Bret Brown C++ Infrastructure interop requirements In short, declaring libraries installed on a filesystem 6Goals ● ✓ A first step towards a robust packaging ecosystem ● ✓ Explicit metadata with a specification ● ✓ All architectures [3/6] c++ -DMYMATH_REQUIRED_FLAG … -c '/Users/hoffman/Work/My Builds/cmake/Help/guide/tutorial/Step3/tutorial.cxx' … [6/6] : && /c++ … CMakeFiles/Tutorial.dir/tutorial.cxx.o -o Tutorial MathFunc

Server, and it supports the extension of new databases with plugins. Ho How it w w it works orks St Step 0 (Pr ep 0 (Pre-kno e-knowledge) wledge) 1. Casdoor follows the authorization process built upon brief understanding of how OAuth 2.0 works. You can refer to this introduction to OAuth 2.0. St Step 1 (Aut ep 1 (Authorization R horization Request) equest) Your Application (which could be a website Authorization Request is completed. St Step 2 (Aut ep 2 (Authorization Grant) horization Grant) This step is straightforward: the user is redirected to the URL composed in Step 1, and the user will see the login

Overview AWS Lambda 2 Step 1: Upload AWS lambda code in any of languages AWS lambda supports, that is NodeJS, Java, Python , C# and Go. Step 2: These are few AWS services on which AWS AWS lambda can be triggered. Step 3: AWS Lambda which has the upload code and the event details on which the trigger has occurred. For example, event from Amazon S3, Amazon API Gateway, Dynamo dB, Amazon SNS, Amazon Kinesis, CloudFront, Amazon SES, CloudTrail , mobile app etc. Step 4: Executes AWS Lambda Code only when triggered by AWS services under the scenarios such as:  User uploads

*下面是 vSphere 上创建虚拟机的步骤： A1. 创建 k8s-master CPU：2 核, 内存：8GB，系统盘：40GB，docker 数据盘：80GB step1. 从模板上新建虚拟机 Step2. 配置虚拟机网络 打开虚拟机的控制台： 设置主机名： hostnamectl set-hostname k8s-master 设置网络： cd /et NETMASK=255.255.255.0 DNS1=202.114.200.254 DNS2=114.114.114.114 IPV6INIT=no 打开虚机网络： Step3. 虚拟机磁盘 2 分区&格式化 fdisk -l fdisk /dev/sdb 依法选择 n,p,1,t,l,8e,w fdisk –l pvcreate /dev/sdb1 数据盘：200GB 【注】所有节点(k8s-master, k8s-node1, k8s-node2, k8s-node3)均需做以下 B~D： B. 升级&配置 centos7 Step1. 升级 linux 内核 uname –r wget https://cbs.centos.org/kojifiles/packages/kernel/4.9.220/37.el7/x86_64/kernel-4

130 8.3 Big-Step Semantics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 8.4 Properties of the Big-Step Semantics . . . . . . . . . . . . . . . . . . . 134 8.5 Small-Step Semantics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 8.6 Properties of the Small-Step Semantics . . . . . . . . . . . . . . . . . 138 8.7 Parallelism . . . . . . . . . . . . . . . . . . . . . . will apply the procedure to find a term of type (α → β → γ) → ((β → α) → β) → α → γ Initially, only step 1 is applicable, with σ := α → β → γ and τ := ((β → α) → β) → α → γ. (Recall that → is right-associative:

Keras installation is quite easy. Follow below steps to properly install Keras on your system. Step 1: Create virtual environment Virtualenv is used to manage Python packages for different projects 4 Windows user can use the below command, py -m venv keras Step 2: Activate the environment This step will configure python and pip executables in your shell path. Linux/Mac OS Windows users move inside the “kerasenv” folder and type the below command, .\env\Scripts\activate Step 3: Python libraries Keras depends on the following python libraries.  Numpy  Pandas 

equally important, thus selecting the most informative features is crucial for making the training step efficient. In the case of visual, textual, and other multimodal data, we often construct the features get to solving the CBOW task8 step by step and train an embedding table in the process. We will start with creating a vocabulary of words in the first step. The second step assigns a unique index to the vectorization. An embedding table with a row for each word is initialized in the third step. Finally, in the fourth step, we train a model which trains the embedding table along with it. We use a single hidden

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223 4 WEB Step . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . new data is inserted into database. 3.6.2 Backup existing Zabbix database This is very important step. Make sure that you have backup of your database. It will help if upgrade procedure fails (lack of and proxy configuration files. 3.6.6 Upgrade database Attention: Database upgrade is a required step when upgrading from one major Zabbix version to another, such as from 1.6 to 1.8. It is not required

sparsity_rate variable’s value will further reduce the size of the sparsified and compressed size. To take a step back, in the above exercise, we pruned the weights with the smallest absolute values (magnitudes) pruning because we prune the model iteratively for rounds. The fine-tuning phase after a pruning step gives a chance to the network to reconfigure itself after the selected weights have been pruned. an exponentially smoothed estimate of over time. For instance, the momentum of weight at training step is given by: 2 Dettmers, Tim, and Luke Zettlemoyer. "Sparse networks from scratch: Faster training