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 Repeatable/consistent • CI/CD • This has worked well for App Dev. Now time for AI/ML • But, must ensure data scientist are not hindered by structure Why Containers, Kubernetes & Helm? • Container • Contains Scalable • Easy to explore hyper-parameters space • Easy to do distributed training But really, Data Scientists shouldn’t have to care about containers, kubernetes and all that stuff • Pachyderm can

贯彻全面 Serverless 化战略，提供计算、存储、数据库等全领域的 Serverless 服务。微软 发力低/无代码领域，其发布的 Power Platform 已经与 Office 365、 Dynamics 365 以及 Azure 三大生态充分打通，形成完整的技术生态。 二是更注重软硬协同，优化性能。在算力多样化、节点高密化、载 体细粒度化等诉求下，底层硬件在云计算的驱动下也因云而变。2022 例，从 2019 年开始其陆续向 OpenAI 投资 130 亿美元，在资金与独家算力 的支持下 OpenAI 得以开展模型训练与算法优化，并逐步对外输出产 品能力，微软将在 Azure、Office 等产品中融合 OpenAI 沉淀的相关 能力，快速提升智能化水平。 四、云计算加速催生算力服务新范式 随着人工智能大模型、全真互联等应用发展，产业对多样性异 构算力、高质量确定性网络的需求不断提升。在云计算的持续推动

Kubernetes支援Ceph、NFS、雲端儲存、NetApp、vSphere、 FlexVolume及PureStorage. • Red Hat Openshift支援Ceph/Rook、Red Hat OpenShift Data Foundation、 GlusterFS、NFS、Cinder及Flexvolume • SUSERancher支援GlusterFS、NFS、vSphere及Longhorn 12. 監控及作業管理 such changes. Canonical Limited, Registered in England and Wales, Company number 110334C Registered Office: 12-14 Finch Road, Douglas, Isle of Man, IM99 1TT VAT Registration: GB 003 2322 47 力悅官方臉書 力悅資安頻道

com/developer/article/1627330 Step1: 安装 docker-ce # 安装需要的支撑软件 yum install -y yum-utils device-mapper-persistent-data lvm2 # 添加 yum 源 yum-config-manager --add-repo https://download.docker.com/linux/centos/docker-ce configmap vi 3-configmap.yaml kind: ConfigMap apiVersion: v1 metadata: name: traefik-config data: traefik.yaml: |- serversTransport: insecureSkipVerify: true api: 输入 step3 上获得的 token Step5: 生成 SSL 证书的 secret //生成自签名 SSL 证书 openssl req -x509 -nodes -days 365 -newkey rsa:2048 -keyout tls.key -out tls.crt -subj "/CN=k8s-dashboard.xxx.com" //生成 SSL 证书的 secret

locations with the use of groundbreaking technologies, to enable decision-making at the point of data collection. Fast, insight-driven decision-making in highly dynamic and dangerous conditions is Allen’s innova- tive edge computing solution, SmartEdge, addresses the increasing need to gather data in real time and perform analysis at the point of collection, supplying imme- diate insight which battlefields. The an- alytics enabled and performed by Smart- Edge allow battalions to make real-time, data-driven decisions which dramatically improve operational outcomes and in- crease the probability of

intensive cryptanalytic attacks ● A cryptoperiod is the time during which a key is used to encrypt data Key rotation: cryptoperiod There are lots of factors that influence the choice of cryptoperiod Strength of cryptographic algorithms used ○ Implementation ○ Operating environment ○ Volume of data ○ Re-keying method ○ Number of key copies ○ Personnel turnover ○ Threat model ○ New and disruptive cardholder data against disclosure and misuse. 3.6 Fully document and implement all key-management processes and procedures for cryptographic keys used for encryption of cardholder data, including

scale • Reduce the learning curve for customer and ourselves • Get consistent user experience and data, leverage with PaaS capability • Facilitate our PaaS and micro-service product Kubernetes Capabilities/Advantages agent to collecting log data ElasticSearch ElasticSearch Monitor/Alert Service CronJob Node Pod Node Pod Unified logging、monitoring、alert with PaaS Consistent data Node group of build nodes configuration and history in MySQL • Logging in central logging service - ElasticSearch • Metric data in monitoring system - prometheus • Alertmanager to invoke various alert and related actions docker

FailedPostStartHook, Unhealthy… Trace system Increase of SLO Data Collect Audit log Event The unhealthy node Monitoring Isolation Recover Degrade Data Analysis Failures/Machine Failures/Reason Report User Storage Analysis Platform Trace Report Weakness The trace system Data Collect: Collect Audit log for the whole cluster. Data analysis: Analyze failure reason if pod is failed. Reason analysis: Analyze kubelet metrics daemonset metrics node load slo metrics csi metrics dirty data With huge amount of metrics data collected, statistical methods can be used to check whether the node is healthy

docker # docker info ★配置docker服务使用systemd去管理（以及信任本地镜像仓库） # vi /etc/docker/daemon.json { "data-root": "/docker_data", "registry-mirrors": [ "h�ps://cof-lee.com:5443" ], "insecure-registries": [ "cof-lee /etc/docker/daemon.json <data-root": "/docker_data", "registry-mirrors": [ "h�ps://cof-lee.com:5443" ], "insecure-registries": [ "cof-lee cm-name2.yaml <data: key1: value1 key2: value2 key3: value3 EOF # kubectl apply -f cm-name2.yaml #创建

node-local-path /data/all-log pod-path /data/all-log kafka-cluster elasticsearch-cluster logstash-cluster kafka-groupid=es topic applog logstash kafka-groupid=file file-storage mkdir -p /data/all- lo pod * app-log log* daemonsets filebeat 容器启动挂载宿主/data/all-log到容器/data/all-log 容器启动时创建namespace和pod-name 创建软连接到logs目录 daemonset在每一个k8s-node节点启动 挂载/data/all-log目录 实时扫描发现新目录日志 logstash通过不同的消费groupid来消费同一