about using Helm with Istio ● Look at helm from a new perspective ● Helm helps automate Istio day-2 tasks ● Helm gitops #IstioCon HELM The package manager for Kubernetes It’s not just for installation

Patching & Upgrades 小版本升级、大版本升级、安全漏洞修复等等。 Data Migrations 迁移、同步、清洗、跨地域、灾备、多活等等。 DB Operator Day-2 Operations Operator 基础模型 第二部分 K8s 架构 Cache Informer 机制 Cache 如何获取到本地（内存中） Informer 启动后会通过 reflector

MachineConfig 创建内容，并通过包括 一组清单文件来将其提供给 openshift-install。 通 通过 过 Machine Config Operator 置 置备 备内核模 内核模块 块（ （day-2） ）：如果可以等到集群启动并运行后再添 加内核模块，则可以通过 Machine Config Operator (MCO) 部署内核模块软件。 在这两种情况下，每个节点都需要在检测到新内核时

8563b865c0345c86e9585f8c0b0a655612c.scope # for i in `ls cpuset.cpus tasks` ; do echo -n "$i "; cat $i ; done cpuset.cpus 1 tasks 32706 # grep ^Cpus_allowed_list /proc/32706/status Cpus_allowed_list: 创建输出文件夹： b. 生成 MachineConfig 安装 CR： 输 输出示例 出示例 7. 使用上一步中的参考 PolicyGenTemplate CR 生成并导出 day-2 配置 CR。运行以下命令： a. 为 day-2 CR 创建输出文件夹： b. 生成并导出第 2 天配置 CR： 该命令在 ./ref 文件夹中为单节点 OpenShift、三节点集群和标准集群生成示例组和特定于站

--disk-container "file:///containers.json" 2 $ watch -n 5 aws ec2 describe-import-snapshot-tasks --region ${AWS_DEFAULT_REGION} { "ImportSnapshotTasks": [ { "Description": bootstrap 节点，以便在 OpenShift Container Platform 集群初始化过程中使用。 $ watch -n 5 aws ec2 describe-import-snapshot-tasks --region ${AWS_DEFAULT_REGION} { "ImportSnapshotTasks": [ { "Description": days ago Docs: man:libvirtd(8) https://libvirt.org Main PID: 9850 (libvirtd) Tasks: 20 (limit: 32768) Memory: 74.8M CGroup: /system.slice/libvirtd.service ├─ 9850 /usr/sbin/libvirtd

--disk-container "file:///containers.json" 2 $ watch -n 5 aws ec2 describe-import-snapshot-tasks --region ${AWS_DEFAULT_REGION} { "ImportSnapshotTasks": [ { "Description": --disk-container "file:///containers.json" 2 $ watch -n 5 aws ec2 describe-import-snapshot-tasks --region ${AWS_DEFAULT_REGION} { "ImportSnapshotTasks": [ { "Description": 虚拟机可能无法引导集群节点，这会阻止虚拟机使用 RHCOS 镜像置备节点。这 可能是因为以下原因： https://libvirt.org Main PID: 9850 (libvirtd) Tasks: 20 (limit: 32768) Memory: 74.8M CGroup: /system.slice/libvirtd.service ├─ 9850 /usr/sbin/libvirtd

performance on a new task requires a large number of labels. 2. Compute Efficiency: Training for new tasks requires new models to be trained from scratch. For models that share the same domain, it is likely that the first few layers learn similar features. Hence training new models from scratch for these tasks is likely wasteful. Regarding the first limitation, we know that model quality can usually be naively expensive, and is unlikely to scale to the level that we want for complex tasks. To achieve a reasonable quality on non-trivial tasks, the amount of labeled data required is large too. For the second limitation

--disk-container "file:///containers.json" 2 $ watch -n 5 aws ec2 describe-import-snapshot-tasks --region ${AWS_DEFAULT_REGION} { "ImportSnapshotTasks": [ { "Description": --disk-container "file:///containers.json" 2 $ watch -n 5 aws ec2 describe-import-snapshot-tasks --region ${AWS_DEFAULT_REGION} { "ImportSnapshotTasks": [ { "Description": --disk-container "file:///containers.json" 2 $ watch -n 5 aws ec2 describe-import-snapshot-tasks --region ${AWS_DEFAULT_REGION} { "ImportSnapshotTasks": [ { "Description":

representations which obtains state-of-the-art results on a wide array of Natural Language Processing (NLP) tasks. This model is based on the BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding representations which obtains state-of-the-art results on a wide array of Natural Language Processing (NLP) tasks. This model is based on the BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding representations which obtains state-of-the-art results on a wide array of Natural Language Processing (NLP) tasks. This model is based on the BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

University 2020 • Flink requires a sufficient number of processing slots in order to execute all tasks of an application. • The JobManager cannot restart the application until enough slots become available JobManager failures ??? Vasiliki Kalavri | Boston University 2020 When the JobManager fails all tasks are automatically cancelled. The new JobManager performs the following steps: 1. It requests It requests processing slots. 3. It restarts the application and resets the state of all its tasks to the last completed checkpoint. Highly available Flink setup ??? Vasiliki Kalavri | Boston University