#IstioCon Local Istio Development John Howard / @howardjohn / Google #IstioCon Fully Cloud docker push kubectl apply docker pull #IstioCon Fully Cloud docker push kubectl apply docker pull requests #IstioCon Thank you! For more information: ● https://github.com/howardjohn/local-istio-development

ON=true。 除了构建之外，指定的环境变量也可以在运行的应用程序本身中使用。例如，使 Rails 应用程序在 development 模式而非 production 模式中启动： 在 .s2i/environment 文件中添加 RAILS_ENV=development。 其他 其他资 资源 源 使用镜像部分中提供了各个镜像支持的环境变量的完整列表。 5.2.3.2. 使用 使用 出示例 $ oc create configmap [options] $ ls example-files game.properties ui.properties $ cat example-files/game.properties enemies=aliens lives=3 enemies.cheat=true enemies.cheat.level=noGoodRotten configmap game-config \ --from-file=example-files/ $ oc describe configmaps game-config Name: game-config Namespace: default Labels: Annotations: Data game.properties:

material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Alibaba’s products remains (community must still agree). Merit gets you privileges: commit access, voting on committers. Open Development - For Everything Asynchronous communication is critical - feedback, critique, new ideas, others unless there's a reason for private list. Don't be a jerk; avoid the poisonous people. The core development activity must be in English. Decision Making - Consensus & Votes Consensus within community

Cluster https://dapr.io State of Enterprise Developers What is holding back micro-service development? Hard to incrementally migrate from existing code to a microservices architecture Programming Distribution and failover Turn-based concurrency State management Timers Reminders Host/Pod Video Game Enemy Pod Y Actor C Actor D Pod X Actor A Actor B Post http://localhost:3500/v1.0/actor

write in many languages They want to leverage existing code What is holding back serverless development? Frequently need to incrementally migrate from existing and legacy code Serverless runtimes Turn-based concurrency • State management • Timers • Reminders Host/Pod Host/Pod Host/Pod Video Game Enemy Virtual Actors with Dapr Pod X Actor A Actor B Pod Y Actor C Actor D App POST ht

文本 标签 A great game Sports The election was over Not Sports Very clean match Sports A clean but forgettable game Sports It was a close election Not Sports 我们想要计算句子“A very close game”是 Sports 的概率以及它不 close game )这个句子的类别是Sports的概率 20 3.朴素贝叶斯案例 特征：单词的频率 已知贝叶斯定理?(?|?) = ?(?|?)?(?) ?(?) ，则： ?( Sports | a very close game ) = ?( a very close game | Sports ) × ?( Sports ) ?( a very close game ) 由 数，只是比较 ?( a very close game | Sports ) × ?( Sports ) 和 ?( a very close game | Not Sports ) × ?( Not Sports ) 21 3.朴素贝叶斯案例 我们假设一个句子中的每个单词都与其他单词无关。 ) ?( a very close game ) = ?(?) × ?( very ) × ?(

出示例 $ oc create configmap [options] $ ls example-files game.properties ui.properties $ cat example-files/game.properties enemies=aliens lives=3 enemies.cheat=true enemies.cheat.level=noGoodRotten configmap game-config \ --from-file=example-files/ $ oc describe configmaps game-config Name: game-config Namespace: default Labels: Annotations: Data game.properties: properties: 158 bytes ui.properties: 83 bytes $ oc get configmaps game-config -o yaml apiVersion: v1 data: game.properties: |- enemies=aliens lives=3 enemies.cheat=true enemies

$ oc create configmap [options] $ ls example-files game.properties ui.properties $ cat example-files/game.properties enemies=aliens lives=3 enemies.cheat=true enemies.cheat.level=noGoodRotten configmap game-config \ --from-file=example-files/ $ oc describe configmaps game-config Name: game-config Namespace: default Labels: Annotations: Data game.properties: 以下示例流程概述了如何从文件 以下示例流程概述了如何从文件创 创建配置映射。 建配置映射。 注意 注意 $ oc get configmaps game-config -o yaml apiVersion: v1 data: game.properties: |- enemies=aliens lives=3 enemies.cheat=true enemies

Spring 2020 2/06: Notions of time and progress Vasiliki Kalavri | Boston University 2020 Mobile game application • input stream: user activity • output: rewards based on how fast the user meets goals the local clock where an event is being processed • a processing-time window wouldn’t account for game activity while the train is in the tunnel • results depend on the processing speed and aren’t deterministic

one (the best) configuration left. An intuitive way to think about it is to imagine a multiplayer game with multiple levels where a few best performing players are promoted to the next level until we have on the target dataset. The controller model learns to generate better architectures as the search game progresses. Figure 7-4: An overview of Neural Architecture Search framed as a Reinforcement Learning