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Ray on Apache Spark™ Apache Spark™上的Ray.pdf

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1、Ray on SparkBen Wilson,DatabricksJiajun Yao,AnyscaleDatabricks2023Who we are Ben WilsonJiajun YaoWorks with ML open source software at DatabricksMLflow maintainerSoftware engineer at AnyscaleRay committerAgenda What is Ray What is Ray-on-Spark Why Ray-on-Spark How to use Ray-on-Spark Demos How does

2、Ray-on-Spark work Future workWhat is RayWhat is Ray An open-source unified distributed framework that makes it easy to scale AI and Python applications.An ecosystem of Python libraries(for scaling ML and more).Makes distributed computing easy and accessible to everyone.Runs on laptop,public cloud,K8

3、s,on-premise.Run anywhereGeneral-purpose framework for distributed computingLibrary+app ecosystemRay coreWhat is RayWhat is Ray def read_array(file):#read ndarray“a”#from“file”return adef add(a,b):return np.add(a,b)a=read_array(file1)b=read_array(file2)sum=add(a,b)class Counter(object):def _init_(se

4、lf):self.value=0 def inc(self):self.value+=1 return self.valuec=Counter()c.inc()c.inc()FunctionClassWhat is Ray ray.remote def read_array(file):#read ndarray“a”#from“file”return a ray.remotedef add(a,b):return np.add(a,b)a_ref=read_array.remote(file1)b_ref=read_array.remote(file2)sum_ref=add.remote(

5、a,b)sum=ray.get(sum_ref)ray.remoteclass Counter(object):def _init_(self):self.value=0 def inc(self):self.value+=1 return self.valuec=Counter.remote()c.inc.remote()c.inc.remote()Function-TaskClass-ActorWhat is RayHigh-level libraries that make scaling easy for both data scientists and ML engineers.Wh

6、at is Ray1,000+OrganizationsUsing Ray25,000+GitHubstars5,000+RepositoriesDepend on Ray820+CommunityContributorsWhat is Ray-on-Spark A library to deploy Ray clusters on Spark and run Ray applications.Ray coreWhy Ray-on-Spark User asksSpark users want to use both Spark MLlib and Ra

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本文主要介绍了Ray这一开源统一分布式框架,以及Ray-on-Spark库。Ray能够简化AI和Python应用的分布式计算,支持在笔记本、公共云、Kubernetes和本地环境中运行。Ray的核心是一个通用的分布式计算库,结合了应用程序生态系统。文章中提到的关键数据包括:1000多家组织使用Ray,25000多个GitHub星标,5000多个依赖Ray的仓库,以及820多个社区贡献者。Ray-on-Spark是一个库,可以在Spark集群上部署Ray集群并运行Ray应用程序。Ray-on-Spark的优势在于,用户可以在同一物理集群上共享成本,同时运行Ray和Spark应用程序,无需管理两个独立的物理集群。使用Ray-on-Spark的步骤包括安装Ray、启动Ray集群、运行Ray应用程序,以及停止Ray集群。最后,文章提到了Ray-on-Spark的未来工作,包括支持自动扩展和Delta数据源在Ray Data中的支持,并呼吁读者在Databricks集群或Spark独立集群上尝试Ray-on-Spark。
"Ray-on-Spark是什么?" "如何使用Ray-on-Spark?" "Ray-on-Spark的未来发展计划是什么?"
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