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RUCK 2017 R에 날개 달기 - Microsoft R과 클라우드 머신러닝 소개
- 1. 한석진 / 부장 마이크로소프트 고급분석 담당 R에 날개 달기 Microsoft R이 더할 수 있는 것
- 3. Microsoft의 고급분석 솔루션으로서의 R (뭐가 더해지나) 자유롭게 선택하는 Compute Context Spark와의 새로운 만남 분석모델을 배포하는 새로운 방법
- 6. SQL Server on Linux Microsoft joins Eclipse Foundation HD Insight managed service on Linux Azure Marketplace 60% of all images in Azure Marketplace are based on Linux/OSS Partnership with the Linux Foundation for Linux on Azure certification 600 Million+ Lines of open source code submitted to GitHub by Microsoft engineersMicrosoft OpenSource Hub Wim Coekaerts Oracle’s Mr. Linux joins Microsoft 1 out of 3 1 out of 3 VMs on Azure run Linux, and more than half of all new VMs run Linux Acquisition Jenkins project on Azure 제품 측면 파트너쉽 측면 제공 서비스 측면 Ross Gardler President Apache SW Foundation 문화 측면 Partnership Run Linux on Windows natively C:Usersmarkhill> bash root@localhost: #
- 10. Rattle
- 12. Deep Neural Nets Fast forest Fast tree (L1 and L2) (L1 and L2) One-Class SVM MML
- 13. Pretrained Image Featurization FeaturizeImage() – Used to identify parts of images – People, things, animals, etc. FeaturizeText() – Returns Ngram digest & counts from many partitions of text data Text Featurization Featurizer Featurizer Ngrams (phrases) counts Text Data Sets Featurizer ngram ngram ngram Image Data Sets Image contents Featurizer Featurizer Image contents found Featurizer GetSentiment() – Pretrained to return sentiment score (0-1) – English only for now Pretrained Sentiment Analysis Featurizer Featurizer getSentime nt() Text Data Sets Featurizer Sentiment Score
- 14. rxEnsemble: – Returns ensembled model combining multiple types – Ensembling settings balance speed & accuracy Many Small Models Ensemble Learning Model 1 Model 2 rxEnsemble Single or Distributed Data Sets ManyModels (w/ rxExecBy): – Used to run model on each of many partitions. – Returns one model trained to per cohort (partition) of data. P3 Model P1 P2 P1 Model P2 Returns a set of Models Data Partitioned by Cohort Model P3 Model P1 Model P2 Model P3 Ensemble Model
- 15. Compute Context
- 16. Defines where the processing happens Current set compute context determines processing location Write Once Deploy Anywhere (WODA) by changing compute context
- 17. rxSetComputeContext(RxSpark(…)) inData <- RxTextData(“/ds/AirOnTime.csv”, fileSystem = RxHdfsFileSystem()) model <- rxLogit(DEP_DEL15 ~ DAY_OF_WEEK + UNIQUE_CARRIER, data = inData) Spark를 쓰기로 선택 다른 처리/분석 코드는 불변
- 18. Context Switching – local, Spark Cluster (+ SQL Server) Demo
- 19. Microsoft R with Spark
- 20. Predict airline delays from historical flight data and weather information Data Sets Airline delay (2009 to 2012) – 44 variables Weather information – 11 variables Demo Data manipulation using sparklyr Interoperability between sparklyr and RevoScaleR Supervised learning using RevoScaleR Predict airline delay per origin airport (small data many models) Interoperability between H2O and RevoScaleR
- 21. R R R R R R R R R R ScaleR Production RStudio Server Community/Pro Microsoft R Server 1. Copy 2. Stream 3. Send
- 22. R Server on HDInsight – 수십억 건으로 확장 Configuration: • HDI cluster size: 100 nodes - All nodes: D4 (8 cores, 28GB) • Dataset: Airlines dataset - transformed, and duplicated • Number of parameters: 370 • Format: CSV • fs.azure.selfthrottling.read.factor=1 0 200 400 600 800 1000 1200 1400 1600 0 5 10 15 20 25 ElapsedTime(seconds) Billions of rows rxLogit on a 100 node HDInsight Cluster
- 23. Configuration: • 1 Edge Node: 16 cores, 112GB • 4 Worker Nodes: 16 cores, 112GB • Dataset: Duplicated Airlines data (.csv) • Number of columns: 26 E2E Process: • Load Data from .csv • Transform Features • Split Data: Train + Test • Fit Model: Logistic Regression (no regularization) • Predict and Write Outputs 23http://tinyurl.com/Strata2017R/Performance_Comparison
- 24. 모델을 손쉽게 배포하는 방법! Operationalizing Analytic Models
- 25. • Turn R analytics Web services in one line of code; • Swagger-based REST APIs, easy to consume, with any programming languages, including R! • Deploying web service server to any platform: Windows, SQL, Linux/Hadoop • On-prem or in cloud • Fast scoring, real time and batch • Scaling to a grid for powerful computing with load balancing • Diagnostic and capacity evaluation tools • Enterprise authentication: AD/LDAP or AAD • Secure connection: HTTPS with SSL/TLS 1.2 • Enterprise grade high availability Instant Deployment Deploy to Anywhere Fast and Scalable Secure and Reliable Unique
- 26. Build the model first Deploy as a web service instantly
- 27. Function Description publishService Publish a predictive function as a Web Service deleteService Delete a Web Service getService Get a Web Service ListServices List the different published web services serviceOption Retrieve, set, and list the different service options updateService Updates a Web Service {mrsdeploy}
- 28. • Seamless integration with authentication solution: LDAP/AD/AAD • Secure connection: HTTPS encrypted by TLS 1.2/SSL • Compliance with Microsoft Security Development Lifecycle R Client
- 29. ModelPrepare SQL 2017 OperationalizeOperationalize R & ScaleR Models CRAN R Models AzureML Web Services R Server VMs ModelPrepare Operationalize T-SQL/Stored Procedure Operationalize R Server On PremCloud Deploy to SQL Server 2017 Deploy to Hadoop / Linux Server / Windows Server 1 2 3 4 SQL 2017 {mrsdeploy}, {azureml}, {sqlutils}
- 30. Operationalizing R Models • On-premises and Cloud • Web Services (API) and SQL Procedures Demo
- 31. 종합 시나리오(예시) 및 결론
- 32. 이미지 분류 딥러닝 절차 (Learning/Scoring) Images Featurization (using pre-trained ResNet18 neural network model) Features Classification Algorithm (Boosted Tree) Classifier Model Learning Labels Images Features Scoring Predictions Featurization (using pre-trained ResNet18 neural network model) Classification resnet18, resnet50, resnet101, alexnet
- 33. SQL Server Edge Distributed Featurization CT Scan Images Azure Blob Storage Classifier Training Featurization Models Table HDInsight-MRS HDInsight에서의 Distributed Featurization + Training
- 34. Featurization Scoring with the classifier model Web App Diagnosis: 35% certainty Stored Procedures with R Code SQL Server에서 딥러닝 모델로 Scoring Stored Procedure call Model table, Features table, New Images table SQL Server
- 38. R Server for Hadoop 9.1 Data Frames Worker Task Worker Task Worker Task ScaleR Master Task Finalizer Initiator Remote Execution: ssh Web Services MRSDeplo y R Tools for Visual Studio BI Tools & Applications Jupyter Notebooks Thin Client IDEs https:// https:// Edge Node
- 39. Snapshot Functions createSnapshot Create a snapshot of the remote session (workspace and working directory) loadSnapshot Load a snapshot from the server into the remote session (workspace and working directory) listSnapshots Get a list of snapshots for the current user downloadSnapshot Download a snapshot from the server deleteSnapshot Delete a snapshot from the server Remote Objects Management listRemoteFiles Get a list of files in the working directory of the remote session deleteRemoteFile Delete a file from the working directory of the remote R session getRemoteFile Copy a file from the working directory of the remote R session putLocalFile Copy a file from the local machine to the working directory of the remote R session getRemoteObject Get an object from the remote R session putLocalObject Put an object from the local R session and load it into the remote R session getRemoteWorkspace Take all objects from the remote R session and load them into the local R session putLocalWorkspace Take all objects from the local R session and load them into the remote R session Remote Connection remoteLogin Remote login to the R Server with AD or admin credentials remoteLoginAAD Remote login to R Server server using Azure AD remoteLogout Logout of the remote session on the DeployR Server. Remote Execution remoteExecute Remote execution of either R code or an R script remoteScript Wrapper function for remote script execution diffLocalRemote Generate a 'diff' report between local and remote pause Pause remote connection and back to local resume Return the user to the 'REMOTE >' command prompt
- 42. Cloud AI Stack Services Processing Frameworks AI Applications Cognitive Services Infrastructure AML Web Services BOT Framework Model & Experimentation Management Data Wrangling & Spark AI Batch Training Storage (Azure Data Services) & Hardware (CPU, GPU, FPGS & ASIC) Inferencing Spark, SQL, Other Engines DSVM Machine Learning and Deep Learning Toolkits CNTK Tensorflow ML Server Scikit-Learn Other Libs. ACS Docker Tooling CPUs Edge
Editor's Notes
- To help us meet these goals, we have three main products Cortana Intelligence Suite Cognitive Services, Bot Framework, Cortana Power BI Machine learning, Stream Analytics, HDInsight Data Lake, SQL, DW Data Factory, Data Catalog, Event Hubs SQL Server 2017 SSRS, DataZen, R SSAS, SQL Server Machine Learning Services OLTP, DW, Hadoop, EDSs SSIS, DQS, MDS Microsoft R R visualizations Microsoft R Hadoop, Teradata, Linux, Windows Spark SQL/ETL
- To help us meet these goals, we have three main products Cortana Intelligence Suite Cognitive Services, Bot Framework, Cortana Power BI Machine learning, Stream Analytics, HDInsight Data Lake, SQL, DW Data Factory, Data Catalog, Event Hubs SQL Server 2017 SSRS, DataZen, R SSAS, SQL Server Machine Learning Services OLTP, DW, Hadoop, EDSs SSIS, DQS, MDS Microsoft R R visualizations Microsoft R Hadoop, Teradata, Linux, Windows Spark SQL/ETL
- CRAN - Growing library of over 10,000+ R packages built by a thriving open source community. Huge repository of freely exchanged, algorithms, techniques, scripts, adapters, techniques, training available. Enterprise Grade Analytics Platform – Solve for operationalization challenges using Microsoft R’s ability to write the code once and deploy on multiple platforms. Get enterprise grade support and safeguard your analytics investments. Works with what you have – We understand your data lives in different environments and your needs may change over time which is why Microsoft R supports several platforms like – Hadoop/Spark, Linux, Windows, Teradata, SQL Best of Open Source and Microsoft innovation Parallelized, remote executing algorithms In-database analytics to take analytics to your data Machine learning packages from Microsoft Time to value Solution templates, tutorials to help you build solutions Microsoft’s partner ecosystem to help you execute projects.
- There are many R products in market having Operationalization capabilities. These 4 pillars separate R Server from other R products.
- If a version is not specified, a temp guid endpoint is created – this is mainly for development phase and sharing among team members privately.
- I can model in any of those environments, and I can deploy in any of those environments. Interchangeably!
- On the left we have left we have R client which offers two things to data scientists They can leverage all the Microsoft R packages locally on their workstations Second, they can push the compute and bid data analytics to where the data lives. This gives them access to the power of servers and eliminates need for data movement, reducing time and increasing security On the right is our commitment of meeting customers where they are, and where their data lives.
- Slide Objective Show how R Server for Hadoop Spark can interoperate with 4 different methos of development and deployment. Talking Points
- Snapshot functions are very useful for remote execution scenarios. It can save the whole workspace and working directory so that you can pick up from exactly where you left last time. Thank about saving and loading a game. It can also be used when publish a web service to help you handle the environment dependencies. But it might impact the performance of the Request-Response time. For optimal performance, consider the size of the snapshot carefully. Before creating a snapshot, ensure that keep only those workspace objects you need and purge the rest. And, in the event that you only need a single object, consider passing that object alone itself instead of using a snapshot.