I was doing a simulation study in RStudio using doAzureParallel package. It uses a Virtual Machines to do the work and merges the result finally in my RStudio workspace. However, suddenly my RStudio Stopped working. But when I log in to Microsoft Azure Portal I can still see that the jobs are running and it shows the number of simulations running, completed, etc. Once I restart my Rstudio is there any possibility to get the R output from the Azure portal? I am new to Azure. I hope I would be helped by some experienced people.
Note: You can only monitor the jobs using Azure Portal.
Once the Job Submission object is passed through the Submit call in the SDK and the workload is submitted to the service that has been created using the portal. Now the job can be monitored via the portal or via the client by getting the details of the job using the Job Id that was returned at submission.
This article covers the following Azure services that support the R language:
The doAzureParallel package is a parallel backend for the widely popular foreach package. With doAzureParallel, each iteration of the foreach loop runs in parallel on an Azure Virtual Machine (VM), allowing users to scale up their R jobs to tens or hundreds of machines.
This tutorial shows you how to deploy a Batch pool and run a parallel R job in Azure Batch directly within RStudio.
Related
I am looking for all the possible options to deploy an application written using R and the Shiny package on AWS. I see one of the options is to use EC2, but I would like to know if there is any serverless option available to deploy R applications on AWS.
Does Sagemaker RStudio have an option to deploy? I looked into AWS documents, but unfortunately I could not see an option.
Any leads would be much appreciated.
Using RStudio on SageMaker (or simply R kernels in SageMaker Studio / Notebooks) enables several deployment strategies for RESTful application endpoints.
In this example, you can see a trained R model deployed to a SageMaker serverless endpoint:
https://github.com/aws-samples/rstudio-on-sagemaker-workshop/blob/main/03_SageMakerPipelinesAndDeploy/01_Pipeline.Rmd#L124-L149
In this example, you will deploy a trained R model to a 24/7 RESTful endpoint using SageMaker real-time endpoints:
https://github.com/aws/amazon-sagemaker-examples/tree/main/r_examples/r_serving_with_plumber
In order to build the serving containers for those models, you can check the above examples or you may want to use recently launched functionality using the native R vetiver package:
https://github.com/juliasilge/vetiverdemo/blob/main/biv-svm/train_sagemaker.Rmd
If you're looking to deploy shiny applications and not models, you can use EC2 or a container service like ECS/Fargate to host the Shiney or RStudio connect server. These blogs may be a bit overkill for what you're looking to do, but they should have good examples of achieving hosted applications:
https://aws.amazon.com/blogs/machine-learning/host-rstudio-connect-and-package-manager-for-ml-development-in-rstudio-on-amazon-sagemaker/
https://aws.amazon.com/blogs/architecture/scaling-rstudio-shiny-using-serverless-architecture-and-aws-fargate/
I have an R script that I need to run once every week. I need it to be done using Azure. One option is to use Azure Data Factory and set up a pipline what will run this R script on a VM (Windows).
The problem I'm facing is that I will have to update every now and then both the R script and the R packages the R script is using.
When setting up this pipeline I will have to generalize the VM (correct me if I'm wrong) and doing so I can no longer log into this VM. And if I can't log into this VM I cannot update the R packages.
What options do I have here?
There is an alternate solution where you can use Azure Batch Service and Azure Data Factory to execute R scripts in your pipeline.
For more information, you can refer this blog: How to execute R Scripts using Azure Batch Services and Azure Data Factory? | by Aditya Kaushal | Medium
Alternatively, to run R scripts in your VM, you can use below options:
Custom Script Extension
Run Command
Hybrid Runbook Worker
Serial console
Reference: Run scripts in an Azure Windows VM - Azure Virtual Machines | Microsoft Docs
I am new to Azure batch. I am trying to use R in parallel with Azure batch in rstudio to run code on a cluster. I am able to successfully start the cluster and get the example code to work properly. When I try to run my own code I am getting an error that says the cluster nodes cannot find my data files. Do I have to change my working directory to Azure batch somehow?
Any information on how to do this is much appreciated.
I have figured out how to get Azure batch to see my data files. Not sure if this is the most efficient way, but here is what I did.
Download a program called Microsoft Azure Storage Explorer which runs on my local computer.
Connect to my Azure storage using the storage name and primary storage key found in the Azure portal.
in Microsoft Azure Storage Explorer find Blob containers, right click create new container.
Upload data files to that new container.
Right click on data files and go to copy URL.
Paste URL in R like this model_Data<-read.csv(paste('https://<STORAGE NAME HERE>.blob.core.windows.net/$root/k',k,'%20data%20file.csv',sep=''),header=T)
What is the equivalent of Google Cloud Datalab(jupyter notebook service) managed service provided in Amazon Cloud, for usage by Data Analysts/Data Scientists? Is the only option is to build Databricks/Jupyter notebook separately on EC2 cluster in AWS, is there not a managed service in AWS available for this?
Checkout Amazon SageMaker, a fully-managed service that enables data scientists and developers to build, train and deploy machine learning models.
As part of SageMaker documentation, there is a section on running Jupyter notebook apps with SageMaker notebook instance. You may also want to read How it works and Get Started sections to understand the complete workflow.
I have an R script that I run every day that scrapes data from a couple of different websites, and then writes the data scraped to a couple of different CSV files. Each day, at a specific time (that changes daily) I open RStudio, open the file, and run the script. I check that it runs correctly each time, and then I save the output to a CSV file. It is often a pain to have to do this everyday (takes ~10-15 minutes a day). I would love it if someway I could have this script run automatically at a pre-defined specific time, and a buddy of mine said AWS is capable of doing this?
Is this true? If so, what is the specific feature / aspect of AWS that is able to do this, this way I can look more into it?
Thanks!
Two options come to mind thinking about this:
Host a EC2 Instance with R on it and configure a CRON-Job to execute your R-Script regularly.
One easy way to get started: Use this AMI.
To execute the script R offers a CLI rscript. See e.g. here on how to set this up
Go Serverless: AWS Lambda is a hosted microservice. Currently R is not natively supported but on the official AWS Blog here they offer a step by step guid on how to run R. Basically you execute R from Python using the rpy2-Package.
Once you have this setup schedule the function via CloudWatch Events (~hosted cron-job). Here you can find a step by step guide on how to do that.
One more thing: You say that your function outputs CSV files: To save them properly you will need to put them to a file-storage like AWS-S3. You can do this i R via the aws.s3-package. Another option would be to use the AWS SDK for python which is preinstalled in the lambda-function. You could e.g. write a csv file to the /tmp/-dir and after the R script is done move the file to S3 via boto3's S3 upload_file function.
IMHO the first option is easier to setup but the second-one is more robust.
It's a bit counterintuitive but you'd use Cloudwatch with an event rule to run periodically. It can run a Lambda or send a message to an SNS topic or SQS queue. The challenge you'll have is that a Lambda doesn't support R so you'd either have to have a Lambda kick off something else or have something waiting on the SNS topic or SQS queue to run the script for you. It isn't a perfect solution as there are, potentially, quite a few moving parts.
#stdunbar is right about using CloudWatch Events to trigger a lambda function. You can set a frequency of the trigger or use a Cron. But as he mentioned, Lambda does not natively support R.
This may help you to use R with Lambda: R Statistics ready to run in AWS Lambda and x86_64 Linux VMs
If you are running windows, one of the easier solution is to write a .BAT script to run your R-script and then use Window's task scheduler to run as desired.
To call your R-script from your batch file use the following syntax:
C:\Program Files\R\R-3.2.4\bin\Rscript.exe" C:\rscripts\hello.R
Just verify the path to the "RScript" application and your R code is correct.
Dockerize your script (write a Dockerfile, build an image)
Push the image to AWS ECR
Create an AWS ECS cluster and AWS ECS task definition within the cluster that will run the image from AWS ECR every time it's spun-up
Use EventBridge to create a time-based trigger that will run the AWS ECS task definition
I recently gave a seminar walking through this at the Why R? 2022 conference.
You can check out the video here: https://www.youtube.com/watch?v=dgkm0QkWXag
And the GitHub repo here: https://github.com/mrismailt/why-r-2022-serverless-r-in-the-cloud