I'm looking for an approach to run a simulation algorithm developed in R in AWS. The input for the R model will come from S3 and the output will need to be written back to S3. The data scientist group at my organization are R experts and my organization has identified as AWS as the enterprise cloud platform. Given this, I need to find a way to run the R simulation model in AWS.
I saw this blog (https://aws.amazon.com/blogs/compute/analyzing-genomics-data-at-scale-using-r-aws-lambda-and-amazon-api-gateway/) which talks about using Lambda to run R code within Ptyhon using rpy2 package. I plan to follow the same approach. I was planning to implement the Lambda function as below-
1) Read input files from S3 to write to local lambda storage (/tmp). This will done using Python boto3 SDK.
2) Invoke R algorithm using rpy2. I plan to save the R algorithm as a .RDS file in S3 and load it using rpy2 and then run the R algorithm. R algorithm would write the output back to local lambda storage.
3) Write output from lambda storage to S3. Again this will be done using Python boto3 SDK.
As you can see, Python is used to interact with S3 and bring files to local lambda storage. R will read from local lambda storage and run the simulation algorithm. All R code will be wrapped within rpy2 in the lambda function. I was planning out this way because I was not sure if R can work with S3 directly.
I now realize that Lambda local storage is limited to 500 MB and I doubt if the input & output files will remain within this limit. I'm now trying to see if R can work directly with S3 within Lambda. If this is possible, I don't have to bring the files to local lambda storage and hence will not run out of space. Again, the R - S3 interaction will need to be wrapped inside rpy2. Is there a way to achieve this? Can the R Cloudyr library work in this scenario? Although I see examples of Cloudyr interacting with S3, but I don't see any example of this usage within Lambda using rpy2.
Any thoughts please?
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I have couple of parquet files in HDFS that I'd like to read into R and some data in R I'd like to write into HDFS and store in parquet file format. I'd like to use arrow library, because I believe it's the R equivalent of pyarrow and pyarrow is awesome.
The problem is, nowhere in the R arrow docs can I find information about working with HDFS and also in general not much information about how to use the library properly.
I am basically looking for the R equivalent of:
from pyarrow import fs
filesystem = fs.HadoopFileSystem(host = 'my_host', port = 0, kerb_ticket = 'my_ticket')
Disclosure:
I know how to use odbc to read and write my data. While reading is fine (but slow), inserting larger amounts of data into impala/hive this way is pure awful (slow, often fails, and impala isn't really built to digest data this way).
I know I could probably use pyarrow to work with hdfs, but would like to avoid installing python in my docker image just for this purpose.
The bindings for this are not currently implemented in R; there is a ticket open here on the project JIRA, which at time of writing is still marked "Unresolved": https://issues.apache.org/jira/browse/ARROW-6981. I'll comment on the JIRA ticket to mention that there is user interest in implementing these bindings.
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
I am trying to reduce my development headaches for creating a ML Webservice on Azure ML Studio. One of the things that stuck me was can we just upload .rda files in the workbench and load it via an RScript (like in the figure below).
But can't connect directly to the R Script block. There's another way to do it (works to upload packages that aren't available in Azure's R directories) -- using zip. But there isn't really any resource out there that I found to access the .rda file in .zip.
I have 2 options here, make the .zip work or any other work around where I can directly use my .rda model. If someone could guide me about how to go forward it would appreciate it.
Note: Currently, I'm creating models via the "Create RModel" block, training them and saving it, so that I can use it to make a predictive web service. But for models like Random Forest, not sure how the randomness might create models (local versions and Azure versions are different, the setting of seed also isn't very helpful). A bit tight on schedule, Azure ML seems boxed for creating iterations and automating the ML workflow (or maybe I'm doing it wrong).
Here is an example of uploading a .rda file for scoring:
https://gallery.cortanaintelligence.com/Experiment/Womens-Health-Risk-Assessment-using-the-XGBoost-classification-algorithm-1
I have an R-script that does stuff with a bunch of tweets and I would like to use the same script on the same data but saved in an Hadoop file system. According to this Hortonworks tutorial I could use R code with data from my HDFS, but it is not quite clear.
Can I use the very same R-script, taking advantage of the mapreduce paradigm, by using this Revolution R? Should I change my code or is there a way to execute the same functions optimized for an Hadoop architecture?
My wish would be to write my code on a standard R IDE like R-Studio and then use it, or use the most of it, on my cloud services (such as Microsoft Azure) with mapreduce on the base.
Yes, you can run any R script across different data platform from Hadoop to Spark to Teradata and SQL Server by using environment specific compute context.
Following two links should help you get started on how to use Revolution R / Microsoft R Server on Hadoop:
https://msdn.microsoft.com/en-us/microsoft-r/scaler-hadoop-getting-started
https://github.com/Azure/Azure-MachineLearning-DataScience/blob/master/Misc/MicrosoftR/Samples/NYCTaxi/NYC2013_MRS_LinearBinary.Rmd
As part of a transition from MATLAB to R, I am trying to figure out how to read TDMS files created with National Instruments LabVIEW using R. TDMS is a fairly complex binary file format (http://www.ni.com/white-paper/5696/en/).
Add-ons exist for excel and open-office (http://www.ni.com/white-paper/3727/en/), and I could make something in LabVIEW to make the conversion, but I am looking for a solution that would let me read the TDMS files directly into R. This would allow us to test out the use of R for certain data processing requirements without changing what we do earlier in the data acquisition process. Having a simple process would also reduce the barriers to others trying out R for this purpose.
Does anyone have any experience with reading TDMS files directly into R, that they could share?
This is far from supporting all TDMS specifications but I started a port of a python npTDMS package into R here https://github.com/msuefishlab/tdmsreader and it has been tested out in the context of a shiny app here
You don't say if you need to automate the reading of these files using R, or just convert the data manually. I'm assuming you or your colleagues don't have any access to LabVIEW yourselves otherwise you could just create a LabVIEW tool to do the conversion (and build it as a standalone application or DLL, if you have the professional development system or app builder - you could run the built app from your R code by passing parameters on a command line).
The document on your first link refers to (a) add-ins for OpenOffice Calc and for Excel, which should work for a manual conversion and which you might be able to automate using those programs' respective macro languages, and (b) a C DLL for reading TDMS - would it be possible for you to use one of those?