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https://github.com/MarkEdmondson1234/cloudRunR

Running R on Cloud Run
https://github.com/MarkEdmondson1234/cloudRunR

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Running R on Cloud Run

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# cloudRunR

Running R on Cloud Run (https://cloud.run).  Cloud Run lets you run any code within a Docker container, and will scale to billions of hits and down to 0, so you only pay for what you need. 



A new R package that automates deploying this demo app is now available here, called [cloudRunner](https://github.com/MarkEdmondson1234/cloudRunner) - https://github.com/MarkEdmondson1234/cloudRunner



A demo app is available in the `plumber` folder of this repo.  It copies the "Hello World" examples from plumber ( https://www.rplumber.io/). 



You can deploy it on your own GCP project via this button:



[![Run on Google Cloud](https://storage.googleapis.com/cloudrun/button.svg)](https://console.cloud.google.com/cloudshell/editor?shellonly=true&cloudshell_image=gcr.io/cloudrun/button&cloudshell_git_repo=https://github.com/MarkEdmondson1234/cloudRunR.git&cloudshell_working_dir=plumber)



The end result should be similar to these Cloud Run URLs:



* https://cloudrunr-ewjogewawq-uc.a.run.app/hello

* https://cloudrunr-ewjogewawq-uc.a.run.app/echo?msg=my%20message

* https://cloudrunr-ewjogewawq-uc.a.run.app/plot

* https://cloudrunr-ewjogewawq-uc.a.run.app/plot?spec=setosa



Adapt the R script in `plumber/api.R` for your own uses.



## Quickstart: Build and Deploy for R



The procedure below follows the examples for other languages given [here](https://cloud.google.com/run/docs/quickstarts/build-and-deploy) but modifies it to run R workloads. In this example an R API created by the wonderful `library(plumber)`



### PORT



You need to run the plumber script on a port defined by the system environment variable, `PORT`, reachable in R via `Sys.getenv('PORT')`  The key line in the Dockerfile that achieves this for plumber is this one:



```

ENTRYPOINT ["R", "-e", "pr <- plumber::plumb(commandArgs()[4]); pr$run(host='0.0.0.0', port=as.numeric(Sys.getenv('PORT')))"]

```



Note the `pr$run` must have a numeric value passed to it for the port.



### Concurrency



R by default is single-threaded, so only 1 hit per container will be immediately served, other hits will queue until the previous hit has completed.  For multi-threading, use `library(future)` to serve up to 80 threads (Python uses gunicorn).  A demo of combining future and plumber is [here](https://github.com/FvD/futureplumber/blob/master/multiprocess/future.R)



The effect of concurrency on scalability of the Cloud Run app is [here](https://cloud.google.com/run/docs/about-concurrency)



### Cloud Build



Set up [Google Cloud Build Trigger](https://console.cloud.google.com/cloud-build/triggers) to turn the Dockerfile into a Docker image to call.



1. Connect GitHub to Cloud Build

2. Configure a trigger to point to the Dockerfile

3. Push change to GitHub



### Cloud Run deploy



Once the Cloud Build has finished it will give you a Docker URI such as `gcr.io/mark-edmondson-gde/cloudrunr:939c04dfe80a1eefed28f9dd59aae5dff5dc1e1e`.  



1. Go to https://console.cloud.google.com/run/

2. Create a new service, name it something cool

3. Put the Docker URI into the Cloud Run field. 

4. Select public endpoint, and limit concurrency to what your app is configured to handle per instance (I chose 8)



And thats it.  A deployed R API. 



### Use the API



You will then get a URL for the API you can use.  For this demo app the endpoints are `/hello`, `/echo?msg="my message"` and `/plot` (or filter the plot via `/plot?spec=setosa`)