I am new to cloud computing and GCP. I am trying to create a notebook instance running R with a GPU. I got a basic instance with 1 core and 0 GPUs to start and I was able to execute some code which was cool. When I try to create an instance with a GPU I keep getting all sorts of errors about something called live migration, or that there are no resources available, etc. Can someone tell me how to start an R notebook instance with a GPU? It can't be this difficult.
The CRAN (The Comprehensive R Archive Network) doesn't support GPU. However, you can follow this link might help you to install a Notebook instance running R with a GPU. You need a machine with Nvidia GPU drivers installed then install R and Jupyter Lab. After that compile those R packages which require it for use with GPU's.
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I've created a program that runs in R that I plan on distributing among a lot of other people. Currently the R script is ran completely automatically and behind the scenes with one .sh script which is exactly how it is intended to be. I'm trying to make it so theres no need for client intervention. The R script itself loads the packages and installs them if they aren't present which takes away the task of them installing the packages themselves.
Is there a way I can provide a folder within my Application's folder that they already download that contains R-script and its dependencies so the code can use that location of Rscript to compile and run the R-program I have created. The goal is to be able to download it and run without the need of internet connection to download R and maybe even the programs required packages if possible.
Any help or ideas is appreciated.
I assume that process you want called "creating binary package". Binary is programs (like EXE files) which can run directly on target CPU without any interpreter software (like Python interpreter for python scripts, or Java VM for java applications). I'm not so familiar with packaging of R programs but I found some materials regarding this issue:
1 - Building binary package R
2 - https://seandavi.github.io/post/build-linux-r-binary-packages/
3 - https://support.rstudio.com/hc/en-us/articles/200486508-Building-Testing-and-Distributing-Packages
Second link assumes Linux as target system. Opposite to interpreted languages, binary files often OS dependent (Linux, Windows, or Mac). I, personally, don't know how compatible are packages between Linux systems with different library sets.
Please comment if you find some information misleading, I'll correct the answer.
I have an R Notebook that I am building to provide an analysis for somebody, and I am wondering if I should choose another option as I don't know if she will be able to run the Notebook without having R installed.
Is it possible to run an R Notebook as a single entity or must you have R installed in order to do it?
To rerun the notebook they require R. But the whole point of R Notebooks is that they produce a static document as output. That document (usually in HTML format) can be shared in isolation, and does not require any additional software besides a web browser to be viewerd.
Notebook will need R to run. To distribute a notebook without the R dependency will be a bit more elaborate, like installing rstudio server within a docker container. User will, in this particular case, need to have Docker installed and know how to start a container. From there on the user can interact with the code through a web browser.
Another option would be to use the cloud solution that some companies offer. It offers sharing functionality and you don't have to worry about the infrastructure or distribution of your work. There are some free plans that may work for you, but the real power is in premium features.
I am new in R and I am working with a datasets that has more than 5 millions of observations. So I thought that it would be a good idea to use RStudio on a virtual machine instead of using it on my local machine.
I am reading the documentation about virtual machines and RServer but it is still not clear to me if I have to use Microsoft R Server to create a VIM and then just install Rstudio as I would do in my local machine or if I can create a generic VIM and then install RStudio. Which is the correct way? Why?
If both of these options are possible, which one is the best?
Please help me. Sorry for my confusion.
You can do either. If you are using Azure (which I think you are given that you mention Microsoft R Server), there is also the Data Science VM, which will come preinstalled with RStudio and many other useful programs.
R Server is more for production workloads with R, so unless you are planning that you could probably stick with the Data Science VM. If you end up choosing this option, you can connect directly to an RStudio instance on the R Server from the Azure portal.
I have an R script which I want to deploy so that it's idiot-proof, one click runs it etc. Unfortunately I don't have the means to pay for a server, and the environment in which it needs to run does not allow the installation of new software, only portable style apps can be run. (School computers) My script also relies on several non-base packages.
Is there any way to deploy R and my script in an easy to run way so it can be used off a usb stick?
You can install R on a USB drive and use it on any computer running the same OS. If you're using Windows, see question 2.6 of the R for Windows FAQ.
If you made the USB stick a bootable disk environment (say linux) with R installed on it, you could boot off it and do it that way.
I am trying to connect R to a Hadoop cluster using R. The cluster has HDFS, Map Reduce, Hive, Pig and Sqoop installed on it.
R will be running on in the Windows environment. I know that rhdfs, rhadoop and rmr exist for Linuix, but I can't find anything on Windows.
Does anyone know of a library to use?
Thank you
Revolution Analytrics is trying to make a name for themselves in this space. They have a couple of nice packages (some of which are open-source and/or free for non-commercial use) which allow you to interact with Hadoop from R in a Windows environment fluidly.