R code failed with: "Error: cannot allocate buffer"

Question

Compiling an RMarkdown script overnight failed with the message:

Error: cannot allocate buffer
Execution halted

The code chunk that it died on was while training a caretEnsemble list of 10 machine learning algorithms. I know it takes a fair bit of RAM and computing time, but I did previously succeed to run that same code in the console. Why did it fail in RMarkdown? I'm fairly sure that even if it ran out of free RAM, there was enough swap.

I'm running Ubuntu with 3GB RAM and 4GB swap.

I found a blog article about memory limits in R, but it only applies to Windows: http://www.r-bloggers.com/memory-limit-management-in-r/

Any ideas on solving/avoiding this problem?

Answer 1

One reason why it may be backing up is that knitr and Rmarkdown just add a layer of computing complexity to things and they take some memory. The console is the most streamline implementation.

Also Caret is fat, slow and unapologetic about it. If the machine learning algorithm is complex, the data set is large and you have limited RAM it can become problematic.

Some things you can do to reduce the burden:

1. If there are unused variables in the set, use a subset of the ones you want and then clear the old set from memory using rm() with your variable name for the data frame in the parentheses.

2. After removing variables, run garbage collect, it reclaims the memory space your removed variables and interim sets are taking up in memory.

R has no native means of memory purging, so if a function is not written with a garbage collect and you do not do it, all your past executed refuse is persisting in memory making life hard.

To do this just type gc() with nothing in the parentheses. Also clear out the memory with gc() between the 10 ML runs. And if you import data with XLConnect the java implementation is nasty inefficient...that alone could tap your memory, gc() after using it every time.

3. After setting up training, testing and validation sets, save the testing and validation files in csv format on the hard drive and REMOVE THEM from your memory and run,you guessed it gc(). Load them again when you need them after the first model.

4. Once you have decided which of the algorithms to run, try installing their original packages separately instead of running Caret, require() each by name as you get to it and clean up after each one with detach(package:packagenamehere) gc().

There are two reasons for this.

One, Caret is a collection of other ML algorithms, and it is inherently slower than ALL of them in their native environment. An example: I was running a data set through random forest in Caret after 30 minutes I was less than 20% done. It had crashed twice already at about the one hour mark. I loaded the original independent package and in about 4 minutes had a completed analysis.

Two, if you require, detach and garbage collect, you have less in resident memory to worry about bogging you down. Otherwise you have ALL of carets functions in memory at once...that is wasteful.

There are some general things that you can do to make it go better that you might not initially think of but could be useful. Depending on your code they may or may not work or work to varying degrees, but try them and see where it gets you.

I. Use the lexical scoping to your advantage. Run the whole script in a clean Rstudio environment and make sure that all of the pieces and parts are living in your work space. Then garbage collect the remnants. Then go to knitr & rMarkdown and call pieces and parts from your existing work space. It is available to you in Markdown under the same rStudio shell so as long as nothing was created inside a loop and without saving it to to global environment.

II. In markdown set your code chunks up so that you cache the stuff that would need to be calculated multiple times so that it lives somewhere ready to be called upon instead of taxing memory multiple times.

If you call a variable from a data frame, do something as simple as multiply against it to each observation in one column and save it back into that original same frame, you could end up with as many as 3 copies in memory. If the file is large that is a killer. So make a clean copy, garbage collect and cache the pure frame.

Caching intuitively seems like it would waste memory, and done wrong it will, but if you rm() the unnecessary from the environment and gc() regularly, you will probably benefit from tactical caching

III. If things are still getting bogged down, you can try to save results in csv files send them to the hard drive and call them back up as needed to move them out of memory if you do not need all of the data at one time.

I am pretty certain that you can set the program up to load and unload libraries, data and results as needed. But honestly the best thing you can do, based on my own biased experience, is move away from Caret on big multi- algorithm processes.

Answer 2

I was getting this error when I was inadvertently running the 32-bit version of R on my 64-bit machine.