I am running some large regression models in R in a grid computing environment. As far as I know, the grid just gives me more memory and faster processors, so I think this question would also apply for those who are using R on a powerful computer.
The regression models I am running have lots of observations, and several factor variables that have many (10s or 100s) of levels each. As a result, the regression can get computationally intensive. I have noticed that when I line up 3 regressions in a script and submit it to the grid, it exits (crashes) due to memory constraints. However, if I run it as 3 different scripts, it runs fine.
I'm doing some clean up, so after each model runs, I save the model object to a separate file, rm(list=ls()) to clear all memory, then run gc() before the next model is run. Still, running all three in one script seems to crash, but breaking up the job seems to be fine.
The sys admin says that breaking it up is important, but I don't see why, if I'm cleaning up after each run. 3 in one script runs them in sequence anyways. Does anyone have an idea why running three individual scripts works, but running all the models in one script would cause R to have memory issues?
thanks! EXL
Similar questions that are worth reading through:
Forcing garbage collection to run in R with the gc() command
Memory Usage in R
My experience has been that R isn't superb at memory management. You can try putting each regression in a function in the hope that letting variables go out of scope works better than gc(), but I wouldn't hold your breath. Is there a particular reason you can't run each in its own batch? More information as Joris requested would help as well.
Related
I am working on a project that involves the analysis of several very large text files. I've divided the project up into pieces, each of which will be done in its own RMarkdown/R Notebook, but I'm running into real problems.
The first is that as I'm working my way through a portion (one R file), I periodically have to rm variables and recapture memory using gc(). When I'm ready to knit the file, I think R is going to re-run everything - which means I need to explicitly write in chunks with my rm/gc steps. Is this correct? I know you can put the option cache = TRUE in the chunk options, but I haven't done that before. If I do, are all of those results held in memory (i.e., in the cache)? If so, what happens when I remove variables and recapture memory? Is this the right way to save results for presentation without having to re-run everything?
Thanks!
Your problem is that your code is dumping everything into the global environment (your Rmd's environment). When I work with larger data I tend to wrap my analysis into a function inside of the chunk, instead of writing it as if it were an R script. I'll give a simple example to illustrate:
Imagine the following as a script:
r <- load_big_data()
train <- r[...]
test <- r[...]
fit <- lm(x ~ y, data = train)
summary(fit)
If this is your chunk, all of these variables are left in the environment when your model run is completed. However, if you encapsulate your work in a function, once the function is done the interim variables are typically released from memory.
r <- load_big_data()
myFun <- function(r) {
train <- r[...]
test <- r[...]
fit <- lm(x ~ y, data = train)
return(summary(fit))
}
Now, instead of having test, train, and fit in the workspace as the Rmd is knit, you only have r in your workspace (and myFun, which is practically costless)
Bonus: You'll find you can reuse these functions the longer your analysis gets!
Updates
RE: cache = TRUE
To answer your subsequent question. cache=TRUE will load from an RDS file instead of re-running the code chunk. It could be effective as a tool to minimize memory usage -- but you'll still need to remember to remove data from the workspace as it loads from the cache rather than running. You should think of this as saving time, rather than saving memory unless you manually clean up.
RE: gc()
gc, or "garbage collection" is a trigger for a process that R runs frequently by itself to collect and dump memory that it has held temporarily but is no longer using. Garbage collection in R is quite good, but using gc can help release memory in more stubborn situations. Hadley does a good job of summarizing here: http://adv-r.had.co.nz/memory.html. With that said, it's rarely ever the silver bullet and typically, if you feel like you need to use it you either need to rethink your approach or rethink your hardware, or both.
re: External resources
This may sound a bit flippant, but sometimes loading up another machine that's much larger than yours to finish the work is wildly less expensive (time == $) than fixing a memory leak. Example: an R5 with 16 cores and 128GB of RAM is $1 per hour. The calculus on your time is often quite lucrative.
I apologize in advance since this post will not have any reproducible example.
I am using R x64 3.4.2 to run some cross-validated analyses on quite big matrices (number of columns ~ 80000, number of rows between 40 and 180). The analyses involve several features selection steps (performed with in-house functions or with functions from the CORElearnpackage, which is written in C++), as well as some clustering of the features and the fitting of a SVM model (by means of the package RWeka, that is written in Java).
I am working on a DELL Precision T7910 machine, with 2 processors Intel Xeon E5-2695 v3 2.30 GHz, 192 Gb RAM and Windows 7 x64 operating system.
To speed up the running time of my analysis I thought to use the doParallel package in combination with foreach. I would set up the cluster as follow
cl <- makeCluster(number_of_cores, type='PSOCK')
registerDoParallel(cl)
with number_of_clusterset to various numbers between 2 and 10 (detectCore() tells me that I have 56 cores in total).
My problem is that even if only setting number_of_cluster to 2, I got a protection from stack overflowerror message. The thing is that I monitor the RAM usage while the script is running and not even 20 Gb of my 192 Gb RAM are being used.
If I run the script in a sequential way it takes its sweet time (~ 3 hours with 42 rows and ~ 80000 columns), but it does run until the end.
I have tried (almost) every trick in the book for good memory management in R:
I am loading and removing big variables as needed in order to reduce memory usage
I am breaking down the steps with functions rather than scripting them directly, to take advantage of scoping
I am calling gc()every time I delete a big object in order to prompt R to return memory to the operating system
But I am still unable to run the script in parallel.
Do someone have any suggestion about this ? Should I just give up and wait > 3 hours every time I run the analyses ? And more generally: how is it possible to have a stack overflow problem when having a lot of free RAM ?
UPDATE
I have now tried to "pseudo-parallelize" the work using the same machine: since I am running a 10-fold cross-validation scheme, I am opening 5 different instances of Rgui and running 2 folds in each instances. Proceeding in this way, everything run smoothly, and the process indeed take 10 times less than running it in a single instance of R. What makes me wonder is that if 10 instances of Rgui can run at the same time and get the job done, this means that the machine has the computational resources needed. Hence I can not really get my head around the fact that %dopar% with 10 clusters does not work.
The "protection stack overflow" means that you have run out of the "protection stack", that is too many pointers have been PROTECTed but not (yet) UNPROTECTed. This could be because of a bug or inefficiency in the code you are running (in native code of a package or in native code of R, but not a bug in R source code).
This problem has nothing to do with the amount of available memory on the heap, so calling gc() will have no impact, and it is not important how much physical memory the machine has. Please do not call gc() explicitly at all, even if there was a problem with the heap usage, it just makes the program run slower but does not help: if there is not enough heap space but it could be obtained by garbage collection, the garbage collector will run automatically. As the problem is the protection stack, neither restructuring the R code nor removing dead variables explicitly will help. In principle, structuring the code into (relatively small) functions is a good thing for maintainability/readability and it also indirectly reduces scope of variables, so removing variables explicitly should become unnecessary.
It might help to increase the pointer protection stack size, which can be done at R startup from the command line using --max-ppsize.
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?
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:
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.
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.
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.
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.
I was getting this error when I was inadvertently running the 32-bit version of R on my 64-bit machine.
My data contains 229907 rows and 200 columns. I am training randomforest on it. I know it will take time. But do not know how much. While running randomforest on this data, R becomes unresponsive. "R Console (64 Bit) (Not Responding)". I just want to know what does it mean? Is R still working or it has stopped working and I should close it and start again?
It's common for RGui to be unresponsive during a long calculation. If you wait long enough, it will usually come back.
The running time won't scale linearly with your data size. With the default parameters, more data means both more observations to process and more nodes per tree. Try building some small forests with ntree=1, different values of the maxnodes parameter and different amounts of data, to get a feel for how long it should take. Have the Windows task manager or similar open at the same time so that you can monitor CPU and RAM usage.
Another thing you can try is making some small forests (small values of ntree) and then using the combine function to make a big forest.
You should check your CPU usage and memory usage. If the CPU is still showing a high usage with the R process, R is probably still going strong.
Consider switching to R 32 bit. For some reason, it seems more stable for me - even when my system is perfectly capable of 64 bit support.
My problem is that I have a large model, which is slow to load to memory. To test it on many samples, I need to run some C program to generating input features for model, then run R script to predict. It takes too much time to load the model every time.
So I am wondering
1) if there is some method to keep the model ( a variable in R) in the memory.
or
2) Can I run a separative process of R as a dedicated server, then all the prediction processes of R can access the variable in the server on the same machine.
The model is never changed during for all the prediction. It is a randomForest model stored in a .rdata file, which has ~500MB. Loading this model is slow.
I know that I can use parallel R (snow, doPar, etc) to perform prediction in parallel, however, this is not what I want, since it require me to change the data flow I used.
Thanks a lot.
If you are regenerating the model every time, you can save the model as an RData file and then share it across the different machines. While it may still take time to load from disk to memory, it will save the time of regenerating.
save(myModel, file="path/to/file.Rda")
# then
load(file="path/to/file.Rda")
Edit per #VictorK's suggetsion:
As Victor points out, since you are saving only a single object, saveRDS may be a better choice.
saveRDS(myModel, file="path/to/file.Rds")
myModel <- readRDS(file="path/to/file.Rds")