Re: Emacs/ESS/org freezes/hangs on big data/ RAM(~256GB) processes when run in org/babel

[Rainer M Krug <Rainer@krugs.de>]

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Andreas Leha <andreas.leha@med.uni-goettingen.de> writes:

> Hi Rainer,

Hi Andreas,

>
> Rainer M Krug <Rainer@krugs.de> writes:
>> "Charles C. Berry" <ccberry@ucsd.edu> writes:
>>
>>> On Wed, 17 Jun 2015, William Denton wrote:
>>>
>>>> On 17 June 2015, Xebar Saram wrote:
>>>>
>>>>> I do alot of modeling work that involves using huge datasets and run
>>>>> process intensive R processes (such as complex mixed models, Gamms etc). in
>>>>> R studio all works well yet when i use the orgmode eval on R code blocks it
>>>>> works well for small simple process but 90% of the time when dealing with
>>>>> complex models and bug data (up to 256GB) it will just freeze emacs/ess.
>>>>> sometimes i can C-c or C-g it and other times i need to physically kill
>>>>> emacs.
>>>>
>>>> I've been having the same problem for a while, but wasn't able to
>>>> isolate it any more than large data sets, lack of memory, and heavy
>>>> CPU usage. Sometimes everything hangs and I need to power cycle the
>>>> computer. :(
>>>>
>>>
>>> And you (both) have `ess-eval-visibly' set to nil, right?
>>>
>>> I do statistical genomics, which can be compute intensive. Sometimes
>>> processes need to run for a while, and I get impatient having to wait.
>>>
>>> I wrote (and use) ox-ravel[1] to speed up my write-run-revise cycle in
>>> org-mode.
>>>
>>> Basically, ravel will export Org mode to a format that knitr (and the
>>> like) can run - turning src blocks into `code chunks'. That allows me
>>> to set the cache=TRUE chunk option, etc. I run knitr on the exported
>>> document to initialize objects for long running computations or to
>>> produce a finished report.
>>>
>>> When I start a session, I run knitr in the R session, then all the
>>> cached objects are loaded in and ready to use.
>>>
>>> If I write a src block I know will take a long time to export, I
>>> export from org mode to update the knitr document and re-knit it to
>>> refresh the cache.
>>
>> I have a similar workflow, only that I use a package like
>> approach, i.e. I tangle function definitions in a folder ./R, data into
>> ./data (which makes it possible to share org defined variables with R
>> running outside org) and scripts, i.e. the things which do a analysis,
>> import data, ... i.e. which might take long, into a folder ./scripts/. I
>> then add the usual R package infrastructure files (DESCRIPTION,
>> NAMESPACE, ...).
>> Then I have one file tangled into ./scripts/init.R:
>>
>> #+begin_src R :tangle ./scripts/init.R  
>> library(devtools)
>> load_all()
>> #+end_src
>>
>>
>> and one for the analysis:
>>
>> #+begin_src R :tangle ./scripts/myAnalysis.R  
>> ## Do some really time intensive and horribly complicated and important
>> ## stuff here
>> save(
>>     fileNames,
>>     bw,
>>     cols,
>>     labels,
>>     fit,
>>     dens,
>>     gof,
>>     gofPerProf,
>>     file = "./cache/results.myAnalysis.rds"
>> )
>> #+end_src
>>
>>
>> Now after tangling, I have my code easily available in a new R session:
>>
>> 1) start R in the directory in which the DESCRIPTION file is, 
>> 2) run source("./scripts/init.R")
>>
>> and I have all my functions and data available.
>>
>> To run a analysis, I do
>>
>> 3) source("./scripts/myAnalysis.R")
>>
>> and the results are saved in a file fn
>>
>> To analyse the data further, I can then simply use
>>
>> #+begin_src R :tangle ./scripts/myAnalysis.R
>> fitSing <- attach("./cache/results.myAnalysis.rds")
>> #+end_src
>>
>>
>> so they won't interfere with my environment in R.
>>
>> I can finally remove the attached environment by doing
>>
>> #+begin_src R :tangle ./scripts/myAnalysis.R  
>> detach(
>>     name = attr(fitSing, "name"),
>>     character.only = TRUE
>> )
>> #+end_src
>>
>> Through these caching and compartmentalizing, I can easily do some
>> things outside org and some inside, and easily combine all the data.
>>
>> Further advantage: I can actually create the package and send it to
>> somebody for testing and review and it should run out of the box, as in
>> the DESCRIPTION file all dependencies are defined.
>>
>> I am using this approach at the moment for a paper and which will also
>> result in a paper. By executing all the scripts, one will be able to do
>> import the raw data, do the analysis and create all graphs used in the
>> paper.
>>
>> Hope this gives you another idea how one can handle long running
>> analysis in R in org,
>>
>> Cheers,
>>
>> Rainer
>>
>
> That is a cool workflow.  I especially like the fact that you end up
> with an R package.

Thanks. Yes - the idea of having a package at the end was one main
reason why I am using this approach.


>
> So, I'll try my again.   Is there there any chance to see working
> example of this?  I'd love to see that.

Let's say I am working on it. I am working on a project which is using
this workflow and when it is finished, the package will be available as
an electronic appendix to the paper.

But I will see if I can condense an example and blog it - I'll let you
kow when it is done.

Cheers,

Rainer



>
> Thanks,
> Andreas
>
>

-- 
Rainer M. Krug, PhD (Conservation Ecology, SUN), MSc (Conservation Biology, UCT), Dipl. Phys. (Germany)

Centre of Excellence for Invasion Biology
Stellenbosch University
South Africa

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