I think your balanced and reasoned approach addresses all my current
concerns. Nice! I will likely adopt your methods. Let me ruminate.
Thanks for this.
.From: Paul Gilbert [mailto:pgilbert902 at gmail.com]
.Sent: Tuesday, March 05, 2013 4:34 PM
.To: Cook, Malcolm
.Cc: 'r-devel at r-project.org'; 'bioconductor at r-project.org';
'r-discussion at listserv.stowers.org'
.Subject: Re: [Rd] [BioC] enabling reproducible research & R package
management & install.package.version & BiocLite
.(More on the original question further below.)
.On 13-03-05 09:48 AM, Cook, Malcolm wrote:
.> What got me started on this line of inquiry was my attempt at
.> balancing the advantages of performing a periodic (daily or
.> update to the 'release' version of locally installed
.> packages on our institute-wide installation of R with the
.> disadvantages of potentially changing the result of an analyst's
.> workflow in mid-project.
.I have implemented a strategy to try to address this as follows:
.1/ Install a new version of R when it is released, and packages in
.version's site-library with package versions as available at the
.the R version is installed. Only upgrade these package versions in
.case they are severely broken.
.2/ Install the same packages in site-library-fresh and upgrade these
.package versions on a regular basis (e.g. daily).
.3/ When a new version of R is released, freeze but do not remove the
.R version, at least not for a fairly long time, and freeze
.site-library-fresh for the old version. Begin with the new version
.1/ and 2/. The old version remains available, so "reverting" is
.The analysts are then responsible for choosing the R version they
.and the library they use. This means they do not have to change R
.package version mid-project, but they can if they wish. I think the
.above two libraries will cover most cases, but it is possible that a
.projects will need their own special library with a combination of
.package versions. In this case the user could create their own
.or you might prefer some more official mechanism.
.The idea of the above strategy is to provide the stability one might
.want for an ongoing project, and the possibility of an upgraded
.if necessary, but not encourage analysts to remain indefinitely with
.versions (by say, putting new packages in an old R version library).
.This strategy has been implemented in a set of make files in the
.RoboAdmin available at http://automater.r-forge.r-project.org/
.be done entirely automatically with a cron job. Constructive
.are always appreciated.
.(IT departments sometimes think that there should be only one
.everything available, which they test and approve. So the initial
.reaction to this approach could be negative. I think they have not
.really thought about the advantages. They usually cannot
.upgrade without user input, and timing is often extremely complicate
.because of ongoing user needs. This strategy is simply shifting
.responsibility and timing to the users, or user departments, that
.actually do the testing and approving.)
.Regarding NFS mounts, it is relatively robust. There can be
.problems, especially for users that have a habit of keeping an R
.open for days at a time and using site-library-fresh packages. In my
.experience this did not happen often enough to worry about a
.Regarding the original question, I would like to think it could be
.possible to keep enough information to reproduce the exact
.but I think for potentially sensitive numerical problems that is
.optimistic. As others have pointed out, results can depend not only
.and package versions, configuration, OS versions, and library and
.compiler versions, but also on the underlying hardware. You might
.some hope using something like an Amazon core instance. (BTW, this
.problem is not specific to R.)
.It is true that restricting to a fixed computing environment at your
.institution may ease things somewhat, but if you occasionally
.hardware or the OS then you will probably lose reproducibility.
.An alternative that I recommend is that you produce a set of tests
.confirm the results of any important project. These can be
.put in the tests/ directory of an R package, which is then
.local, not on CRAN, and built/tested whenever a new R and packages
.installed. (Tools for this are also available at the above indicated
.site.) This approach means that you continue to reproduce the old
.results, or if not, discover differences/problems in the old or new
.version of R and/or packages that may be important to you. I have
.successfully using a variant of this since about 1993, using R and
.package tests/ since they became available.
.> I just got the "green light" to institute such periodic updates
.> I have been arguing is in our collective best interest. In
.> I promised my best effort to provide a means for preserving or
.> reverting to a working R library configuration.
.> Please note that the reproducibility I am most eager to provide is
.> limited to reproducibility within the computing environment of our
.> institute, which perhaps takes away some of the dragon's nests,
.> though certainly not all.
.> There are technical issues of updating package installations on an
.> NFS mount that might have files/libraries open on it from running
.> sessions. I am interested in learning of approaches for
.> minimizing/eliminating exposure to these issue as well. The
.> first/best approach seems to be to institute a 'black out' period
.> when users should expect the installed library to change.
.> there are improvements to this????
.> .-----Original Message----- .From: Mike Marchywka
.> [mailto:marchywka at hotmail.com] .Sent: Tuesday, March 05, 2013
.> AM .To: amackey at virginia.edu; Cook, Malcolm .Cc:
.> r-devel at r-project.org; bioconductor at r-project.org;
.> r-discussion at listserv.stowers.org .Subject: RE: [Rd] [BioC]
.> reproducible research & R package management &
.> install.package.version & BiocLite . . .I hate to ask what go this
.> thread started but it sounds like someone was counting on .exact
.> numeric reproducibility or was there a bug in a specific release?
.> actual .fact, the best way to determine reproducibility is run the
.> code in a variety of .packages. Alternatively, you can do
.> in java and not assume .that calculations commute or associate as
.> code is modified but it seems .pointless. Sensitivity
.> would seem to lead to more reprodicible results .than trying to
.> a specific set of code quirks. . .I also seem to recall that FPU
.> have random lower order bits in some cases, .same code/data give
.> different results. Alsways assume FP is stochastic and plan .on
.> anlayzing the "noise." . .
.> .> From: amackey at virginia.edu .> Date: Mon, 4 Mar 2013 16:28:48
.> -0500 .> To: MEC at stowers.org .> CC: r-devel at r-project.org;
.> bioconductor at r-project.org; r-discussion at
.> Subject: Re: [Rd] [BioC] enabling reproducible research & R
.> management & install.package.version & BiocLite .> .> On Mon, Mar
.> 2013 at 4:13 PM, Cook, Malcolm <mec at="" stowers.org=""> wrote: .> .> >
.> where do the dragons lurk .> > .> .> webs of interconnected
.> dynamically loaded libraries, identical versions of .> R compiled
.> with different BLAS/LAPACK options, etc. Go with the VM if you .>
.> really, truly, want this level of exact reproducibility. .> .> An
.> alternative (and arguably more useful) strategy would be to cache
.> results of each computational step, and report when results differ
.> upon .> re-execution with identical inputs; if you cache
.> along with .> each result, you can identify which package(s)
.> and begin to hunt .> down why the change occurred (possibly for
.> better); couple this with .> the concept of keeping both code
.> results in version control, then you .> can move forward with a
.> (re)analysis without being crippled by out-of-date .> software. .>
.> -Aaron .> .> -- .> Aaron J. Mackey, PhD .> Assistant Professor .>
.> Center for Public Health Genomics .> University of Virginia .>
.> amackey at virginia.edu .> http://www.cphg.virginia.edu/mackey
.> [[alternative HTML version deleted]] .> .>
.> ______________________________________________ .>
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