On 04/14/2015 01:17 PM, Wolfgang Huber wrote:
Dear Sean
I understand the second point. As for .Call not being the right paradigm, then
maybe some other method invocation mechanism? In essence, my question is
whether someone already has figured out whether new virtualisation tools can
help avoid some of the tradtional Makeovers/configure pain.
The part of your question that challenged me was to 'run under a “normal”,
system-installed R', for which I don't have any meaningful help to offer.
Probably the following is not what you were looking for...
There was no explicit mention of this in Sean's answer, so I'll point to
http://bioconductor.org/help/docker/
A more typical use is that R is on the docker container, maybe starting the
docker container in such a way that you have access to your non-docker file system.
I might run the devel version of R / Bioc (the devel version was a bit stale
recently; I'm not sure if it is updated) with
docker run -ti bioconductor/devel_sequencing R
(the first time this will be painful, but the second time instantaneous). The
image comes with all the usual tools (e.g., compilers) and all of the packages
with a 'Sequencing' biocViews; most additional packages can be installed w/out
problem.
If there were complex dependencies, then one might start with one of the simpler
containers, add the necessary dependencies, save the image, and distribute it,
as outlined at
http://bioconductor.org/help/docker/#modifying-the-images
I bet that many of the common complexities are already on the image. A fun
alternative to running R is to run RStudio Server on the image, and connect to
it via your browser
docker run -p 8787:8787 bioconductor/devel_base
(point your browser to http://localhost:8787 and log in with username/password
rstudio/rstudio).
I guess this also suggests a way to interact with some complicated docker-based
package from within R on another computer, serving the package up as some kind
of a web service.
Martin
Wolfgang
On Apr 14, 2015, at 13:52 GMT+2, Sean Davis <seand...@gmail.com> wrote:
Hi, Wolfgang.
One way to think of docker is as a very efficient, self-contained virtual machine. The operative
term is "self-contained". The docker containers resemble real machines from the inside
and the outside. These machines can expose ports and can mount file systems, but something like
.Call would need to use a network protocol, basically. So, I think the direct answer to your
question is "no".
That said, there is no reason that a docker container containing all complex
system dependencies for the Bioc build system, for example, couldn't be created
with a minimal R installation. Such a system could then become the basis for
further installations, perhaps even package-specific ones (though those would
need to include all R package dependencies, also). R would need to run INSIDE
the container, though, to get the benefits of the installed complex
dependencies.
I imagine Dan or others might have other thoughts to contribute.
Sean
On Tue, Apr 14, 2015 at 7:23 AM, Wolfgang Huber <whu...@embl.de> wrote:
Is it possible to ship individual R packages (that e.g. contain complex, tricky
to compile C/C++ libraries or other system resources) as Docker containers (or
analogous) so that they would still run under a “normal”, system-installed R.
Or, is it possible to provide a Docker container that contains such complex
system dependencies such that a normal R package can access it e.g. via .Call ?
(This question exposes my significant ignorance on the topic, I’m still asking
it for the potential benefit of a potential answer.)
Wolfgang
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