maybe starting the docker container in such a way that you have access
to your non-docker file system.
One way to achieve that is to mount a directory from your host system
inside the container:
# Creating a subdirectory in /home/rstudio and making it read/write
for all (permissions in dockers FS can be a bit tricky)
docker run --name="rstudio-local-data"
bioconductor/release_sequencing bash -c 'mkdir /home/rstudio/data &&
chmod o+rw /home/rstudio/data'
# Committing the changes to create a new image from the now
modified bioconductor/release_sequencing
docker commit rstudio-local-data rstudio-local-data
# Mounting my current working directory inside the docker and
starting rstudio-server
docker run -p 8787:8787 -v $(pwd):/home/rstudio/data
rstudio-local-data supervisord
From there on you can open a browser and navigate to
http://localhost:8787 as Martin said.
/Bastian
Martin Morgan <mailto:mtmor...@fredhutch.org>
15 Apr 2015 02:19
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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Wolfgang Huber <mailto:whu...@embl.de>
14 Apr 2015 22:17
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.
Wolfgang
_______________________________________________
Bioc-devel@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/bioc-devel
Sean Davis <mailto:seand...@gmail.com>
14 Apr 2015 13:52
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
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Wolfgang Huber <mailto:whu...@embl.de>
14 Apr 2015 13:23
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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Bioc-devel@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/bioc-devel
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https://stat.ethz.ch/mailman/listinfo/bioc-devel
