I finally polished the recipes, adding test running and checksums for
both 1.4.0 and 1.5.0. I also uploaded compiled versions of FEniCS to
Binstar.
Here is the source of the recipes (check the maint-1.4.0 branch too)
with non-very-rigorous instructions on installing and building:
https://github.com/juanlu001/fenics-recipes
To install FEniCS in CentOS 6 these commands should work:
$ bash
$ conda create -n fenics27 python=2.7
$ source activate fenics27
(fenics27) $ conda install "fenics=1.4.0" mkl --channel
https://conda.binstar.org/juanlu001/channel/fenics:1.4.0:centos
I still find that installing the packages in a different distribution
that the one used for building them has its problems (e.g. some
hardcoded paths in instant and ffc, at least in 1.4.0, that require
manual fixing) but still these recipes work wonderfully as a build
system. I have compiled all the libraries like thirty times in the past
two weeks but in the end I reached my goal, which was using FEniCS in my
native system. Hope they are useful :)
Best regards,
Juan Luis
On 2015-01-11 21:38, Juan Luis Cano wrote:
Hello all,
I just wanted to say that I finally got VTK plotting to work.
Fortunately there was a VTK conda package, so I switched my build
system to a Linux Mint with a proper graphical server and it worked. I
tested the package both in Mint and Arch Linux and I can claim success :)
Let me repeat the commands:
$ conda create --name py27 python=2.7
$ source activate py27
(py27)$ conda install fenics --channel juanlu001
I will repeat the process with the first 1.5 release with updated
requirements, and by then I will probably put all the conda recipes in
an independent Bitbucket repo. Again, any feedback is welcome.
Best regards,
Juan Luis
On 2015-01-05 22:32, Garth N. Wells wrote:
I think is is great.
I haven’t tested yet, but a suggestion to make the process simpler is
to let PETSc build suitesparse, etc. PETSc is a C library but can be
installed with pip (it has a Python-based build system). It can take
care of a number of dependencies (solvers, graph partitioners, etc).
I’ve copied Andy Terrel at Conitnuum Analytics who might have
something to chip in with.
Garth
On 5 Jan 2015, at 13:07, Juan Luis Cano <[email protected]> wrote:
Hello all,
My name is Juan Luis Cano, I'm studying a MSc in Aerospace
Engineering in Madrid and I started recently to play with FEniCS for
my final degree project. For my day to day work I am using a
virtualized Linux Mint and everything works like a charm thanks to
the Ubuntu PPA, but as it is not the distribution which I normally
use I tried to build a conda package these holidays.
I noticed there are a couple of build systems out there (dorsal,
hashdist) but, as the Anaconda distribution[1] is getting popular in
the scientific Python world these days, I really wanted to try to
provide FEniCS packages for it (at least in Linux). For those who
don't know it, Anaconda's package manager, conda, is open source[2]
and provides a nice build system[3].
You can try out my progress so far with a Linux 64 bit box and a
Python 2.7 environment:
$ conda create --name py27 python=2.7
$ source activate py27
(py27)$ conda install fenics --channel juanlu001
The build process itself was painful because I knew very little
about FEniCS dependencies a week ago but right now I managed to run
the `demo_poisson.py` (_without_ plotting, see below). The results
seem OK from Paraview.
The good thing is that I made the builds in an Ubuntu Server box but
it works the same in an Arch Linux machine too. I didn't try to
compile it against PETSc, Trilinos and such yet because I wanted
some feedback from the community first, and know if this is
something useful for anybody!
The trick here was avoiding the Ubuntu packages (via apt-get) and
compile the dependencies in the form of conda packages themselves. I
did such with boost and suitesparse, for instance[4]. This way there
are no linking problems across different Linux distros. I am stuck
with VTK though because it seems to look for libGL.so, which in turn
pulls from X11... and everythings gets messy very quickly[5].
So if I can get some feedback about how does this work in others'
computers, if this is any useful and which packages should I try to
build next that would be great. Anybody can reproduce the build
process using my conda-recipes fork.
Kind regards and happy new year!
Juan Luis
[1] https://store.continuum.io/cshop/anaconda
[2] https://github.com/conda/
[3] http://conda.pydata.org/docs/build.html
[4] https://binstar.org/juanlu001/
[5]
https://github.com/Juanlu001/conda-recipes/commit/a18cedc56e330ba09961b8ddaeb86f580e22f3cc
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