On 2015-01-26 14:50, Maarten wrote:
I tried these commands to install FEniCS on a CentOS 6.6 system. Here
is the result:
> which python
<INSTALL_PATH>/anaconda/envs/fenics27/bin/python
However:
>>> import dolfin
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File
"<INSTALL_PATH>/anaconda/envs/fenics27/lib/python2.7/site-packages/dolfin/__init__.py",
line 16, in <module>
import cpp
File
"<INSTALL_PATH>/anaconda/envs/fenics27/lib/python2.7/site-packages/dolfin/cpp/__init__.py",
line 42, in <module>
exec("import %s" % module_name)
File "<string>", line 1, in <module>
File
"<INSTALL_PATH>/anaconda/envs/fenics27/lib/python2.7/site-packages/dolfin/cpp/common.py",
line 32, in <module>
_common = swig_import_helper()
File
"<INSTALL_PATH>/anaconda/envs/fenics27/lib/python2.7/site-packages/dolfin/cpp/common.py",
line 28, in swig_import_helper
_mod = imp.load_module('_common', fp, pathname, description)
ImportError: libboost_filesystem.so.1.55.0: cannot open shared object
file: No such file or directory
So no succes yet.
Regards,
Maarten
Hello Maarten,
I see what happened: conda installed boost from the default channel,
which is a different version of what I used to compile DOLFIN (perhaps
it should look for libboost_*.so instead of attaching the version number?)
The workaround is forcing conda to install boost from my channel once again:
$ (fenics27) $ conda install "boost=1.55" --channel
https://conda.binstar.org/juanlu001/channel/fenics:1.4.0:centos
This will downgrade boost and give you a working FEniCS :) Hopefully!
I just updated the FEniCS recipe accordingly so the installation
procedure should work from the first step now.
Regards,
Juan Luis
On 15 January 2015 at 23:14, Juan Luis Cano <[email protected]
<mailto:[email protected]>> wrote:
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] <mailto:[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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