My colleagues and I use PyOpenCL as part of an cell-based modelling program
(Tim Rudge's CellModeller, here <http://haselofflab.github.io/CellModeller/>
We would like to use CellModeller alongside a PDE Solver (FEniCS
<https://fenicsproject.org/>), whose NumPy requirements (>=1.13) conflict
with those of PyOpenCL (1.9). Is it possible to make PyOpenCL compatible
with later NumPy versions?
Here's the conflict in my conda environment:
$ conda install numpy=1.13
Solving environment: failed
UnsatisfiableError: The following specifications were found to be in
- pyopencl -> numpy=1.9.2
Use "conda info <package>" to see the dependencies for each package.
It looks like even the latest version of PyOpenCL (2018.2.2) still only
uses NumPy 1.9.3. What scope is there for overcoming this compatibility
Many thanks and best wishes,
Dr. William P J Smith
Postdoctoral researcher, Foster lab, University of Oxford
Hey hey :)
I am fairly new with pyopencl. Still getting the grip of it...
Although the docs suggest installing pyopencl with conda I have been using
pip, which seems to work quite well, apart from an issue with dependencies.
pip install pyopencl on a freshly created virtual environment will complain
about missing numpy and warn about missing pybind11 and mako.
Regarding numpy, and if I understand correctly, some effort has been put (
fix the issue with the class "ExtensionUsingNumpy" as opposed to
I gave it a try with the latest version in the github repo, and `pip
install .`, but it still complains about missing numpy.
Should I install dependencies manually beforehand? Shouldn't this be
handled in setup.py with setup_requires= and install_requires=?
From a user perspective it makes a lot of sense to just run `pip install
pyopencl` and have all dependencies installed, similar to what happens when
we install scipy or matplotlib.
Thanks in advance,