Python is programing language that continues to grow in popularity for scientific computing. It is very fast to write code in, but the software that results is much much slower than C or Fortran; one should be wary of doing too much compute-intensive work in Python.
There is a dizzying amount of documentation available for programming in Python on the Python.org webpage; SciNet has given a mini-course of 8 lectures on Research Computing with Python in the Fall of 2013. An excellent set of material for teaching scientists to program in Python is also available at the Software Carpentry homepage.
Python on Niagara
We currently have three families of Python installed.
- Intel Python
- regular Python
Here we describe the differences between these packages.
Anaconda is a pre-assembled set of commonly-used self-consistent Python packages. The source for this collection is here. There are two types of Anaconda Python available:
- The whole Anaconda software stack (the anacondaX/A.B.C modules).
- Anaconda's Python, with all the Python packages, but without the rest of the Anaconda stack (gcc, bzip2, HDF5/NetCDF tools, etc) (the python/A.B.C-anacondaX.Y.Z modules).
As of 9 July 2018 the following Anaconda modules are available:
$ module avail anaconda ----------------- /scinet/niagara/software/2018a/modules/base ------------------ anaconda2/5.1.0 python/2.7.14-anaconda5.1.0 r/3.4.3-anaconda5.1.0 anaconda3/5.1.0 python/3.6.4-anaconda5.1.0
Note that none of these modules require a compiler to be loaded. Also, note the presence of the R module. Anaconda now also comes with R; this package is the R analogy to the Anaconda Python modules.
You load the module in the usual way:
$ module load anaconda3/5.1.0 $ python >>>
The Intel Python modules are based on the Anaconda package. Intel has modified the package, and optimized the libraries to use the MKL libraries, which should make them faster than the Anaconda modules for some calculations.
As of 9 July 2018 the following Intel Python modules are available:
$ module avail intelpython ----------------- /scinet/niagara/software/2018a/modules/base ------------------ intelpython2/2018.2 intelpython3/2018.2
The base Python program has also been installed from source. This installation comes with no Python packages installed other than virtualenv and pip. You can use this module, in concert with virtualenv and pip, to build your own virtual environment.
$ module avail python ----------------- /scinet/niagara/software/2018a/modules/base ------------------ intelpython2/2018.2 python/2.7.14 intelpython3/2018.2 python/3.6.4-anaconda5.1.0 python/2.7.14-anaconda5.1.0 python/3.6.5 (D)
$ module load python/3.6.5 $ python >>>
Installing your own Python Modules
If you need to install your own Python modules, either in Anaconda or in regular Python, you should set up a virtual environment (or a 'conda' environment if you're using Anaconda). Visit the PythonVirtualEnv page for instructions on how to set this up.
Producing Matplotlib Figures on Niagara Compute Nodes and in Job Scripts
The conventional way of producing figures from python using matplotlib i.e.,
import matplotlib.pyplot as plt plt.plot(.....) plt.savefig(...)
will not work on the Niagara compute nodes. The reason is that pyplot will try to open the figure in a window on the screen, but the compute nodes do not have screens or window managers. There is an easy workaround, however, that sets up a different 'backend' to matplotlib, one that does not try to open a window, as follows:
import matplotlib as mpl mpl.use('Agg') import matplotlib.pyplot as plt plt.plot(.....) plt.savefig(...)
It is essential that the mpl.use('Agg') command precedes the importing of pyplot.