Python

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.

Python on Niagara

We currently have two families of Python installed on Niagara.

• Regular Python
• Intel Python (a variant of anaconda)

Here we describe the differences between these packages.

Regular Python

Python versions 2.7 and 3.6 have been installed from source and are optimized for Niagara. We call these 'regular' python versions because they are not dependent on other distribution mechanisms such as conda. Such distributions do not play well with the rest of the software stack, so the 'regular' python modules should be your first choice.

In the Niagara Software Stack version 2019b, i.e., NiaEnv/2019b, the specific versions are 2.7.15 and 3.6.8, so you can load python 2 or python 3 using

   module load python/2.7.15
   module load python/3.6.8

Both these installation comes with the following optimized python packages preinstalled:

   virtualenv
   intel-numpy
   intel-scipy
   intel-scikit-learn
   ipp
   daal
   jinja2
   cython 
   matplotlib
   ipython
   numba
   numexpr
   pandas
   line_profiler
   memory_profiler
   funcsigs
   pycosat
   pyeditline
   pyOpenSSL
   PySocks
   PyYAML
   requests 
   xgboost

In this list, a intel-PACKAGE package provides an Intel-optimized version of PACKAGE, often using Intel's high performance Math Kernel Library.

In the previous NiaEnv/2018a stack, the regular python versions did not have these packages, and users needed to install them in their own home directory. This was wasteful in terms of storage and has occasional led to quota issues, so we highly recommend using the NiaEnv/2019b packages.

You can use these modules, in concert with virtualenv and pip, to build your own virtual environment.

Intel Python

The Intel Python modules are based on the Anaconda package, a python distribution that aims to simplify package management. 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. These modifications have also been incorporated in the intel-PACKAGES included in the regular python modules discussed above, but with Intel Python, you also get the conda command. You can load the python 2 version of the intel python with

   module load intelpython2

and python 3 with

   module load intelpython3

Packages in this module can be installed in so-called conda environments, although virtualenv also works.

A word of caution: conda environment are very wasteful when it comes to the number of files that they store in your home directory, and their is a good chance you will hit your quote of 250,000 files with only a few conda environments. And conda being a package manager on its own means that it does not always work well in combination with the rest of the software stack.

Miniconda and Anaconda

If your are looking for anaconda or miniconda, you should find that intelpython is a good substitute. In the NiaEnv/2019b stack, we no longer provide anaconda modules, but we do have aliases conda2 and conda3 for intelpython2 and intelpython3.

We advice against installing your own anaconda or miniconda in your home directory. Instead, start from one of the intelpython modules and use conda environments, or, even better, start from a regular python module and create a virtualenv in which you can install your own packages. Installing your own anaconda or miniconda would cause many more files to be installed in your $HOME directory, and this might cause trouble with the quota on the number of files.

Installing your own Python Modules

If you need to install your own Python modules, either in regular python or with conda, you should set up a virtual or conda environment. Visit the Installing your own Python Modules page for instructions on how to set this up.

We would urge you do remove any conda or virtual environments that you are not using, to help reduce the number of files on the $HOME file system.

Running serial Python jobs

As with all serial jobs, if your Python computation does not use multiple cores, you should bundle them up so the 40 cores of a node are all performing work. Examples of this can be found on this page.

Using a Jupyter Notebook

You may develop your Python scripts in a Jupyter Notebook on Niagara. A node has been set aside as a Jupyter Hub. See this page for details on how to access that node, and develop your code.

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.

SciNet's Python Classes

There is a dizzying amount of documentation available for programming in Python on the Python.org webpage. That begin said, each fall, SciNet runs two 4-week classes on using Python for research:

• SCMP142: Introduction to Programming with Python. This class is intended for those with little-to-no programming experience who wish to learn how to program.
• SCMP112: Introduction to Scientific Computing with Python. This class focusses on using Python to perform research computing.

An excellent set of material for teaching scientists to program in Python is also available at the Software Carpentry homepage.