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 like (ana)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 installations come 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. You use these package in python the same way you would non-optimized versions, i.e., import PACKAGE.

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.

Additional packages in these module should be installed in virtual environments.

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 or the python 3 version of intel python with

   module load intelpython2
   module load intelpython3

Packages in this module can be installed in so-called conda environments (see below), 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.

There are many optional and conflicting packages for Python that users could potentially want (see e.g. http://pypi.python.org/pypi). Therefore, users need to install these additional packages locally in their home directories. In fact, there is no choice, as users do not have permissions to install packages system-wide.

Python provides a number of ways to install packages, the most common of which are the pip and conda commands. By default, these commands would install in the same directory as the one in which the python executable lives, but python provides a number of ways for users to install libraries in their home directories instead.

One way to do this with pip using the --user option, but you shouldn't. That approach is now mostly superseded by virtual environments, and we do not recommend using the --user option as it can interfere with other Python environments.

Virtual environments are a standard in Python to create isolated Python environments. This is useful when certain modules or certain versions of modules are not available in the default python environment.

Virtual environments can be used either with the regular python modules or the intelpython/anaconda modules.

Note that the use of conda is highly discouraged on Niagara.

Using Virtualenv in Regular Python

Creation

In the terminal, first load a python module, e.g.

   module load NiaEnv/2019b python/3.11.5

or (on e.g. Teach)

   module load TeachEnv/2022a python/3.11.5

Then create a directory for the virtual environments. One can put a virtual environment anywhere, but this directory structure is recommended:

   mkdir ~/.virtualenvs

Now we create our first virtualenv called myenv choose any name you like:

   virtualenv --system-site-packages ~/.virtualenvs/myenv

The "--system-site-packages" flag will use the system-installed versions of packages rather than installing them anew (the list of these packages can be found on the Python wiki page). This will result in fewer files created in your virtual environment. After that you can activate that virtual environment:

   source ~/.virtualenvs/myenv/bin/activate 

As you are in the virtualenv now, you can just type pip install <required module> to install any module into your virtual environment.

To go back to the normal python installation simply type

   deactivate

Command line and job usage

You need to activate the appropriate environment every time you log in, and at the start of all your jobs scripts. However, the installation of packages only needs to be done once. In the NiaEnv/2019b stack, it is *not* necessary to load the python module before activating the environment, while in the NiaEnv/2018a stack, you need to load the python module before activating the environment.

Usage of your virtual environment by others

Sharing a virtual environment with another user is easy. As long as the directory containing the virtual environment is readable by that other user (which on Niagara is the default when that user is in the same group as the directory), then they simply have to source the activate file in the bin directory of that environment, e.g.

   source /home/g/group/user/.virtualenvs/myenv/bin/activate

Usage in the Jupyter Hub

You can use your virtual environment in Niagara's Jupyter_Hub, but there are two additional steps required to get the JupterHub to know about your environment and to make it as one of its possible "kernels" for new notebooks.

After having activated your environment, execute the following command

   venv2jup

which is nearly equivalent to the following two commands

   pip install ipykernel
   python -m ipykernel install --name NAME --user
   

The first installs the packages needed to interface with jupyter as a kernel, the latter puts an entry in the .share/jupyter directory, in which the jupyterhub looks for possible kernels. The advantage of the venv2jup command is that in addition to these two commands, it also corrects some paths in case modules are loaded and checks if all is setup properly. This procedure works for NiaEnv/2020a and NiaEnv/2019b, but may fail for NiaEnv/2018a.

For conda environments that were installed in .conda/envs, the jupyter notebook should pick them up automatically.

Using Virtual Environments in Intelpython/Anaconda

Caveat: Although using conda is possible on Niagara, it is strongly recommended not to do so, as it causes several difficulties.

Creation

One can use the same kind of virtual environments for the intelpython and conda modules as for regular modules. However, environments are built-in in Anaconda, see [1]. These "conda environments" are not the same as regular virtual environments, as they can contain general packages, such as compilers. The latter feature means that conda environments are much more flexible, but also that they do not cooperate well with other software modules on Niagara, and will created 10-100 thousands files that can easily cause issues with your file quota on $HOME. Therefore, you should always use regular virtual environments and pip on Niagara and not conda, unless you have a good reason not too.

First, you just need to load a conda-like module, e.g.

   module load NiaEnv/2019b intelpython3

Then, you create a virtual environment

   conda create -n myPythonEnv python=3.6

(conda puts the environment in the directory $HOME/.conda/envs/myPythonEnv)

Next, you activate your conda environment:

   source activate myPythonEnv

At this point you are in your own environment and can just do the installation of any package that you need, e.g.

   pip install myFAVpackage

or

   conda install myFAVpackage

To go back to the normal python installation, type

   source deactivate

Command line and job usage

You need to load the intelpython/anaconda module and activate the appropriate environment every time you log in, and at the start of all your jobs scripts. However, the installation of packages only needs to be done once.

Usage in the Jupyter Hub

You can use conda environment in Niagara's Jupyter_Hub. If they were installed in .conda/envs, the jupyter notebook should pick them up automatically.

Cleaning up conda

Once the installation of a package finishes, please clean the cache:

conda clean -y --all
rm -rf $HOME/.conda/pkgs/*

If you do no need a conda environment anymore, make sure to remove it:

conda remove --name myPythonEnv --all

To verify that the environment was removed, run:

conda info --envs

Installing the Scientific Python Suite

For many scientific codes the packages numpy, scipy, matplotlib, pandas and ipython are used. Versions of these are already in the python modules (except for the regular python modules in the NiaEnv/2018a stack).

However, if you need different versions, you could start your virtual environment without --system-site-packages. In that case, for regular python modules, please install versions of package with an intel- prefix, if they exists, so that you will get the most optimized version of the package.

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.

Using mpi4py

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.