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| [http://www.python.org/ 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 is documented on the Alliance wiki: |
| __FORCETOC__
| | https://docs.alliancecan.ca/wiki/Python |
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| = Python on Niagara =
| | That page covers the current modules and job examples for Trillium. For |
| | | SciNet-specific access, see [[Trillium Quickstart]]. |
| We currently have two families of Python installed on [[Niagara_Quickstart|Niagara]].
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| * Regular Python
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| * Intel Python (a variant of anaconda)
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| Here we describe the differences between these packages.
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| Note that it is highly recommended that you use the NiaEnv/2019b stack by loading the corresponding module, ie.:
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| module load NiaEnv/2019b
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| If you do not, you are using the 2018a stack whose python setup is less optimal.
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| == Regular Python ==
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| Python versions 2.7 and 3.6, 3.7, and 3.8 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.
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| In the [[Niagara_Quickstart#Software_stacks:_NiaEnv_and_CCEnv | Niagara Software Stack]] version 2019b, i.e., NiaEnv/2019b, the specific versions are 2.7.15 and 3.8.5, so you can load python 2 or python 3 using
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| module load python/2.7.15
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| module load python/3.8.5
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| Both these installations come with the following optimized python packages preinstalled:
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| virtualenv
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| numpy
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| scipy
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| scikit-learn
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| ipp
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| daal
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| cython
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| matplotlib
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| ipython
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| numba
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| numexpr
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| pandas
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| line_profiler
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| memory_profiler
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| funcsigs
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| pycosat
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| pyeditline
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| pyOpenSSL
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| PySocks
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| PyYAML
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| requests
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| xgboost
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| In addition, the python/3.8.5 module also has dask installed.
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| 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, which is the default since September 1, 2020.
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| Additional packages in these module should be installed in [[#Using_Virtualenv_in_Regular_Python | virtual environments]].
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| == Intel Python ==
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| 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-<tt>PACKAGES</tt> 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
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| module load intelpython2
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| module load intelpython3
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| Packages in this module can be installed in so-called conda environments (see below), although virtualenv also works.
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| 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 there 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.
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| == Miniconda and Anaconda ==
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| 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.
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| 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.
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| = Installing your own Python Modules =
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| 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.
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| 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.
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| {{:Installing your own Python Modules}}
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| = Running serial Python jobs =
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| 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 [[Running_Serial_Jobs_on_Niagara|this]] page.
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| = Using a Jupyter Notebook =
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| == Jupyter Hub ==
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| You may develop your Python scripts in a Jupyter Notebook on Niagara. A node has been set aside as a Jupyter Hub. See [[Jupyter_Hub | the Jupyter Hub page]] for details on how to access that node, and develop your code.
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| The Jupyter Hub is a shared resource, much like the login nodes. You should not use it for extensive computations. For that you'll need to run Jupyter on a compute node.
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| == Running Jupyter on a Niagara Compute Node ==
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| If you need more memory or more cores for your notebook calculation, you should request a node through the scheduler and run Jupyter on it yourself.
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| 1. To be able to run Jupyter on a compute node, you must first install it inside a virtual environment and create a little helper script called <tt>notebook.sh</tt> that will be used to start the jupyter server in step 2. These are the command that you should use for the installation (which you should do only once, on a login node):
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| <pre>
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| $ module load NiaEnv/2019b python/3.8.5
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| $ virtualenv --system-site-packages $HOME/.virtualenvs/jupyter
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| $ source $HOME/.virtualenvs/jupyter/bin/activate
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| $ pip install jupyter jupyterlab
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| $ deactivate
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| $ cat > $HOME/.virtualenvs/jupyter/bin/notebook.sh <<EOF
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| source $HOME/.virtualenvs/jupyter/bin/activate
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| XDG_DATA_HOME=\$SCRATCH/.share
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| XDG_CACHE_HOME=\$SCRATCH/.cache
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| XDG_CONFIG_HOME=\$SCRATCH/.config
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| XDG_RUNTIME_DIR=\$SCRATCH/.runtime
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| JUPYTER_CONFIG_DIR=\$SCRATCH/.config/.jupyter
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| jupyter \${1:-notebook} --ip \$(hostname -f) --no-browser --notebook-dir=\$PWD
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| EOF
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| $ chmod +x $HOME/.virtualenvs/jupyter/bin/notebook.sh
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| </pre>
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| You can choose another directory than <tt>$HOME/.virtualenvs/jupyter</tt> for where to create the virtual environment, but you need to be consistent and use the same directory everywhere.
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| 2. To run the jupyter server on a compute node, start an interactive debug session with the <tt>debugjob command</tt> and then launch the server:
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| <pre>
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| $ salloc --time=1:00:00 -N 1 -n 40 # get one dedicated nodes with 40 cores.
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| $ cd $SCRATCH # $HOME is read-only, so move to $SCRATCH
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| $ $HOME/.virtualenvs/jupyter/bin/notebook.sh # add "lab" to start with the jupyter lab
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| </pre>
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| Make sure you note down (a) the name is of the compute node that you get allocates (the salloc command will let you know, they start with "<tt>nia</tt>" followed by a 4 digit number), and (b) the last URL that the notebook.sh tells you to use to connect.
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| 3. To connect to this jupyter server running on a compute node, which is not accessible from the internet, in a different terminal on you own computer, you must reconnect to niagara with a port-forwarding
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| tunnel to the compute node on which jupyter is running:
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| <pre>
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| $ ssh -L8888:niaXXXX:8888 niagara.scinet.utoronto.ca -N
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| </pre>
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| where <tt>niaXXXX</tt> is the name of the compute node ((a) above). This command should just "hang" there, it only serves to forward port number 8888 to port 8888 on the compute node.
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| Finally, point your browser to the URL that the <tt>notebook.sh</tt> command returned (the one with 127.0.01 in it).
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| Then connect to the 127.0.0.1 link that the notebook.sh command printed out.
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| = Producing Matplotlib Figures on Niagara Compute Nodes and in Job Scripts =
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| The conventional way of producing figures from python using matplotlib i.e.,
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| import matplotlib.pyplot as plt
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| plt.plot(.....)
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| plt.savefig(...)
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| 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:
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| import matplotlib as mpl
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| mpl.use('Agg')
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| import matplotlib.pyplot as plt
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| plt.plot(.....)
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| plt.savefig(...)
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| It is essential that the <tt>mpl.use('Agg')</tt> command precedes the importing of pyplot.
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| = Using mpi4py =
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| Several of the Python installations contain mpi4py preinstalled. However, using mpi4py requires loading an MPI module. There are several combinations of compiler/MPI/python modules which can be used.
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| == Using Regular Python ==
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| The Python in the regular <tt>python</tt> module (compiled from source) does not come with mpi4py. You will need to install mpi4py in your own storage space, preferably in a virtual environment.
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| $ module load NiaEnv/2019b gcc/8.3.0 intelmpi/2019u5 python/3.6.8
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| $ virtualenv --system-site-packages ~/.virtualenvs/mpi4pyenv
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| $ source ~/.virtualenvs/mpi4pyenv/bin/activate
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| (mpi4pyenv)$ pip install mpi4py
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| == Using intelpython ==
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| Using the either the NiaEnv/2019b or NiaEnv/2018a stack (the most-recent software stack is always recommended), the intelpython modules all have mpi4py, and should all work if an MPI module is also loaded. An example of this, using the NiaEnv/2019b stack, might be
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| $ module load NiaEnv/2019b
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| $ module load intel/2019u4 intelmpi/2019u4
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| $ module load intelpython3/2019u4
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| Other combinations of compilers (intel/gcc) or MPI module (intelmpi/openmpi) will also work with intelpython.
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| == Using Anaconda ==
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| Under the NiaEnv/2018a stack anaconda is available as a module. This module does not come with mpi4py, but can be installed using the usual steps:
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| $ module load gcc/7.3.0 openmpi/3.1.1
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| $ module load anaconda3/2018.12
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| $
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| $ conda create -n myenv
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| $
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| $ source activate myenv
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| (myenv) $
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| (myenv) $ conda install mpi4py
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| (myenv) $
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| == Error messages ==
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| When using openmpi with mpi4py, you may get an error of this type:
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| pml_ucx.c:285 Error: UCP worker does not support MPI_THREAD_MULTIPLE
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| Add the following lines to your Python script, BEFORE you import the mpi4py package:
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| import mpi4py.rc
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| mpi4py.rc.threads = False
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| = SciNet's Python Classes =
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| There is a dizzying amount of documentation available for programming in Python on the [http://python.org/ Python.org webpage]. That begin said, each fall, SciNet runs two 4-week classes on using Python for research:
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| * [https://support.scinet.utoronto.ca/education/browse.php?category=-1&search=scmp142&include=all&filter=Filter 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.
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| * [https://support.scinet.utoronto.ca/education/browse.php?category=-1&search=scmp112&include=all&filter=Filter SCMP112]: Introduction to Scientific Computing with Python. This class focusses on using Python to perform research computing.
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| An excellent set of material for teaching scientists to program in Python is also available at the [https://v4.software-carpentry.org/python/index.html Software Carpentry homepage].
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