Python for general users¶
Cray Python 3 distribution¶
Python 3 on ARCHER2 is provided by Cray.
The central installation provides many of the most common packages used for scientific computation and data analysis. These include:
- numpy and scipy - built against Cray LibSci
- mpi4py - built against Cray MPT
The Python 3 module can be loaded (either on the front-end or in a submission script) using:
module load cray-python
Adding your own packages¶
If the packages you require are not included in the central Python distribution, further packages can easily be built on top using pip. This can be done using:
pip install --user <package_name>
This uses the “--user” flag to ensure the packages are installed in your user directory. This means that you can have your own Python environment that is independent of the central installation.
This section on the
conda tool is adapted from the NERSC Python documentation at https://docs.nersc.gov/programming/high-level-environments/python/.
Creating conda environments¶
conda tool allows you to build your own custom Python installation
First, create a conda environment using
conda create. Specify a name for
your environment using the “-n” flag, and the version of the Python interpreter
you want installed. For example, the following will create a Python 3.6
environment called “myenv”:
conda create -n myenv python=3.6
You will then be asked to confirm the package managements steps that will be taken, along with the installation location.
Activating conda environments¶
There are two options for activating a conda environments:
source activate is the only option for versions of conda prior to 4.6.
You can activate an environment with the name “myenv” with the command:
source activate myenv
and then deactivate it with:
conda activate is available in conda 4.6 and later. It can be more complex
to use than
source activate, but has some advantages.
conda activate for the first time, first run the command
conda init. This sets the current Python environment as the default. It does
this by adding lines to your
.bashrc file that will be run at login. (These
lines are enclosed by the lines
# >>> conda initialize >>> and
# <<< conda initialize <<<, and can be removed or edited manually if
Having set your default environment, your custom environments can be activated using:
conda activate myenv
and deactivated using:
conda can also be used to find and install packages into your environments. For example, the following can be used to find and then install a package (in this case, scipy):
conda search scipy conda install scipy
Example Python submission script¶
#!/bin/bash --login #SBATCH --name=python_test #SBATCH --nodes=1 #SBATCH --tasks-per-node=1 #SBATCH --cpus-per-task=1 #SBATCH --time=00:10:00 # Replace [budget code] below with your project code (e.g. t01) #SBATCH --account=[budget code] # Load the Python module module load cray-python # Run your Python progamme python python_test.py
Programmes that have been parallelised with mpi4py can be run on multiple
processors on ARCHER2. A sample submission script is given below. The primary
difference from the Python submission script in the previous section is that we
must run the programme using
srun python my_prog.py instead of
my_prog,py. Failing to do so will cause a segmentation fault in your programme
when it reaches the line
from mpi4py import MPI.
#!/bin/bash --login # Slurm job options (job-name, compute nodes, job time) #SBATCH --job-name=mpi4py_test #SBATCH --nodes=1 #SBATCH --tasks-per-node=2 #SBATCH --cpus-per-task=1 #SBATCH --time=0:10:0 # Replace [budget code] below with your budget code (e.g. t01) #SBATCH --account=[budget code] # Load the Python module module load cray-python # Run your Python programme # Note that srun MUST be used to wrap the call to python, otherwise an error # will occur srun python mpi4py_test.py