The following scripts provide a foundation to use a different Python version
on the the Faculty Platform servers than what's shipped in the default Python2
and Python3 Conda environments (Python 2.7.16 and 3.6.10 respectively, at the time
of writing). These scripts are intended to be a quick fix while the Platform
implements support for newer Python versions, and thus there are quirks of
behaviour to take into account, as explained in this documentation.
There are 3 scripts attached here:
01_plain_environment.sh- this is a barebones environment, to be minimal and quick to start up02_default_packages.sh- an environment that installs the default packages found on the platform platform03_gpu_default_packages.sh- this environment is the same as the previous one, with the addition of installing the GPU-enabled versions of the Tensorflow library (versions the same as on the platform)
Create a Faculty environment for your custom Python setup. In the environment's Script
section, copy in the whole content of the relevant script that you have chosen.
To adjust the behaviour of the script, edit the variables present at the top of the script:
PYTHON_VERSION sets the version of Python to be installed. Not all Python libraries support
all Python versions, and thus not all Python version / script combinations have been tested.
Please do your own testing whether any given library functions properly with the Python version
you've chosen!
CONDA_ENV is the name of the new Conda environment (not
to confuse with Faculty environment), where the new Python version is set up. Faculty uses Conda
to create isolated Python environments in the platform servers, which can differ in their Python
version, their installed packages, etc. The Conda environment name is used to refer to these. By
default the Platform currently ships with two environments, Python2 and Python3, while this
script will create a third environment to use, called Custom-3.7 in its current form. You can
change this value if you prefer to something more natural for your use case.
If you want to specify extra packages to be installed in the given environment, the scripts have
marked sections where those modifications (specifying package list for conda install and
pip install) can be made.
Note that with these scripts, you cannot use the Faculty environments' "Python" section to define your Python packages (with Pip and Conda), as they apply to the Faculty-provided Python environments only. You need to use the scripts' variables as described above to set your Python package dependencies!
For interactive servers, we recommend using the 02_default_packages.sh environment. It
takes a bit longer time to start up because of the installation steps, but results in
an server that closely follows the setup of the Faculty-provided Python environment. This
script also automatically sets up automatic activation of this new environment in the
terminal of interactive servers.
When using Jupyter, you have to manually select this new environment from the list of your kernels to use:
In jobs, you will have to manually activate the environment in your job script before being able to use. Thus you might have to wrap those Python scripts in a small shell script to activate the Conda environment before running the Python calls, for example:
#!/bin/bash
conda activate Custom-3.7
<... rest of the script ...>
