Setting up a Python Environment¶
You can also use these instructions to set up python environments on your personal computer
Setting up a python environment using Anaconda¶
The Anaconda distribution gives you access to many commonly used libraries and is the fastest way to set up a ready to use python environment. It is also the recommended way of installing jupyter.
Begin by going to the Anaconda website and copying the link to the lastest Linux ‘64-Bit (x86) Installer.
Download it onto alder using
wget "<link to installer file>" e.g.
$ wget "https://repo.anaconda.com/archive/Anaconda3-2019.03-Linux-x86_64.sh"
Install Anaconda using
bash <name of the file you just downloaded>, then following the installer’s instructions.
It is highly recommended to use the default settings provided by the installer.
$ bash Anaconda3-2019.03-Linux-x86_64.sh
Installing conda only¶
The links to the Miniconda installer can be found here, and can be installed using the same procedure as in the previous section.
Miniconda only includes python and the conda package manager.
Setting up a custom python environment¶
1. Using conda¶
Conda combines pip and venv and offers a better user experience. This excellent tutorial from UoA e-research gives concise instructions on setting up virtual environments using conda.
2. Using venv¶
venv comes into play if you prefer not to work with conda.
Replace ‘myvenv’ with whatever path you want to keep you virtual environment in.
# create the venv $ python3 -m venv myvenv # activate the venv $ source myvenv/bin/activate # you should now see the name of our venv in the terminal prefix (myvenv) [user@<cluster> ~]$
$ pip install --upgrade pip
You can view the packages you have install into your virtual environment using
You should see something like:
Package Version ---------- ------- pip 19.1.1 setuptools 28.8.0
Now we can install some packages
# install a package $ pip install numpy # install many packages at once $ pip install scipy matplotlib # install a particular version of a package $ pip install h5py==2.9.0
You can deactivate the virtual environment with
Whenever you want to use the environment again, activate it using
source <path to venv activate>, e.g.
$ source myvenv/bin/activate
If you want to save your environment configuration, use
$ pip freeze > requirements.txt
You can also use a requirements file to build or modify your virtual environment
$ pip install -r requirements.txt