Conda Environments

Virtual environments are a convenient way to manage library dependencies, environment variables, and ensure reproducibility. There are a couple of approaches to this: virtualenv,conda, and docker – see here for a discussion. This post will focus on conda, and give a few practical commands to get up and running.

• Download and install miniconda

  sh curl https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -o "$conda_dir/miniconda.sh"
  sh -x miniconda.sh -b -p "./miniconda3" 
  

Note: The default conda set-up requires editing the .bashrc file and setting environment variables to point to the conda executable. This is a pain when dealing with multiple servers, fortunately there are ways around this and the commands given here will not rely on editing the .bashrc.

Basic commands

• Create environment called conda_venv, for Python version 3.8
  ./miniconda3/bin/conda create -n conda_venv python=3.8
• Activate environment
  source ./miniconda3/bin/activate conda_venv
• De-activate environment
  conda deactivate conda_venv
  This adds the environment executables such as Python, pip and conda to the executable path.
• Install/ uninstall: (once env is activated)
  • Through conda: conda install -c anaconda numpy
  • Through pip: pip install numpy
    Note: this pip executable will be installed when installing python, and the libraries installed via pip will be specific to the conda environment and not the global environment

• Export installed dependencies to file
  conda env export > env.yaml

• Install dependencies from file: this is actually creating an environment from a yaml file, so no need to create an empty env first conda env create -f environment.yml

Conda environments with jupyter notebook

• Make sure jupyter is installed
  pip install jupyter
• Add kernel for environment
  python -m ipykernel install --user --name=conda_venv
• For windows conda install pywin32