Build/manage Python and R virtual environments with conda¶

Before working with conda environments, start by installing conda.

After installing conda, the only environment will be the default called base. You can install anything you like in base but we recommend limiting this to only the most essential commonly used tools you use because the larger an environment gets the more complicated the dependencies become.

List available environments¶

conda env list

# Example output
# conda environments:
#
# base                  *  /home/user/miniconda3
# datasci                  /home/user/miniconda3/envs/datasci
# plantcv                  /home/user/miniconda3/envs/plantcv

The star indicates the current active environment, which you can also see listed next to your command-line prompt, for example: (base) [username@ip-10-0-0-38 ~]$

Activate an environment¶

Activating an environment alters your shell session so that your active paths are set to the environment executable directory and libraries.

Activate the datasci environment in the example above:

conda activate datasci

You should note the change to your command-line prompt: (datasci) [username@ip-10-0-0-38 ~]$

Deactivate and environment¶

You can turn off an environment by deactivating the currently activate environment.

conda deactivate

Deleting an environment¶

Environments can be removed, in the example below we delete the datasci environment:

conda env remove -n datasci

Working with conda packages¶

Packages installed in a conda environment are downloaded from the Anaconda package repository. conda packages are maintained by a variety of sources, including core packages in the default channel maintained by Anaconda, and commonly used community channels conda-forge and bioconda. However, regardless of channel, these packages are all findable through the search interface at Anaconda. For example, if you wanted to install the R package ggplot2 and you search for it, you will find that the package name is r-ggplot2 and it is available from the conda-forge channel (it is also available in other channels, but the versions are older).

Install one or more packages in the active environment¶

When working in an environment, you can install additional packages. For example:

A new Python package:

conda install -c conda-forge plantcv

A couple R packages:

conda install -c conda-forge r-ggplot2 r-dplyr

A specific version of a program:

conda install -c bioconda samtools=1.15.1

Create a new environment with a command¶

For relatively simple environments, you can specify all the packages on the command-line:

conda create -n myenv -c conda-forge -c bioconda 'r-base>=4' r-ggplot2 samtools

Create a new environment from a configuration file¶

For larger environments and for better documentation and reproducibility, you can build a simple configuration file with environment specifications. The environment files are encoded in YAML, for example environment.yml:

name: myenv
channels:
    - conda-forge
    - bioconda
dependencies:
    - python=3.10
    - matplotlib
    - numpy
    - pandas
    - scipy
    - scikit-image
    - scikit-learn
    - opencv>=4

Then to create the environment:

conda env create -f environment.yml