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Using Conda Environments

Create Environment

After loading a Miniconda module version for the first time and configuring your .condarc file, create a new conda environment named my_conda_env in your /projectnb space. We recommend use of the mamba command for superior speed than the traditional conda command. Note that mamba will create a completely empty environment, so we are installing Python version 3.10 in the example below.

[rcs@scc1 ~] module load miniconda
[rcs@scc1 ~] mamba create -y -n my_conda_env python=3.10
...
Preparing transaction: done
Verifying transaction: done
Executing transaction: done

If you plan to use a Jupyter notebook or Jupyter Lab  or Spyder for your code development, you need to install notebook, jupyterlab, or spyder packages, respectively:

[rcs@scc1 ~] module load miniconda
[rcs@scc1 ~] mamba create -y -n my_conda_env python=3.10 notebook

 

 

Activate Environment

Activate my_conda_env. Note that once activated, the command prompt begins with (my_conda_env) signifying you are inside the conda environment.

[rcs@scc1 ~] mamba activate my_conda_env
(my_conda_env) [rcs@scc1 ~]

 

Install Packages in Environment

To add packages released via conda channels, use mamba install. For packages released only though PyPI, use pip install.

Conda Packages

(my_conda_env) [rcs@scc1 ~] mamba install -y -c conda-forge spyder
...
Preparing transaction: done                                                     
Verifying transaction: done                                                     
Executing transaction: done

In the above example, we installed Spyder from the conda-forge channel. There are many other conda channels such as bioconda, anaconda, or defaults.

PyPI Packages

(my_conda_env) [rcs@scc1 ~] pip install htseq
...
Installing collected packages: pysam, numpy, htseq
Successfully installed htseq-2.0.4 numpy-1.26.1 pysam-0.22.0

In the above example, we installed HTSeq into my_conda_env same as done within Python.

 

Installing and Distributing Conda Environments

Software are often distributed as conda environments through use of YAML configuration files.

Install from YAML

Here is an example of creating a conda environment from a YAML file. With the -n name argument an environment will be created in the location specified in your .condarc file. The -p /path/to/new/env flag can be used to place the environment in a specific location:

[rcs@scc1 ~] mamba env create -n polymer_env -f environment.yml

In the above example, we installed Polymer from the distributed YAML file.

Export to YAML

Here is how you export an activated conda environment to a YAML format file:

(my_conda_env) [rcs@scc1 ~] mamba env export -f environment.yml

This creates a YAML file with the conda and PyPI packages listed with the version and a build specification for each one. To export an environment with just the versions listed add the --no-builds flag. This can be helpful if you want to move an environment between operating systems, such as re-creating a conda enviroment from the SCC on a Mac OSX laptop:

(my_conda_env) [rcs@scc1 ~] mamba env export --no-builds -f environment.yml

 

 

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