How-To: Creating personalized conda environments

Owned by Freddy Lopez
Last updated: Aug 12, 2024
4 min read

Overview:

Conda is an open-source package management and environment management system that simplifies the process of installing, updating, and managing software packages and their dependencies across various programming languages. It is particularly useful for creating isolated environments for different projects, ensuring compatibility and reproducibility. miniconda3 is currently available on Talapas2, enabling users to create and manage their own environments at their convenience and according to their workflow needs.

Methods for Creating Conda Environment:

There are two primary methods for creating conda environments. The first method involves using an environment.yml file, which specifies the desired packages and their versions. This approach allows for easy sharing and replication of environments across different systems, ensuring consistency in dependencies. The second method involves manually creating a base conda environment and then using pip to install additional packages as needed. This approach offers flexibility for adding packages incrementally and customizing the environment on-the-fly. Both methods have their advantages and can be chosen based on the specific needs of the project.

Loading miniconda3:

In order to create a conda environment, we need to have conda available within out session. To do this, simply load miniconda using module load:

$ module load miniconda3/20240410

Once miniconda3 is loaded successfully, choose a method in which to create your environment.

Using an environment.yml:

  1. Create an environment.yml file: Start by creating a new text file named environment.yml in your project directory.

    1. $ touch environment.yml

  2. Specify Environment Configuration: Open the environment.yml file in a text editor of your choice and specify the desired configuration for your environment. This typically includes the name of the environment (name), the list of packages to be installed (dependencies), and optionally the channels from which to install packages (channels). For example:

    1. name: example_environment channels: - defaults - conda-forge dependencies: - python=3.8 - numpy - pandas - matplotlib - scikit-learn

    2. Note regarding channels:

      1. In many instances, the default channels, along with conda-forge, are adequate for installing the specified dependencies. However, should certain packages not be available in either of these channels, you have the option to specify the channel where the package can be located. Typically, the package's documentation will indicate the appropriate channel.

    3. Data Science packages

      1. If you are creating an environment in which you would like many of the common packages used for data science, there exists a dependency you can include named datascience that will install those common packages without needing to specify each one.

    4. Using conda for installing R packages

      1. Conda also allows users to be able to create environments for R and allows users to create and manage their own R environments. To do this with an environment.yml file, you will need to specify a few additional channels for the R libraries. An example environment.yml with the necessary channels and some example packages can be seen below:

  3. Creating the conda environment with miniconda3: Once the environment.yml is complete with all required dependencies, we can create the environemnt with the following command:

Upon successful completion, you can activate the environment by using:

Note: (“example_environment” is the name specified in environment.yml)

This will activate the environment within your session and allow you to access all the packages that were installed. If it turns out you need more packages than originally specified, you can add more by using $ pip install <package name> . This will add said package onto your environment.

  1. Deactivating conda environment:Once finished with your session, simply enter:

This will quit the conda environment until you reactivate it again when needed.

 

Not using environment.yml:

  1. Create a conda environment: To create a conda environment without using an environment.yml file, simply provide a name (“example_environemnt")for your environment in the command below:

    1. Note: you can specify the version of python as seen above with python=VERSION. If not specified, conda will use its default python version.

  2. Activate environment: Once the environemnt has been created, simply activate it via the command: $ conda activate example_environment (where “example_environment” is the name of your environment).

  3. Using pip to install packages: Once activated, you have the flexibility to incrementally add new packages and dependencies to your environment as needed to adapt to changing workflow requirements. This allows you to create a tailor the environment with only the packages you need.

  4. Deactivating conda environment:Once finished with your session, simply enter:

This will quit the conda environment until you reactivate it again when needed.

 

Reactivating Existing Conda Environments:

To reactivate an existing environment that was created:

  1. Load miniconda3:

  2. Find environment name: If you forgot what your environments name was, you can either refer back to your environment.yml file or use the command: $ conda env list. This will produce a list of all the available conda environments and should help you remember what the name was.

  3. Activate environment: As before, once you have the name of your created environment, simply do:

  4. Deactivating conda environment:Once finished with your session, simply enter: