This document guides MAAP users in the process of selecting, extending existing environments (the set of libraries availables for analysis) or creating custom environments.


The MAAP ADE offers various workspace options, each workspace coming with its own environment that has pre-installed essential libraries for computing and geospatial analysis. At the time of writing this guide, here are the options :

Workspace image options

For example, the MAAP RGEDI Stable and MAAP R Stable workspace options come with various pre-installed R packages.

For more information : Each of these options rely on Docker images that were build off from Dockerfiles that are publicly available in the MAAP workspace repository. If you want to learn more about what libraries each image contains, check out this repository.

Extending environments

Users may need libraries for their specific analysis purposes that are not present in the environments of the different workspace options offered. In this case, ideally, the steps should be the following :

  1. The user explores her/his environment need by extending the environment of an existing workspace or creating her/his custom environment in an existing workspace (see next sections).
  2. Once that is done, the user submits a ticket/coordinates with the platform team to create a new workspace option with the requested, finalized environment.

The above approach is ideal because modifications to the pre-defined workspace environment do not survive a workspace restart (see next sections), and because sharing new experimented environments is valuable.

The next sections explain how to extend environments or create custom environments, and for this, introduces information regarding which environment management solution we are using.

Package manager

We use mamba (a fast conda drop-in replacement) as a package manager to install, update or remove packages (libraries). mamba works with ‘environments’ that are directories in your local file system containing a set of packages. When you work ‘in a given environment’, it means you that your programs will look for dependencies in that environment’s mamba directory. All workspaces launch with a environment called base, which is a mamba environment that has all the pre-installed libraries. If you open a terminal launcher after creating a Basic Stable workspace :

Workspace image options

You can notice that a base mamba environment is activated, and its libraries are located in /opt/conda.

Extending the base environment in a given workspace session.

Note : any modification to the ``base`` environment does not survive a workspace restart. In other words, modifications to ``/opt/conda`` disappear after a workspace restart.

Extending an existing mamba environment means adding packages on top of what it contains, which works provided there are no dependency conflicts. You can use a configuration file specifying all the new libraries to add. It is recommended for reproducibility and shareability. See the below sections for configuration file usage.

Alternatively, you can install libraries using the mamba install command to install additional packages in your current environment (run mamba --help to learn more about how to use mamba commands). For example :

mamba install xarray

Custom environments

You can use the mamba CLI to create a new, custom environment.

The parameters (the list of libraries, the location where to search for them, etc…) can be passed either from a configuration YAML file or directly on the console. We recommend using the first option (a YAML file is easier to share and modify).

Basic custom environment

Here is an example configuration file that we name env :

# env.yml
name: env
    - conda-forge
    - python=3.8
    - pandas=1.5.3
    - geopandas=0.12.2

It installs specific versions python, pandas and geopandas from either conda-forge or defaults. If versions aren’t specified, the latest is installed. We recommend to always specify the version for reproducibility. The basic command to create this environment would be :

mamba env create -f env.yml

However, this stores this environment files in /opt/conda, which a directory that is reset when the workspace restarts, and so custom environments are lost. Therefore, you want to specify a storage location in your user directory with the --prefix parameter

mamba env create -f env.yml --prefix /projects/env

and to activate it :

mamba activate env

Updating an existing environment with a configuration file

For this section and the next, you can find example configuration files in example_conda_configuration_files in the same folder as this notebook.

You can update an existing environment with a configuration file as well. For example, let’s assume you have a mamba environment with a set of packages already installed in it (for example the base environment), but it doesn’t have xarray and geopandas. You can create a file like base.yml, that specifies updates to the base environment. Then, running mamba env update -f base.yml will update base by adding xarray and geopandas, provided it does not cause conflicts with the existing libraries.

Using pip for python packages

Some python packages might not be availabe in the channel you are using, or in any mamba channel. If that package however is in PyPI (the official python package repository), one can use pip within a mamba environment to download packages. The recommended way is to specify this in the configuration file. You can find an example in env2.yml, that creates an environment named env2. In that example, we add stackstac as a dependency to install from PyPI because it is not available in the conda-forge channel.

Using custom environments in jupyter notebooks

To make your environment accessible in a Jupyter notebook, you need to register a kernel that has your environment. You can use the ipykernel install command for this, which means you must list ipykernel as a dependency in your configuration file. An example is in env3.yml, that creates an environment named env3. After creating it, you run the kernel registration command this way :

python -m ipykernel install --user --name env3 --display-name "Python env3"

Once this is done, wait around 30 seconds for the registration to propagate. Then, click on a new launcher button. Among the notebook options, you should see “Python env3”. This will spin up a notebook with your environment. Below you can find a screenshot showing the commands on the notebook :

Register a kernel with a conda environment and launch a notebook with it

Suggested packages for custom environment

MAAP users typically use the python maap-py. It’s pre-installed in all workspaces, in the base mamba environment, but any custom environment should specify it, otherwise it is not going to be accessible from that environment. However, maap-py is not packaged in a public package repository, like PyPI or conda-forge. It is possible to install it directly from its github repository with pip though. An example configuration file can be found at env4.yml. You can note that inside of it maap-py is ‘versioned’ using a commit hash (at the end of the github URL).