Initializing help system before first use

Configuring Additional Environments

You can define multiple Conda environments and associate them with different Xpress Insight execution environments. This makes it possible to use different environments for different Xpress Insight apps.
In addition to the environment that is loaded by default, you can define any number of additional Conda environments by creating multiple environment.yml files. You can then associate these Conda environments with specific Insight execution environments, which are mapped to applications in the Insight administrator interface.

To create a new Conda environment, follow these steps:

  1. From the root directory for the configuration you are using, navigate to the environments subdirectory.
  2. Create a new subdirectory (for example, environments/my_env).
    Each environment.yml file must be in the environments directory or an immediate subdirectory.
  3. In the new subdirectory, create a new environment.yml file.
    The easiest way to do this is to copy an existing environment.yml file, such as the default example file in the environments directory. (If you do copy an existing environment, make sure you edit the name scalar to give the new environment a unique name.) For details about how to write the environment.yml file, see the Conda documentation.
  4. From the root directory of the configuration you are using, edit the worker-config/override.properties file.
    1. Add the following lines. (Long lines have been split for readability. In the override.properties file, each name/value pair must be on a single line.) 
      insight.worker.execution.custom-environment.INSIGHT_ENV.MINICONDA_ENV=conda-env

insight.worker.execution.custom-environment.INSIGHT_ENV.PATH=
    ${MINICONDA_ROOT_PREFIX}/envs/conda-env/bin:${PATH}
      where:
      • conda-env is the name of the Conda environment, as specified in environment.yml.
      • INSIGHT_ENV is a name for the Insight execution environment you want to map to this Conda environment. This does not need to be identical to that of the Conda environment, but it is a good practice to make it similar (subject to casing conventions).
    2. Add the required language-specific properties, depending on which languages the environment uses:
      • Python:
        insight.worker.execution.custom-environment.INSIGHT_ENV.PYTHON_EXE=${MINICONDA_ROOT_PREFIX}/envs/conda-env/bin/python
      • Java:
        insight.worker.execution.custom-environment.INSIGHT_ENV.JAVA_HOME=${MINICONDA_ROOT_PREFIX}/envs/conda-env/lib/java
      • R:
        insight.worker.execution.custom-environment.INSIGHT_ENV.R_HOME=${MINICONDA_ROOT_PREFIX}/envs/conda-env/lib/R
    3. Add any other application properties the environment requires, in the same format: 
      insight.worker.execution.custom-environment.INSIGHT_ENV.PROPERTY=value
      Note: The override.properties file might define custom properties for the default environment in the following format: 
      insight.worker.execution.environment.PROPERTY=value
      Any such properties are applied to all execution environments, so you should make sure that they are valid for the new environment you are configuring. If they are not, override them or unset them (by specifying an empty value) in the properties for the new environment.
    The following example shows the properties that you might add in override.properties for a custom Conda environment called python-311, mapped an Insight custom execution environment called py_311. (Long lines have been split for readability. In the override.properties file, each name/value pair must be on a single line.) 
    # Custom environment for Python 3.11. Assumes that the conda environment
# python-311 is defined in an environment.yml file.

insight.worker.execution.custom-environment.py_311.PATH=
    ${CONDA_ROOT_PREFIX}/envs/python-311/bin:${PATH}

insight.worker.execution.custom-environment.py_311.PYTHON_EXE=
    ${CONDA_ROOT_PREFIX}/envs/python-311/bin/python

insight.worker.execution.custom-environment.py_311.JAVA_HOME=
    ${CONDA_ROOT_PREFIX}/envs/python-311/lib/java

insight.worker.execution.custom-environment.py_311.R_HOME=
    ${CONDA_ROOT_PREFIX}/envs/python-311/lib/R
  5. (Optional) If you want the new environment to be the default environment, edit the .env file in the root directory for the configuration you are using.
    Change the value of the MINICONDA_ENV environment variable to match the new environment name. This environment variable specifies which environment is to be loaded by default when the worker container starts.
  6. Stop and restart the worker container using docker compose down and docker compose up (or Docker Desktop).
On the Admin page of the Xpress Insight user interface, you can now map Insight apps to the Insight execution environment you specified. (For more information, see Mapping Execution Environments to Apps.) You can also specify the Insight execution environment when publishing an app to Insight from Xpress Workbench.
Parent Topic Working with Conda Environments

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