You can change your default environment for Python and CPLEX in the experiment Overview.

To change the default environment for DOcplex and Modeling Assistant models:

1. Open the Overview, click to open the Information pane, and select the Environments tab.

   

2. Expand the environment section according to your model type. For Python and Modeling Assistant models, expand Python environment. You can see the default Python environment (if one exists). To change the default environment for OPL, CPLEX, or CPO models, expand the appropriate environment section according to your model type and follow this same procedure.
3. Expand the name of your environment, and select a different Python environment.
4. Optional: To create a new environment:
   1. Select New environment for Python.
      A new window opens for you to define your new environment.
   2. Enter a name, and select a CPLEX version, hardware specification, copies (number of nodes), Python version and (optionally) you can set Associate a Python extension to On to include any Python libraries that you want to add.
   3. Click New Python extension.
   4. Enter a name for your extension in the new Create a Python extension window that opens, and click Create.
   5. In the new Configure Python extension window that opens, you can set YAML code to On and enter or edit the provided YAML code.
      For example, use the provided template to add the custom libraries:

      # Modify the following content to add a software customization to an environment.
      # To remove an existing customization, delete the entire content and click Apply.
      
      # Add conda channels on a new line after defaults, indented by two spaces and a hyphen.
      channels:
        - defaults
      
      # To add packages through conda or pip, remove the comment on the following line.
      # dependencies:
      
      # Add conda packages here, indented by two spaces and a hyphen.
      # Remove the comment on the following line and replace sample package name with your package name:
      #  - a_conda_package=1.0
      
      # Add pip packages here, indented by four spaces and a hyphen.
      # Remove the comments on the following lines  and replace sample package name with your package name.
      #  - pip:
      #    - a_pip_package==1.0

      You can also click Browse to add any Python libraries.

      For example, this image shows a dynamic programming Python library that is imported and YAML code set to On.

      Click Done.

   6. Click Create in the New environment window.
   Your chosen (or newly created) environment appears as ticked in the Python environments drop-down list in the Environments tab. The tick indicates that this is the default Python environment for all scenarios in your experiment.
5. Select Manage experiment environments to see a detailed list of all existing environments for your experiment in the Environments tab.
   

   You can use the options provided by clicking the three vertical dots next to an environment to Edit, Set as default, Update in a deployment space or Delete the environment. You can also create a New environment from the Manage experiment environments window, but creating a new environment from this window does not make it the default unless you explicitly set is as the default.

   Updating your environment for Python or CPLEX versions: Python versions are regularly updated. If however you have explicitly specified an older Python version in your model, you must update this version specification or your models will not work. You can either create a new Python environment, as described earlier, or edit one from Manage experiment environments. This is also useful if you want to select a different version of CPLEX for your default environment.
6. Click the Python extensions tab.

   

   Here you can view your Python extensions and see which environment it is used in. You can also create a New Python extension or use the options to Edit, Download, and Delete existing ones. If you edit a Python extension that is used by an experiment environment, the environment will be re-created.

   You can also view your Python environments in your deployment space assets and any Python extensions you have added will appear in the software specification.