Changing default environments and adding Python extensions for additional Python libraries

You can change the default environment for your experiment from the Overview tab. For example, you can change the Python version or CPLEX version that is used by default. You can also create a new environment and add more Python libraries by using a new Python extension.

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

1. Open the Overview, click the information icon 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 a 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), and the Python version.
   3. Set Associate a Python extension to On to include any Python libraries that you want to add.
   4. Click New Python extension.
   5. Enter a name for your extension in the new Create a Python extension window that opens, and click Create.
   6. 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.

      If necessary, modify the YAML code and click Save.

      Click Done.

   7. Click Create in the New environment window.
5. Optional: 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 that are provided by clicking the menu icon next to an environment. You can choose to Edit, Set as default, Update in 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.

6. Click the Python extensions tab.

   

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

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