Configure a conda virtual environment
Aqua supports creating virtual environments for Python with Conda. The following procedure applies to all supported operating systems. Use the platform switcher at the top of this page to view shortcuts specific to your operating system.
Create a conda environment
Ensure that Anaconda or Miniconda is downloaded and installed on your computer, and you're aware of a path to its executable file.
For more information, refer to the installation instructions.
Navigate to
or press Ctrl+Alt+Shift+S.In the Project Structure dialog, select SDKs under the Platform Settings section, click , and choose Add Python SDK from the popup menu.
In the left-hand pane of the Add Python Interpreter dialog, select Conda Environment.
The following actions depend on whether you want to create a new conda environment or to use an existing one.
- New conda environment
Specify the location of the new conda environment in the Location field, or click and browse for the desired location in your file system. The directory for the new conda environment should be empty.
Select the Python version from the list.
Normally, Aqua will detect conda installation.
Otherwise, specify the location of the conda executable, or click to browse for it.
Select the Make available to all projects checkbox if you want to reuse this environment when creating Python interpreters in Aqua.
- Existing conda environment
Select the environment from the list.
If the desired interpreter is not on the list, click , and then browse for the Python executable within the previously configured conda environment.
If necessary, specify the location of the conda executable, or click to browse for it.
Select the Make available to all projects checkbox if you want to reuse this environment when creating Python interpreters in Aqua.
The selected conda environment will be reused for the current project.
Click OK to complete the task.
If the directory with your source files contains an environment.yml file, Aqua can create a conda environment based on it.
For any of the configured Python interpreters (but Docker-based), you can: