Configure a virtualenv environment
Aqua makes it possible to use the virtualenv tool to create a project-specific isolated virtual environment. The main purpose of virtual environments is to manage settings and dependencies of a particular project regardless of other Python projects. virtualenv tool comes bundled with Aqua, so the user does not need to install it.
Create a virtualenv environment
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 Virtualenv Environment.
The following actions depend on whether you want to create a new virtual environment or to use an existing one.
- New virtual environment
Select the base interpreter from the list, or click
and find the Python executable in your file system.Specify the location of the new virtual environment in the Location field, or click
and browse for the location in your file system. The directory for the new virtual environment should be empty.Select the Inherit global site-packages checkbox if you want all packages installed in the global Python on your machine to be added to the virtual environment you're going to create. This checkbox corresponds to the
--system-site-packagesoption of the virtualenv tool.Select the Make available to all projects checkbox if you want to reuse this environment when creating Python interpreters in Aqua.
- Existing virtual environment
Choose the desired interpreter from the list.
If the desired interpreter is not on the list, click
, and then browse for the desired Python executable (for example, venv/bin/python on macOS or venv\Scripts\python.exe on Windows).
The selected virtual environment will be reused for the current project.
Click OK to complete the task.
You can create as many virtual environments as required. To easily tell them from each other, use different names.
This approach is particularly helpful when you want to upgrade a version of Python your environment is based on, for example, from 3.5 to 3.9. You can specify a new base interpreter and use requirements.txt to ensure all the necessary packages are installed.
For any of the configured Python interpreters (but Docker-based), you can: