Configure a Conda virtual environment
IntelliJ IDEA 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.
To 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.
Refer to the installation instructions for more details.
Ensure that the Python plugin is installed and enabled.
Navigate to
Ctrl+Alt+Shift+S.In the Project Structure dialog, select SDKs under the Platform Settings section, click , and from the popup menu, choose Python SDK.
In the left-hand pane of the Add Python Interpreter dialog, select Conda Environment. The following actions depend on whether the Conda environment existed before.
If New Virtualenv is selected:
Specify the location of the new Conda environment in the text field, or click and find location in your file system. Note that the directory where the new Conda environment should be located, must be empty!
Select the Python version from the list.
Specify the location of the Conda executable file in the text field, or click and find location in the Conda installation directory. You're basically looking for a path that you've used when installing Conda on your machine.
Select the Make available to all projects checkbox if you want to reuse this environment when creating Python interpreters in IntelliJ IDEA.
If Existing environment is selected:
Expand the Interpreter list and select any of the existing interpreters. Alternatively, click and specify a path to the Conda executable in your file system, for example, C:\Users\jetbrains\Anaconda3\python.exe.
Select the Make available to all projects checkbox if you want to reuse this environment when creating Python interpreters in IntelliJ IDEA.
Click OK to complete the task.
IntelliJ IDEA can create a Conda environment for your project based on the environment.yml file.
For any of the configured Python interpreters (but Docker-based), you can: