Python

This guide will show you how to seamlessly integrate Doppler into your Python application development and debugging workflow inside Visual Studio Code.

Prerequisites

• Doppler CLI installed locally and authenticated
• Created a Doppler Project for your application

Setup

Ensure you've authenticated the Doppler CLI so secrets can be accessed from your machine:

doppler login

Then configure the Doppler CLI to select which project and environment to fetch secrets from by running the following command from a terminal within Visual Studio Code:

doppler setup

Configuration

Because Visual Studio Code uses a custom Microsoft Python debug library, the Doppler CLI cannot be used to run your application directly.

That's why we created the doppler-env package which when activated with the DOPPLER_ENV environment variable, injects secrets as environment variables into the Python debug process prior to your application code being run.

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Ensure Visual Studio Code is configured to use the Python interpreter belonging to this application's virtual environment by running the Python: Select Interpreter command.

Install the doppler-env package in your virtual environment:

pip install doppler-env

pipenv install doppler-env --dev

poetry add doppler-env@latest --dev

Then simply update your Python launch configuration to add the DOPPLER_ENV environment variable:

{
  "version": "0.2.0",
  "configurations": [
    {
      "name": "python: server",
      "type": "python",
      "python": "${env:HOME}/.virtualenvs/python-web3/bin/python",
      "request": "launch",
      "program": "web3-start.py",
      "env": {
        "DOPPLER_ENV": "1" // Enable doppler-env to inject env vars
      }
    }   
  ]
}

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Awesome Work!

Now you know how to inject Doppler secrets into your Python applications when debugging with Visual Studio Code.