DevContainers Setup in Visual Studio Code

DevContainers Setup in Visual Studio Code#

DevContainers provide a pre-configured development environment for PyRIT that ensures consistency across all contributors. This is the recommended setup for contributors using Visual Studio Code.

Note

Development Version: This setup uses the latest development code from the main branch, not a stable release. The notebooks in your cloned repository will match your code version.

Who Should Use DevContainers?#

✅ Use DevContainers if you:

  • Use Visual Studio Code as your editor. (Note that DevContainers can be used independently but our installation guide leverages VS Code since it’s seamless.)

  • Want a pre-configured development environment

  • Want consistency with other contributors

  • Prefer not to manage Python environments manually

  • Need all development tools and extensions pre-installed

❌ Consider local installation if you:

  • Use a different IDE or editor

  • Prefer full control over your development environment

  • Need to customize your setup beyond what DevContainers offer

Prerequisites#

Before starting, install:

  1. Docker (Docker Desktop if you are using Windows)

  2. Visual Studio Code

  3. DevContainers Extension in VS Code

You can also follow the Installation section on Developing inside a Container for more details.

Setup Steps#

1. Clone the PyRIT Repository#

git clone https://github.com/Azure/PyRIT
cd PyRIT

2. Open in VS Code#

Open the PyRIT folder in Visual Studio Code:

code .

3. Reopen in Container#

Make sure Docker is running first, for example, by starting “Docker Desktop.”

Press Ctrl + Shift + P (or Cmd + Shift + P on macOS) to open the VS Code Command Palette, then type and select:

Dev Containers: Reopen in Container

DevContainer in VS Code Commands Menu

VS Code will:

  1. Build the development container (this may take several minutes the first time)

  2. Install all dependencies

  3. Configure the development environment

  4. Reopen VS Code inside the container

Tip

The first build takes longer as Docker downloads and configures everything. Subsequent opens will be much faster!

Working with Jupyter Notebooks#

Selecting a Kernel#

When working with Jupyter Notebooks (.ipynb files):

  1. Open a notebook file in VS Code

  2. Click the “Select Kernel” button in the top-right corner of the notebook

  3. Choose “Python Environments…”

  4. Select the pyrit-dev kernel

Alternatively, you can use the Command Palette:

  • Press Ctrl + Shift + P (or Cmd + Shift + P on macOS)

  • Type >Notebook: Select Notebook Kernel

  • Choose “Python Environments…” > pyrit-dev

This kernel will run all code examples in Python Notebooks.

Viewing Jupyter Variables#

To view variables populated by code examples:

  1. Go to View > Output

  2. Select Jupyter from the dropdown menu

Important

Note for Contributors: When constructing a pull request, notebooks should not be edited directly. Instead, edit the corresponding .py file. See Working with Notebooks for more details.

Populating Secrets#

After setting up your DevContainer, configure your API keys and secrets:

See Populating Secrets for detailed instructions.

Common DevContainer Operations#

Rebuild the Container#

If you need to rebuild the container (e.g., after dependency changes):

  1. Press Ctrl + Shift + P (or Cmd + Shift + P on macOS)

  2. Type and select: Dev Containers: Rebuild Container

Reopen Locally#

To exit the container and return to your local environment:

  1. Press Ctrl + Shift + P (or Cmd + Shift + P on macOS)

  2. Type and select: Dev Containers: Reopen Folder Locally

Troubleshooting#

Container Build Fails#

Problem: DevContainer fails to build

Solutions:

  1. Ensure Docker is running

  2. Check that you have sufficient disk space

  3. Try rebuilding: Dev Containers: Rebuild Container Without Cache

Extension Not Loading#

Problem: VS Code extensions don’t load in the container

Solution: Check the .devcontainer/devcontainer.json file to ensure extensions are listed. Rebuild the container if needed.

Performance Issues#

Problem: Container runs slowly

Solutions:

  1. Allocate more resources to Docker in Docker Desktop settings

  2. On Windows, ensure you’re using WSL 2 backend for better performance

  3. Close unnecessary applications to free up system resources

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