Debugpy FAQs

Question 1

What is Debugpy and how does it enhance debugging in Docker?

Accepted Answer

Debugpy is a tool that injects the `debugpy` debugger into already-running Python processes inside Docker containers. It enhances your debugging workflow by providing rich context, retrieving recent logs, suggesting intelligent breakpoint plans, and enabling full IDE-equivalent debugger control via the Debug Adapter Protocol (DAP).

Question 2

What types of Python applications does Debugpy support?

Accepted Answer

Debugpy is highly effective for debugging any Python application running in a Docker container. It's particularly optimized for FastAPI-style services and automatically prioritizes uvicorn and gunicorn worker processes for more targeted and efficient debugging.

Question 3

Can I use Debugpy with my existing IDE?

Accepted Answer

Yes, Debugpy provides full IDE-equivalent debugger control through Debug Adapter Protocol (DAP) sessions. This means you can connect from any DAP-compatible IDE (such as VS Code or Cursor) to set breakpoints, step through code, inspect variables, and manage your debugging session seamlessly.

Question 4

Are there any prerequisites for using Debugpy to attach to containers?

Accepted Answer

To use Debugpy, the `debugpy` library must be installed inside your target Docker container. Additionally, attaching to a running process may require specific container capabilities, such as `cap_add: [SYS_PTRACE]`, to grant necessary permissions. Correct path mappings between your local code and the container's code are also crucial for breakpoints to resolve correctly.

Question 5

How does Debugpy handle multi-container or multi-process debugging scenarios?

Accepted Answer

Debugpy supports multi-container debugging, allowing you to manage independent debug sessions for each `(host, port)` pair. For multi-process setups like Gunicorn or Uvicorn, it intelligently targets worker processes rather than the master, ensuring you debug the actual request-handling code. Path mapping can be auto-detected or manually configured to simplify these complex environments.

Debugpy

Debugpy

Key Features

Use Cases

Key Features

Use Cases