Remote Debugging with VS Code

Introduction

In this tutorial, we will show how to set up remote debugging with VS Code on the platform using the flow template.

Initializing a new project

Make sure you have CLI and cookiecutter installed, refer to https://github.com/neuro-inc/apolo-documentation/blob/master/apolo-console/first-steps/getting-started.md for instructions.

Then, initialize an empty flow:

$ cookiecutter gh:neuro-inc/cookiecutter-neuro-project --checkout release

The project initialization command asks several questions about your project:

project_name [Name of the project]: Apolo VSCode
project_dir [apolo-vscode]:
project_id [apolo-vscode]:
code_directory [modules]: 
preserve Neuro Flow template hints [yes]:

Configuring the project

Add debugpy to your project's requirements.txt file (located in the project's root folder):

apolo-flow

flake8
mypy
isort
black
debugpy

Add the following lines to the beginning of your code file. In this example, it's modules/train.py.

import debugpy
debugpy.listen(("0.0.0.0", 5678))
debugpy.wait_for_client()

Next, configure the project's environment on the platform:

$ apolo-flow build myimage

When the image is built, you can upload your code from a local file to the platform storage:

$ apolo-flow upload code

By default, this will upload everything from your project's modules folder to the storage:<your_project_id>/modules storage folder. To configure the source and the target for this command, go to your project's .neuro/live.yml file and find the code section under volumes:

volumes:
  data:
    remote: storage:$[[ flow.project_id ]]/data
    mount: /project/data
    local: data
  code:
    remote: storage:$[[ flow.project_id ]]/modules
    mount: /project/modules
    local: modules
  config:
    remote: storage:$[[ flow.project_id ]]/config
    mount: /project/config
    local: config
    read_only: True

Here, you can specify your local code folder and the storage folder you want to upload this code to.

Running your code

In this example, we will be running a training job based on the code contained in the train.py file we just uploaded to the platform storage. To do this, run:

$ apolo-flow run train

Once the job is running, detach from it by pressing Ctrl+P, Ctrl+Q and run the following command:

$ apolo job port-forward <job-id> 5678:5678

This will allow you to access the job by the 5678 port.

Debugging

Open your code file in VS Code and navigate to Run > Start Debugging or press F5:

Select Remote Attach:

Enter localhost as the host name and the job's port number (in this case, it's 5687):

When this is done, you can set the breakpoint and start debugging.