I found a solution, you can create your training script in the space of colab, and then execute it from colab with

!python training.py

and training.py has all that needs from clearML, and it works perfectly!

Hi @<1628202899001577472:profile|SkinnyKitten28> ! What code do you see that is being captured?

Hello!

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"""Custom kernel launcher app to customize socket options."""

from ipykernel import kernelapp
import zmq

We want to set the high water mark on all sockets to 0, as we don't want

the backend dropping any messages. We want to set this before any calls to

bind or connect.

In principle we should override init_sockets, but it's hard to set options

on the zmq.Context there without rewriting the entire method. Instead we

settle for only setting this on iopub, as that's the most important for our

use case.

class ColabKernelApp(kernelapp.IPKernelApp):

def init_iopub(self, context):
context.setsockopt(zmq.RCVHWM, 0)
context.setsockopt(zmq.SNDHWM, 0)
return super().init_iopub(context)

if name == 'main':
ColabKernelApp.launch_instance()