Yes. Here is some simple code that reproduces the issue:

import argparse
from datetime import datetime

import clearml


def train(args):
    print(f"Running training: {args}")


def test(args):
    print(f"Running testing: {args}")


def parse_args():
    parser = argparse.ArgumentParser()
    subparser = parser.add_subparsers(dest="subparser")

    train_parser = subparser.add_parser("train")
    train_parser.add_argument("project", type=str)
    train_parser.add_argument("queue", typ...

Thanks, I’ll try that and report back.

I’m not sure if this was solved, but I am encountering a similar issue. From what I see it all depends on what multiprocessing start method is used.
When using fork ClearML works fine and it’s able to capture everything, however it is not recommended to use fork as it is not safe with multithreading (e.g. see None ).
With spawn and forkserver (which is used in the script above) ClearML is not able to automatically...

Thank you @<1523701205467926528:profile|AgitatedDove14> . I have Task.init() right at the beginning of the script (i.e. before multiprocessing), but I don’t have the Task.urrent_task() call, so maybe that would solve the issue. Where should that be? In the function that is parallelised? Or can it also be right after Task.init() ?

Hi, I am on clearml == 1.9.0 and I am having the same issue.
Is there a recommended workaround or plans to fix it?

Hi @<1523701205467926528:profile|AgitatedDove14> ,
I can confirm that calling Task.current_task() makes ClearML log the console, models and scalars again 🙂

When the execution starts locally the args are like:

Namespace(subparser='train', project='test', epochs=0)

then remotely they get converted to:

Namespace(subparser="['train', '--project', 'test', '--epoch', '0']", project='test', epochs=0)

Which is similar to what Tim reported a few messages above.

So when in the code I do something like if args.subparser == "train": ... I get a normal behaviour locally (i.e. True ), but not remotely because args.subarser...