However, deleting tasks gives me errors.

It is server version 1.0 and everything that came with it.

AgitatedDove14 I have to problem that "debug samples" are not shown anymore after running many iterations. What's appropriate to use here: A colleague told me increasing task_log_buffer_capacity worked. Is this the right way? What is the difference to file_history_size ?

https://clearml.slack.com/archives/CTK20V944/p1620855259093200 This thread may also be interesting for you.

Thanks for answering. I don't quite get your explanation. You mean if I have 100 experiments and I start up another one (experiment "101"), then experiment "0" logs will get replaced?

It is explained here: https://github.com/allegroai/clearml/issues/91#issuecomment-578544866

I am pretty sure there is a flag in the clearml.conf where you can specify which python binary to use.

MortifiedDove27 Sure did, but I do not understand it very well. Else I would not be asking here for an intuitive explanation 🙂 Maybe you can explain it to me?

Ah, thank you very much

Thanks, that makes sense. Can you also explain what task_log_buffer_capacity does?

By preexisting task I meant I have existing code that already uses Task.init . I would like to use this code as my main task in my pipeline, i.e. after carla started.

Yes

Thank you for helping!

Here is a part of the cleanup service log. Unfortunately, I cannot even download the full log currently, because the clearml-server will just throw errors for everything.

Yea, but before in my original setup the config file was filled. I just added some lines to the config and now the error is back.

SweetBadger76 I am using the Cleanup Service

Another example on what I would expect:
` ### start_carla.py
def get_task():
task = Task.init(project_name="examples", task_name="start-carla", task_type="application")
# experiment is not run here. The experiment is only run when this is executed as standalone or on a clearml-agent.
return task

def run_experiment(task):
...

This task can also be run as standalone or run by a clearml-agent

if name == "main":
task = get_task()
run_experiment(task)

run_pi...

When I go into the GUI there are no artifacts displayed.

btw: why is agent.package_manager and agent attribute. Imo it does not make sense because conda can install pip packages, but pip cannot install conda packages which can lead to install failures, right?

Agent runs in docker mode. I ran the agent on the same machine as the server this time.

No problem! I profit so much from clearml 🙂

Just tested again. The ordering definitly matters.

Thanks a lot. Will try it!

It is now looking for conflicts.

I do not have a global cuda install on this machine. Everything except for the driver is installed via conda.

fyi: NVIDIA-SMI 460.56 Driver Version: 460.56 CUDA Version: 11.2

Yep, this install PyTorch CPU

Restart did not fix it, but somehow looking at tasks works again.

The package is just subdir by the way. So it should not be in installed packages anyways, right?

test_clearml , so directly from top-level.