Could you elaborate on that:
"So the agent failed to actually restore it from the git (files that are not added are not considered part of the git diff, this is usually git behavior)."

Or I am using this method wrong.

I am still trying to solve the add_requirements + importlib combo. If I use detect_with_freeze I can not use add_requirements and if I use automatic code analysis it will not find all packages because of importlib .
For now I come to the conclusion, that keeping a requirements.txt and making clearml parse the requirements from there should be the most robust solution. Unfortunately, there seems to be no way to do this with Task.init .

What I get for args when I print it locally is not the same as what ClearML extracts from args .

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

CharmingPuppy6 These threads may also be interesting for you: https://clearml.slack.com/archives/CTK20V944/p1614867532303700 https://clearml.slack.com/archives/CTK20V944/p1617963053397600

@<1576381444509405184:profile|ManiacalLizard2> Thank you, but afaik this only works locally and not if you run your task on a clearml-agent!

Yes

I mean, could my hard drive not become full at some point? Can clearml-agent currently detect this?

Can you explain what you meant by entropy point file? In a new git repository my code works fine.

Nvm, I took a look at conda history and there I see it

I guess it started with the usage of the cleanup_service.

To answer my own question: In the WebUI where one inputs the credentials, use https for the host instead of the auto-added http

I usually also experience no problems with restarting the clearml-server. It seems like it has to do with the OOM (or whatever issue I have).

Thanks, I will look into it. For me the weird thing is that saving works and only deletion fails somehow.

The one I posted on top 22.03-py3 😄

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.

I see a python 3 fileserver.py running on a single thread with 100% load.

Unfortunately, I do not know that. Must be before October 2021 at least. I know I asked here how to use the preinstalled version and AgitatedDove14 helped me to get it work. But I cannot find the old thread 😕

Also I can see that clearml correctly loads the config
STORAGE S3BucketConfig(bucket='clearml', host='myhost:9000', key='mykey' secret='mysecret', token='', multipart=False, acl='', secure=True, region=None, verify=True, use_credentials_chain=False)

Is this working in the latest version? clearml-agent falls back to /usr/bin/python3.8 no matter how I configure clearml.conf Just want to make sure, so I can investigate what's wrong with my machine if it is working for you.

Good idea. No, clearml-agent does not crash and works fine afterwards. Then it is probably some other problem with my machine. Thank you!

I created an github issue because the problem with the slow deletion still exists. https://github.com/allegroai/clearml/issues/586#issue-1142916619

When I change the owner and the group of the files to root it works.

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

Here is how my start_carla .py task looks like currently:

` import os
import subprocess
from time import sleep

from clearml import Task
from clearml.config import running_remotely

def create_task(node):
task = Task.create(
project_name="examples",
task_name="start-carla",
repo="myrepo",
branch="carla-clearml-integration",
script="src/start_carla_task.py",
working_directory="src",
packages=["clearml"],
add_task_init_call=...

Nvm. I think I understood. When the file has never been added to repository it is not tracked.

Perfect, works! 🙂

Thank you for answering. So your suggestion would be similar to VexedCat68 's first idea, right?

The agent is run with pip. However, the docker image uses conda (because NVIDIA uses conda to build PyTorch most probably). My theory is that when the task is run the first time on an agent, Task.init will update the requirements. Then when ran a second time, the task will contain the requirements of the (conda-) environment from the first run.