But you can manually add them with Task.add_requirements, no?

In my opinion an ugly solution. I would have to keep track of which requirements are missing. Then I would rather just add all requirements manually.

Hey, thank you for answering.
I know this issue and I have it sometimes, but my current issue is a direct result of me trying to make SSL work. So I am not asking for help in solving my problem, but only for help how to debug. Finding out which step leads to the artifact not being deleted (e.g. the fileserver cannot be reached by from wherever the delete request is send)

I think sometimes there can be dependencies that require a newer pip version or something like that. I am not sure though. Why can we even change the pip version in the clearml.conf?

` =============
== PyTorch ==

NVIDIA Release 22.03 (build 33569136)
PyTorch Version 1.12.0a0+2c916ef ... Looking in indexes: ,
Requirement already satisfied: pip in /root/.clearml/venvs-builds/3.8/lib/python3.8/site-packages (22.0.4)
2022-04-07 16:40:57
Looking in indexes: ,
Requirement already satisfied: Cython in /opt/conda/lib/python3.8/site-packages (0.29.28)
Looking in indexes: ,
Requirement already satisfied: numpy==1.22.3 in /opt/conda/...

Okay, this seems to work fine.

What do you mean by prints?

Sure, let me try.

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

One last question then I have everything solved: Is it possible to pass clearml the files to analyze manually? For example my setup consists of a run_this.py and this_should_be_run_A.py and this_should_be_run_B.py . I can then programmatically choose which file to import with importlib. Is there a way to tell clearml programmatically to analyze the files, so it can built up the requirements correctly?

Could be clean log after restart. Unfortunately, I restarted the server right away 😞 I gonna post if it happens again with the appropriate logs.

Thank you very much! 😃

Perfect, works! 🙂

AlertBlackbird30 Thanks for asking. Just take everything with I grain of salt I say, because I am also not sure whether I do machine learning the correct way 😄

I think you got the right idea. I actually do reinforcement learning (RL), so I have multiple RL-environments and RL-agents. However, while the code for the agents differs between the agents, the glue code is the same. So what I do is I call python run_experiment.py --agent http://myproject.agents.my ` _agent --environm...

When I passed specific arguments (for example --steps) it ignored them...

I am not sure what you mean by this. It should not ignore anything.

One more thing: The cuda_version that clearml finds automatically is wrong.

Yep, I will add this as an issue. Btw: Should I rather post the kind of questions I am asking as an issue or do they fit better here?

Python 3.8.8, clearml 1.0.2

Yes

The default behavior mimics Python’s assert statement: validation is on by default, but is disabled if Python is run in optimized mode (via python -O). Validation may be expensive, so you may want to disable it once a model is working.

And in the WebUI I can see arguments similar to the second print statement's.

Yea, and the script ends with clearml.Task - INFO - Waiting to finish uploads

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

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

@<1523701087100473344:profile|SuccessfulKoala55> Only when I delete on self-hosted.
@<1523712723274174464:profile|LazyFish41> WebApp: 1.10.0-357 • Server: 1.10.0-357 • API: 2.24

This has been happening with every version of clearml-server ever. Most probably there should be a queue in front of ES, so it does not process to many request at the same time?

It could be that either the clearml-server has bad behaviour while clean up is ongoing or even after.

Both, actually. So what I personally would find intuitive is something like this:
` class Task:
def load_statedict(self, state_dict):
pass

async def synchronize(self):
    ...

async def task_execute_remotely(self):
    await self.synchronize()
    ...

def add_requirement(self, requirement):
    ...

@classmethod
async def init(task_name):
    task = Task()
    task.load_statedict(await Task.load_or_create(task_name))
    await tas...

So missing args that are not specified are not None like intended, but just do not exists in args . And command is a list instead of a single str.

I think such an option can work, but actually if I had free wishes I would say that the clearml.Task code would need some refactoring (but I am not an experienced software engineer, so I could be totally wrong). It is not clear, what and how Task.init does what it does and the very long method declaration is confusing. I think there should be two ways to initialize tasks:
Specify a lot manually, e.g. ` task = Task.create()
task.add_requirements(from_requirements_files(..))
task.add_entr...

Ah, now I see. This sounds like a good solution.

Or alternatively I just saw that Task.create takes a requirements.txt as an argument. This would also be fine for me, however I am not sure whether I should use Task.create ?