Think I will have to fork and play around with it 

NICE! (BTW: if you manage to get it working I'll be more than happy to help push the PR)
Maybe the quickest win is to store just the .py as model ?

Well I guess you can say this is definitely not self explanatory line 😉
but, it is actually asking whether we should extract the code, think of it as:
if extract_archive and cached_file: return cls._extract_to_cache(cached_file, name)

Thanks MortifiedDove27 ! Let me see if I can reproduce it, if I understand the difference, it's the Task.init in a nested function, is that it?
BTW what's the hydra version? Python, and OS?

just to check. Does the k8s glue install torch by default?

SubstantialElk6 what do you mean the glue installs torch ?
The glue will take a Task from the queue create a k8s job (basically use the same docker and inside the docker run get the agent to execute the requested Task). Where would the "torch" come into play?

Hi GiganticTurtle0
ClearML will only list the directly imported packaged (not their requirements), meaning in your case it will only list "tf_funcs" (which you imported).
But I do not think there is a package named "tf_funcs" right ?

Oh yes, you probably have sorting or filter applies there :)

Hi UnevenDolphin73

I cannot initialize a task before loading the file, but the docs for

connect_configuration

Yes, that's basically the problem. you have to decide where is the main driver.
If you are executing the code "manually" (i.e. not via the agent) then there is no problem, obviously you have the local file and you can use it to load the "project name" etc, then you just call Task.connect_configuration to log the content.
If you are running the same code via the agent...

HealthyStarfish45 this sounds very cool! How can I help?

I'm glad to hear 🙂
If you can reproduce it, let me know

Thank you!

Hi FrothyShark37
Can you verify with the latest version?
pip install -U clearml

So now for it to take place you need to enqueue the Task and set an agent to pick it up and run it.
When the agent is running the Task the new parameter will be passed.
does that make sense ?

Okay that might explain the issue...
MysteriousBee56 so what you are saying is
python3 -m trains-agent --help does NOT work
but trains-agent --help does work?

SubstantialElk6 I just executed it , and everything seems okay on my machine.
Could you pull the latest clearml-agent from the github and try again ?

EDIT:
just try to run:
git clone cd clearml-agent python examples/k8s_glue_example.py

maybe this can cause the issue?

Not likely.
In the original pipeline (the one executed from the Pycharm) do you see the "Pipeline" section under Configuration -> "Config objects" in the UI?

Hi FloppyDeer99
Since this thread is a bit old, I might have missed something 🙂
Are we saying the links are not working in the UI ?
(notice the links themselves are generated by the clearml package, so if there was a bug, still not sure here, then old links will remain invalid until manually fixed) Can you verify that the latest clearml generates working links?

But from the log it seems that:
you are not running as root in the docker? Python3.8 is installed (and not python 3.6 as before)

But I do not know how it can help me:(

In your code itself after the Task.init call add:
task.set_initial_iteration(0)See reply here:
https://github.com/allegroai/clearml/issues/496#issuecomment-980037382

So the only difference is how I log in into machine to start clear-ml

the only different that I can think of is the OS Environments in the two login types:
can you run export in the two cases and check the diff between them?
export

Hi DefeatedCrab47
You mean by trains-agent, or accumulated over all experiences ?

And command is a list instead of a single str

"command list", you mean the command argument ?

Hi @<1523711619815706624:profile|StrangePelican34>
Hmm, I think this is missing from the docs, let me ping the guys about that 🙏

Also could you explain the difference between trigger.start() and trigger.start_remotely()

Start will start the trigger process (the one "watching the changes") locally (this makes sense for debugging etc.)
start_remotely will launch the trigger process on the "services" where it should live forever 🙂

Okay so when I add trigger_on_tags, the repetition issue is resolved.

Nice!

This problem occurs when I'm scheduling a task. Copies of the task keep being put on the queue ...

Hmm make sense, then I would call the export_task once (kind of the easiest to get the entire Task object description pre-filled for you) with that, you can just create as many as needed by calling import_task.
Would that help?

MortifiedDove27
Nice!!!

There is a version coming out next week, the one after it (probably 2/3 weeks later) will have this feature

Hi SubstantialElk6

Generically, we would 'export' the preprocessing steps, setup an inference server, and then pipe data through the above to get results. How should we achieve this with ClearML?

We are working on integrating the OpenVino serving and Nvidia Triton serving engiones, into ClearML (they will be both available soon)

Automated retraining

In cases of data drift, retraining of models would be necessary. Generically, we pass newly labelled data to fine...

I’ll check if I could wrap the code in something that calls the Task.delete if debugging

Whatever you think works best for you, I was genuinely curious 🙂
To me (personally) it is helpful to have a log even while debugging (comparing to previous runs etc, trying to see what went wrong even on a console output level). When I'm done I just search for everything I worked on select all, and archive them. Then a cleanup service in the background clears all the archived Tasks once they ar...

VexedCat68 are you manually creating the OutputModel object?

yes they do 🙂