I am using a venv environment, managed by poetry. Not sure if that helps.

Understand. Here I only see:

KindChimpanzee37 : Ok, will do. (More question from my side though. :-D) But I need to have pretty good idea before presenting our concept to the bosses.

I am using professional version 1.6.4.

I am running it in the Python Console in PyCharm with Task.init. I get with the log:

ClearML Task: overwriting (reusing) task id=dfa2dff538d54c18ad97ea1593cbd357
2023-02-14 13:06:44,336 - clearml.Task - WARNING - Failed auto-detecting task repository: [WinError 123] Die Syntax für den Dateinamen, Verzeichnisnamen oder die Datenträgerbezeichnung ist falsch: '[...]\<input>'
ClearML results page: [None](https://app.clear.ml/projects/9acc061c880344a881790461a4baa837/experiments/dfa2dff538d54c1...

But then I do not see the "installed packages" in "excecution".

My entire code is

from clearml import Task, TaskTypes
task = Task.init(project_name='FirstTrial', task_name='first_trial', task_type=TaskTypes.training)
PACKAGE_VERSION = '0.4.1'
dataset_name = "Demodata"

which I - now - also ran as a whole script.

@<1523701205467926528:profile|AgitatedDove14> : Wait, so, if a task is initialized in process A and I call mark_completed in a process B, which process is terminated? A or B?

"uncommitted changes" and "container" is also empty.

@<1523701205467926528:profile|AgitatedDove14> In the documentation it warns about .close() "Only call Task.close if you are certain the Task is not needed."
What does the documentation refer to? My understanding would be that if close the task within a program, I am not able to use the task object anymore as before and I need to retrieve it via query_tasks to get it again. Is that correct?

@<1523701070390366208:profile|CostlyOstrich36> indeed

>pip show clearml
WARNING: Ignoring invalid distribution -upyterlab (c:\users\...\lib\site-packages)
WARNING: Ignoring invalid distribution -illow (c:\users\...\lib\site-packages)
Name: clearml
Version: 1.6.4
Summary: ClearML - Auto-Magical Experiment Manager, Version Control, and MLOps for AI
Home-page: None
`Auth...

@<1523701087100473344:profile|SuccessfulKoala55> : I referenced this conversation in the issue None

I just see the website that I linked to. I am not sure what is meant by "python environment". I cannot make a screen shot, because I do not know where to look for this in the first place.

Ah... if I run the same script not from PyCharm, but from the terminal, then it gets completed... puh...

As far as I understand, the workflow is like this. I define some model. Then I register it as an OutputModel. Then I train it. During training I save snapshots (not idea how, though) and then I save the final model when training is finished. This way the Model is a) connected to the task and b) available in the model store of ClearML.

Later, in a different task, I can load an already trained model with InputModel. This InputModel is read-only (regarding the ClearML model store), but I can ma...

@<1523701083040387072:profile|UnevenDolphin73> : From which URL is your most recent screenshot?

I mean those, that you see in the screen shot. The difference in code is - at least for me - to write

• parameters_data = {'custom1': 'no', 'custom2': False}; parameters_data = task.connect(parameters_data , name='data')
• task.set_user_properties(custom1='no', custom2=False)
  

@<1523701083040387072:profile|UnevenDolphin73> : I do not get this impression, because during update_weights I get the message

2023-02-21 13:54:49,185 - clearml.model - INFO - No output storage destination defined, registering local model C:\Users..._Demodaten_FF_2023-02-21_13-53-51.624362.model

@<1523701205467926528:profile|AgitatedDove14> Is it true that, when using the "pipeline from tasks" approach, my Python environment in which the pipeline is programmed, does not need to know any of the code with which the tasks have been programmed and still the respective pipeline would be executed just fine?

KindChimpanzee37 : First I went to the dataset and clicked on "Task information ->" in the right bottom corner of the "VERSION INFO". I supposed that is the same as what you meant with "right click on more information"? Because I did not find any option to "right click on more information". The "Task information ->" leads me to a view in the experiment manager. I posted the two screen shots.

PS: It is weird to me that the datamanager leads me to the experiment manager, specifically an experi...

CostlyOstrich36 any ideas?

I expect either 'var1' to be 'b' or - better - there to be log of the change, so that I would be able to see how the value changed over time.

KindChimpanzee37 , any idea 🙂 ?

CostlyOstrich36 sure:
[..]\urbansounds8k\venv\lib\site-packages\torchaudio\backend\utils.py:62: UserWarning: No audio backend is available. warnings.warn("No audio backend is available.") ClearML Task: overwriting (reusing) task id=[..] 2022-09-14 14:40:16,484 - clearml.Task - INFO - No repository found, storing script code instead ClearML results page: `
Traceback (most recent call last):
File "[..]\urbansounds8k\preprocessing.py", line 145, in <module>
datasetbuilder = DataSe...

@<1523701083040387072:profile|UnevenDolphin73> : No, I love it ❤ . Now, I just have to read everything 😄 .

I am not sure if it the fact the name of the file ends with .model is an issue - but that would be somewhat crazy design...

@<1523701083040387072:profile|UnevenDolphin73> , you wrote

Well, I would say then that in the second scenario it’s just rerunning the pipeline, and in the third it’s not running it at all

(I mean, in both the code may have changed, the only difference is that in the second case you’re interested in measuring it, and in the third,
you’re not, so it sounds like a user-specific decision).

Well, I would hope that in the second scenario step A is not rerun. Yes, in the third scen...

@<1523701205467926528:profile|AgitatedDove14> : "does that make sense ?" Not really.

"you do not need to automatically Add/Log/Track things into the Task in the current process." - I do not need to automatically do [...]? You mean I can do it automatically, but alternatively I can do it manually? Do you mean I use close within a process to prevent automatic logging/adding/tracking? But, as far as I know, after I used close I am not able to log etc. manually either. So...

"Mark...

It is documented at None ... super deep in the code. If you don't know that output_uri in TASK's (!) init is relevant, you would never know...