GrittyStarfish67 : In terms of "has a good name" you literally mean the name or do you mean, they have a good reputation 😄 ?

How would you compare those to ClearML?

AgitatedDove14 : Not sure: They also have the feature store (data management), as mentioned, which is pretty MLOps-y 🙂 . Also, they do have workflows ( https://docs.mlrun.org/en/latest/concepts/multi-stage-workflows.html ) and artifacts/model management ( https://docs.mlrun.org/en/latest/store/artifacts.html ) and serving ( https://docs.mlrun.org/en/latest/serving/serving-graph.html ).

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

I have... its included in None

@<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?

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...

Understand. Here I only see:

Maybe this is only logged after it is not "running" anymore, but I am not sure how to "complete" a task programmatically.

@<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...

@<1523701205467926528:profile|AgitatedDove14>

@<1523701070390366208:profile|CostlyOstrich36> : After more playing around, it seems that ClearML Server does not store the models or artifacts itself. These are stored somewhere else (e.g., AWS S3-bucket) or on my local machine and ClearML Server is only storing configuration parameters and previews (e.g., when the artifact is a pandas dataframe). Is that right? Is there a way to save the models completely on the ClearML server?

@<1523701070390366208:profile|CostlyOstrich36> : Thanks, where can I find more information on ClearML's model repository. I hardly find any in the documentation.

Also, that leaves the question open, what Model is for. I described how I understand, the workflow should look like, but my question remains open...

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

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

@<1523701070390366208:profile|CostlyOstrich36> , I am build a PoC, evaluating if we should use ClearML for our entire ML team and go Scale or Enterprise pricing. For that I need to know all/most capabilities and concepts of ClearML to see if ClearML is future-proof.

TL;DR: difficult to narrow it down, but we (amongst other things), we need a model store

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

Ah, wait, when I run the entire script, I do see the respective information in "execution".

I see that with task.mark_completed(), I am able to programmatically complete the task.

But still, in the web app the task is considered to be still "running". I am not sure what to do, so that the task is considered to be "completed".

Secondly, I do not understand this:

None says

Manually mark a Task as completed. This will close the running process and will change the Task’s status to Completed (Use this function to close and change status of remotely executed tasks). To simply change the Task’s status to completed, use task.close()

None says

Closes the current Task and cha...

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.

Ok, I checked: A is terminated. This is not what I thought would happen and not what I intended with my documentation. I should clarify that.