I also tried switching to dockerized mode now, getting the same issue 🤔

It failed on some missing files in my remote_execution, but otherwise seems fine now

I opened a GH issue shortly after posting here. @<1523701312477204480:profile|FrothyDog40> replied (hoping I tagged the right person).

We need to close the task. This is part of our unittests for a framework built on top of ClearML, so every test creates and closes a task.

Yes exactly that AgitatedDove14
Testing our logic maps correctly, etc for everything related to ClearML

• in the second scenario, I might have not changed the results of the step, but my refactoring changed the speed considerably and this is something I measure.
• in the third scenario, I might have not changed the results of the step and my refactoring just cleaned the code, but besides that, nothing substantially was changed. Thus I do not want a rerun.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 ...

Sorry AgitatedDove14 , forgot to get back to this.
I've been trying to convince my team to drop poetry 😄

It does not 🙂
We started discussing it here - https://clearml.slack.com/archives/CTK20V944/p1640955599257500?thread_ts=1640867211.238900&cid=CTK20V944
You suggested this solution - https://clearml.slack.com/archives/CTK20V944/p1640973263261400?thread_ts=1640867211.238900&cid=CTK20V944
And I eventually found this solution to work - https://clearml.slack.com/archives/CTK20V944/p1641034236266500?thread_ts=1640867211.238900&cid=CTK20V944

Any sneak preview? 😉 😁

packages an entire folder as zip

What if I have multiple files that are not in the same folder? (That is the current use-case)

It otherwise makes sense I think 🙂
Our workaround now for using a Dataset as we do, is to store the dataset ID as a configuration parameter, so it's always included too 😉

Well, -ish. Ideally what we're after is one of the following:
Couple a task with a dataset. Keep it visible in it's destined location. Create a dataset separately from the task. Have control over its visibility and location. If it's hidden, it should not affect normal UI interaction (most annoying is having to click twice on the same project name when there are hidden datasets, which do not appear in the project view)

I'm not sure what you mean by "entity", but honestly anything work. We're already monkey-patching our way 😄

That gives us the benefit of creating "local datasets" (confined to the scope of the project, do not appear in Datasets tabs, but appear as normal tasks within the project)

Yeah I figured (2) would be the way to go actually 😄

Local changes are applied before installing requirements, right?

Tada! https://github.com/allegroai/clearml-agent/issues/90

I’ll also post this on the main channel -->

A follow up question (instead of opening a new thread), is there a way I could signal some files/directories to be copied to the execute_remotely task?

I'm using 1.1.6 (upgraded from 1.1.6rc0) - should I try 1.1.7rc0 or smth?

Ah I see, if the pipeline controller begins in a Task it does not add the tags to it…

Still failing with 1.2.0rc3 😞 AgitatedDove14 any thoughts on your end?

Not necessarily on the same branch, no

Yeah I managed to work around those former two, mostly by using Task.create instead of Task.init . It's actually the whole bunch of daemons running in the background that takes a long time, not the zipping.

Regarding the second - I'm not doing anything per se. I'm running in offline mode and I'm trying to create a dataset, and this is the error I get...
There is a data object it, but there is no script object attached to it (presumably again because of pytest?)

Those are for specific packages, I'm wondering about the package managers as a whole

Also something we are very much interested in (including the logger-based scatter plots etc)

In which repo?:)

Here's how it failed for us 😅
poetry stores git related data in poetry.lock , so when you pip list , you get an internal package we have with its version, but no git reference, i.e. internal_module==1.2.3 instead of internal_module @ git+https://....@commit .
Then pip actually fails (our internal module is not on pypi), but poetry suceeds

Okay this was a deep dive into clearml-agent code 😁
Took a long time to figure out that there was a specific Python version with a specific virtualenv that was old (Python 3.6.9 and Python 3.8 had latest virtualenv, but Python 3.7.5 had an old virtualenv).
Then the task requested to use Python 3.7, and that old virtualenv version was broken.

As a result -> Could the agent maybe also output the virtualenv version used with setting up the environment for the first time?

I think I may have brought this up multiple times in different ways :D
When dealing with long and complicated configurations (whether config objects, yaml, or otherwise), it's often useful to break them down into relevant chunks (think hydra, maybe).

In our case, we have a custom YAML instruction !include , i.e.
` # foo.yaml
bar: baz

bar.yaml

obj: !include foo.yaml
maybe_another_obj: !include foo.yaml `

Say I upload each of these yamls as a configuration object (as with the above). Once I try to load bar.yaml remotely it will crash, since foo.yaml is missing (and is instead a clearml configuration object).
Does that make sense?

Yes, exactly! I've added instructions for the users on creating their account and running clearml-init , and then they run the snippet that updates the api and sdk sections.

Or did you mean I can couple a short "mini config" with the package and redirect clearml to use this local one (instead of the one at ~/clearml.conf)?