Latest (1.5.1 I believe?), full log incoming, but it's like I've posted elsewhere already 🤔
It just sets up the environment and immediately crashes when trying to run the code.
The setup itself is done correctly.

FWIW It’s also listed in other places @<1523704157695905792:profile|VivaciousBadger56> , e.g. None says:

In order to make sure we also automatically upload the model snapshot (instead of saving its local path), we need to pass a storage location for the model files to be uploaded to.
For example, upload all snapshots to an S3 bucket…

I can only say I’ve found ClearML to be very helpful, even given the documentation issue.
I think they’ve been working on upgrading it for a while, hopefully something new comes out soon.
Maybe @<1523701205467926528:profile|AgitatedDove14> has further info 🙂

Also, creating from functions allows dynamic pipeline creation without requiring the tasks to pre-exist in ClearML, which is IMO the strongest point to make about it

SuccessfulKoala55 it does not

Is there a preferred way to stop the agent?

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?)

Still crashing, I think that may not be the correct virtual environment to edit 🤔
It's the one created later down the line

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

It could be related to ClearML agent or server then. We temporarily upload a given .env file to internal S3 bucket (cache), then switch to remote execution. When the remote execution starts, it first looks for this .env file, downloads it using StorageManager, uses dotenv, and then continues the execution normally

Hmmm maybe 🤔 I thought that was expected behavior from poetry side actually

Thanks David! I appreciate that, it would be very nice to have a consistent pattern in this!

Visibility, same issue as I had 😁

SmugDolphin23 I think you can simply change not (type(deferred_init) == int and deferred_init == 0) to deferred_init is True ?

Or the above;)

My current workaround is to use poetry and tell users to delete poetry.lock if they want their environment copied verbatim

Yes; I tried running it both outside venv and inside a venv. No idea why it uses 2.7?

This seems to be fine for now, if any future lookups finds this thread, btw
with mock.patch('clearml.datasets.dataset.Dataset.create'): ...

AFAICS it's quite trivial implementation at the moment, and would otherwise require parsing the text file to find some references, right?
https://github.com/allegroai/clearml/blob/18c7dc70cefdd4ad739be3799bb3d284883f28b2/clearml/task.py#L1592

Those are cool and very welcome additions (hopefully the additional info in the Info tab will be a link?) 😁

The main issue is the clutter that the forced renaming creates, as shown in the pictures I attached in the other thread.
Why does ClearML hide the dataset task from the main WebUI? Users should have some control over that. If I specified a project for the dataset, I specifically want it there, in that project, not hidden away in some .datasets hidden sub-project. Not...

When I use the APIClient to fetch the tags for the project, I get an empty collection of system tags:

<projects.GetProjectTagsResponse: {
    "tags": [],
    "system_tags": []
}>

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

I... did not, ashamed to admit. The documentation says only boolean values.

We're using self hosted account

We're using the example autoscaler, nothing modified

Yes it would be 🙂
Visualization is always a difficult topic... I'm not sure about that, but a callback would be nice.

One idea that comes to mind (this is of course limited to DataFrames), but think the git diff , where I imagine 3 independent section:
Removed columns (+ truncated preview of removed values) (see below) Added columns (+ truncated preview of removed values)
The middle column is then a bit complicated, but I would see some kind of "shared columns" dataframe, where each ...

IIRC, get_local_copy() downloads a local copy and returns the path to the downloaded file. So you might be interested in e.g.
local_csv = pd.read_csv(a_task.artifacts['train_data'].get_local_copy())
With the models, you're looking for get_weights() . It acts the same as get_local_copy() , so it returns a path.

EDIT: I think also get_local_copy() for a model should work 👍

UPDATE: Apparently the quotation type matters for furl ? I switched the ' to \" and it seems to work now

I tried that, unfortunately it does not help 😞

The instance that took a while to terminate (or has taken a while to disappear from the idle workers)