I get an error about incorrect Task ID’s – in the above pseudo code it would be the ID of the step Task that was displayed in the error

But yeah, more generally having a different UI for different data types could be useful (e.g. categorical variables, integers, decimals, etc), just not a direct concern for me at this moment

There are no experiments in the project, let alone the pipeline; they’ve all been archived

And the app is presumably crashed because I can’t click the “Close” button – it’s (the whole page) totally unresponsive and I have to refresh the page, at which point the pipeline still exists (ie was not deleted).

I have left it on the deletion screen (screenshot) for 20-30 mins at one point and it didn’t do anything, so this seems to be a bug

I’m just the messenger here, didn’t set up the web app...

Ah okay, that is a very easy solution to the problem. I wasn’t aware that you could build and run pipelines like that, and I especially wasn’t aware that you could return values from a pipeline and have them accessible to a script in the way that you have given.

Does this require you run the pipeline locally (I see you have set default execution queue) or do any other specific set-up?

I will give it a go tomorrow and report back – the only issue I foresee will be if doing this somehow inc...

The Dataset object itself is not being passed around. The point of showing you that was to say that the Dataset may change and therefore the number of objects (loaded from the Dataset, eg a number of pandas DataFrames that were CSV’s in the dataset) could change

Do notice this will only work for pipelines from Tasks, is this a good fit for you?

The issue with this is that we would then presumably have to run/“build” each of the Tasks (pipeline steps) separately to put them on the ClearML server and then get their Task ID’s in order to even write the code for the Pipeline, which increases the complexity of any automated CI/CD flow. Correct me if I’m wrong.

Essentially, I think the key thing here is we want to be able to build the entire Pipe...

To illustrate, here’s an example repo:

repo/
    package1/
    package2/ # installed separately to package1
    task_script.py # requires package1 and package2 to have been pip installed
    

I used task.flush(wait_for_uploads=True) in the final cell of the notebook

So the DAG is getting confused on bringing the results of the Tasks together

Sorry, I don’t understand how this helps with validating the pipeline run.
Where would the validation code sit?
And the ClearML Pipeline run needs to be available on the ClearML Server (at least as a draft) so that it can be marked as in-production and cloned in the future

Ahh. This is a shame. I really want to use ClearML to efficiently compute features but it’s proving a challenge!
Thanks

The pseudo-code you wrote previously is what would be required, I believe

be able to get the pipeline’s Task ID back at the end

This is the missing piece. We can’t perform validation without this, afaik