It should have worked....
Can you run the examples from the repo and see if they work?

And the correct region ?

👍

Hmm are you running from inside the Kaggle jupyter thing ?

Hi @<1523715429694967808:profile|ThickCrow29>

I am using the PipelineController with abort_on_failure set to False.

Is this a pipeline from code or from Tasks?
What is the clearml version?
Lastly, if a component fails, and another components is dependent on it's output, how would it run? if it is not dependent, why is it a child component?

Hi OutrageousSheep60

AS-IS

• without compressing or breaking it up into chunks.

So for that I would suggest to manually archive it, and upload as external link?
Or are you saying you want to control the compression used by Dataset class ?
https://github.com/allegroai/clearml/blob/72d9b22e0d27f317a364acfeacbcf5c70f852e8c/clearml/datasets/dataset.py#L603

Okay this is a bit tricky (and come to think about it, we should allow a more direct interface):
pipe.add_step(name='train', parents=['data_pipeline', ], base_task_project='xxx', base_task_name='yyy', task_overrides={'configuration.OmegaConf': dict(value=yaml.dump(MY_NEW_CONFIG), name='OmegaConf', type='OmegaConf YAML')} )Notice that if you had any other configuration on the base task, you should add them as well (basically it overwrites the configurati...

PipelineController works with default image, but it incurs overhead 4-5 min

You can try to spin the "services" queue without docker support, if there is no need for containers it will accelerate the process.

Repository cloning failed: Command '['git', 'fetch', '--all', '--recurse-submodules']' returned non-zero exit status 1.

This error is about failing to clone the pipeline code repo, how is that connected to changing the container ?!
Can you provide the full log?

Wait, how do I reproduce it on community server? Maybe it has something to do with number of columns ? Or whether it is already wider than the screen? What's your browser / OS ?

Hi SkinnyPanda43

Let's say that I install the shared libs with pip in editable mode on my development evironment, how does the clearml-agent will handle those libraries if I submit a job

So installing packages from local folders with "-e" is in general ill-advised.
But using a full git path should work out of the box. for example if you install pip install https://github.com/user/repo/repo.git then the agent will be able to install it on the remote machine. The main challenge...

Or use python:3.9 when starting the agent

This is probably the best solution 🙂

No it will not 😞 the closer is closer to the actual print.
That said, I'm sure it would not be complicated to add.
But I have to wonder, this will really create a mess in the console log, so if someone wants it, it will be global (i.e. also in the visible console. not only in the backend), so the case where the console on the machine itself is "clean" but the backend log is full of debug stuff is not clear to me

Like this one:
https://demoapp.trains.allegro.ai/projects/380af79766304cf78b40e15656013d89/experiments/c41d64546aa54414b7c848a5261e85c7/output/debugImages

. I’m using the default operation mode which uses kubectl run. Should I use templates and specify a service in there to be able to connect to the pods?

Ohh the default "kubectl run" does not support the "ports-mode" 😞

There’s a static number of pod which services are created for…

You got it! 🙂

If this is how the repo links look like, do not set anything in the clearml.conf
It "should" use the ssh for the ssh links, and http for the http links.

Hi DilapidatedDucks58
is this something new ?
usually copy pasting directly from the UI parses everything, no?

This points to the wrong cu117 / driver - could that be?

Hmm interesting, is it a drop in replacement to poetry ?

It's always the details... Is the new Task running inside a new subprocess ?
basically there is a difference between
remote task spawning new tasks (as subprocesses, or as jobs on remote machine), remote task still running remote task, is being replaced by a spawned task (same process?!)UnevenDolphin73 am I missing a 3rd option? which of these is your case?
p,s. I have a suspicion that there might be a misuse of "Task" here?! What are you considering a Task? (from clearml perspective a Task...

Can you test with the hydra example? if the example works, any chance you can send a toy to reproduce it ?
https://github.com/allegroai/clearml/tree/master/examples/frameworks/hydra

Hi StrangePelican34 , you mean poetry as package manager of the agent? The venvs cache will only work for pip and conda, poetry handles everything internally:(

Yes, though the main caveat is the data is not really immutable 😞

Give me a minute

I think the main difference is that I can see a value of having access to the raw format within the cloud vendor and not only have it as an archive

I see it does make sense.
Two options, one, as you mentioned use the ClearML StorageManager to upload the files, then register them as external links with Dataset.
Two, I know the enterprise tier has HyperDatasets, that are essentially what you describe, with version control over the "metadata" and "raw storage" on the GCP, including the ab...

I'm still unclear on why cloning the repo in use happens automatically for the pipeline task and not for component tasks.

I think in the pipeline it was the original default, but it turns out for a lot of users this was not their defualt use case ...
Anyhow you can also pass repo="." which will load + detect the repo in the execution environemtn and automatically fill it in

I thought about the fact that maybe we need to write everything in one place

It will be in the same place, under the main Task
Should work out of the box

👍

TrickyRaccoon92 Thanks you so much! 😊

This is a Sagemaker notebook instances

Yes I think this is the issue

creating a dataset with parents worked very well and produced great visuals on the UI!

woot woot!

I tried the squash solution, however this somehow caused a download of all the datasets into my

so this actually works, kind or like git squash, bottom line it will repackage the data from all the different versions into one new version. This means downloading the data from all squashed versions, then repackaging it into a single new version. Make sense ?