Nice!

script, and the kwcoco not imported directly (but from within another package).

fyi: usually the assumption is that clearml will only list the directly imported packages, as these will pull the respective required packages when the agent will be installing them ... (meaning that if in the repository you are never actually directly importing kwcoco, it will not be listed (the package that you do import directly, the you mentioned is importing kwcoco, will be listed). I hope this ...

That sounds like success!

Also can you right click on the image and save it on your machine, see if it is cropped, or it is just a UI issue

SmugOx94

after having installed 

numpy==1.16

 in the first case or 

numpy==1.19

 in the second case. Is it correct?

Correct

the reason is simply that I'd like to setup an MLOps system where

I see the rational here (obviously one would have to maintain their requirements.txt)
The current way trains-agent works is that if there is a list of "installed packages" it will use it, and if it is empty it will default to the requirements.txt
We cou...

https://github.com/allegroai/clearml/issues/199
Seems already supported for a while now ...

Assuming Tensorflow (which would be an entire folder)
local_folder_or_files = mode.get_weights_package()

Hi @<1610083503607648256:profile|DiminutiveToad80>
This depends on how you configure the agents in your clearm.conf

You can do https if user/pass are configured, and you can force SSH and it will auto-mount your host SSH folder into the container and use it.
None
[None](https://github.com/allegroai/clearml-agent/blob/0254279ed5987fbc69cebae245efaea33aec1ff2/docs/cl...

Awesome, thank you!!

Awesome! let me know how/if it works 🙂

Hmm @<1523701083040387072:profile|UnevenDolphin73> I think this is the reason, None
and this means that even without a full lock file poetry can still build an environment

Okay, now I'm lost, is this reproducible ? are you saying Dataset with remote links to S3 does not work?
Did you provide credntials to your S3 (in tour clear.conf) ?

I would do something like:

` from clearml import Logger

def forward(...):
    self.iteration += 1 
    weights = self.compute_weights(...)
    m = (weights * (target-preds)).mean()
    Logger.current_logger().report_scalar(title="debug", series="mean_weight", value=m, iteration=self.iteration)
    return m `

Hmmm, let me check

The new parameter 

abort_on_failed_steps

 could be a list containing the name of the

I like that, we can also have it as an argument per step (i.e. the decorator can say, abort_pipeline_on_fail or continue_pipeline_processing)

Sure @<1523720500038078464:profile|MotionlessSeagull22> DM me 🙂

It does work about 50% of the times

EcstaticGoat95 what do you mean by "work about 50%" ? do you mean the other 50% it hangs ?

WickedGoat98 give me a minute, I'm not sure it is not ClearML related

And no scalars ?

Sure, venv mode

BTW: if you want to sync between artifacts / settings, I would recommend calling task.reload() to get the latest values back from the server.

SoreDragonfly16 could you test with Task.init using reuse_last_task_id=False for example:
task = Task.init('project', 'experiment', reuse_last_task_id=False)The only thing that I can think of is running two experiments with the same project/name on the same machine, this will ensure every time you run the code, you create a new experiment.

Hi SoreDragonfly16
The warning you mention means that someone state of the experiment was changed to aborted , which in term will actually kill the process.
What do you mean by "If I disable the logger," ?

SoreDragonfly16 could you reproduce the issue?
What's your OS? trains versions?

SoreDragonfly16 the torchvision warning has nothing to do with the Trains warning.
The Trains warning means that somehow someone changes the state of the Task from running (in_progress) to "stopped" (aborted). Could it be one of the subprocesses raised an exception ?

SoreDragonfly16 notice that if in the web UI you aborting a task it will do exactly what you described, print a message and quit the process. Any chance someone did that?

My main issue with this approach is that it breaks the workflow into “a-sync” set of tasks:

This is kind of the way you depicted it, meaning, there is an an initial dataset, "offline process" (i.e. external labeling) then, ingest process.

I was wondering if the “waiting” operator can actually be a part of the pipeline.
This way it will look more clear what is the workflow we are executing.

Hmm, so pipeline is "aborted", then the trigger relaunches the pipeline, and the pipeli...

That is a good question ... let me check 🙂

EnviousPanda91 so which frame works are being missed? Is it a request to support new framework or are you saying there is a bug somewhere?

The odd thing it was able to authenticate but then it could not find the Task to delete.
Could it be someone already deleted the Task ?
(BTW: a new version of the cleanup service is in the working 🙂 )

I'll make sure we fix it