WackyRabbit7 How do I reproduce it ?

Yey!

see here the docker_setup_bash_script argument
None
It will be executed (no need for the #!/bin/bash btw) before starting to setup the env inside the container, so apt-get and the like can be executed if needed. Notice that if this is something that Always needs to be executed, you can put the same list of commands here: [None](https://github.com/allegroai/clearml-agen...

My question is what happens if I launch in parallel multiple doit commands that create new Tasks.

Should work out of the box.

I would like to confirm that current_task ...

Correct.

If you passed the correct path it should work (if it fails it would have failed right at the beginning).
BTW: I think it is clearml-agent --config-file <file here> daemon ...

Hi ShortElephant92

This isn't an issue if the user is using a Service Account JSON Key,

Are you saying that when you are using GS python sdk directly it works?

For context, the google cloud storage SDK allows an authorized user credentials.

ClearML actually uses the google python SDK, the JSON is just a way to pass the credentials to the google SDK, I'm not sure it points to "service account"? where did that requirement came from ?
is it from here ` Service account info was n...

okay, let me know if it works

I am creating this user

Please explain, I think this is the culprit ...

GrievingTurkey78 are you able to reproduce it?

Yes , both work :(

(no objection to add an argument but, I just wonder what's the value)

So I think there are two bugs here?
--args overrides="key=value" does not work request: add --hydra to override hydra arguments (and if this is added the first one is not needed)Is that correct?

I'm hoping we are ready to release

You mean why you have two processes ?

TenseOstrich47 notice:
task.logger.report_matplotlib_figure( title=f"Performance Heatmap - {name}", series="Device Brand Predictions", iteration=0, figure=figure, **report_image=True,** )report_image=True means it will be uploaded as an image not a plot (like imshow), the default is False , which would put it under Plots section

Code you add a few prints, and see where it hangs ? there's no reason for it to hang (even the plot upload is done ...

Still figuring out, what is the best orchestration tool,which can run this end-2-end.

DeliciousBluewhale87 / PleasantGiraffe85 based on the scenario above what is the missing step that you need to cover? Is it the UI presenting the entire workflow? Or maybe the a start trigger that can be configured ?

I want in my CI tests to reproduce a run in an agent

you mean to run it on the CI machine ?

because the env changes and some things break in agents and not locally

That should not happen, no? Maybe there is a bug that needs fixing on clearml-agent ?

"This is Not a an S3 endpoint... what is the files server you configured for it?

Hi GreasyPenguin14
Sure you can, although a bit convoluted (I'll make sure we have a nice interface 🙂 )
import hashlib title = hashlib.md5('epoch_accuracy_title'.encode('utf-8')).hexdigest() series = hashlib.md5('epoch_accuracy_series'.encode('utf-8')).hexdigest() task_filter = { 'page_size': 2, 'page': 0, 'order_by': ['last_metrics.{}.{}'.format(title, series)] } queried_tasks = Task.get_tasks(project_name='examples', task_filter=task_filter)

Hi @<1643060801088524288:profile|HarebrainedOstrich43>
I think I understand what's going on, in order for the pipeline logic to be "aware" of the pipeline component, it needs to be declared in the pipeline logic script file (or scope if you will).
Try to import from src.testagentcomponent import step_one also in the global pipeline script (not just inside the function)

ComfortableShark77 it seems the clearml-serving is trying to Upload data to a different server (not download the model)
I'm assuming this has to do with the CLEARML_FILES_HOST, and missing credentials. It has nothing to do with downloading the model (that as you posted, will be from the s3 bucket).
Does that make sense ?

Would this be best if it were executed in the Triton execution environment?

It seems the issue is unrelated to the Triton ...

Could I use the 

clearml-agent build

  command and the 

Triton serving engine

 task ID to create a docker container that I could then use interactively to run these tests?

Yep, that should do it 🙂
I would start simple, no need to get the docker itself it seems like clearml credentials issue?!

Hi MotionlessCoral18
You can set all mount points here:
https://github.com/allegroai/clearml-agent/blob/6e31171d314a6e9b276c36d45314025783956b00/docs/clearml.conf#L241

Hmm can you try:
--args overrides="['log.clearml=True','train.epochs=200','clearml.save=True']"

JitteryCoyote63

I agree that its name is not search-engine friendly,

LOL 😄
It was an internal joke the guys decided to call it "trains" cause you know it trains...
It was unstoppable, we should probably do a line of merch with AI 🚆 😉
Anyhow, this one definitely backfired...

but instead, they cannot be run if the files they produce, were not committed.

The thing with git, if you have new files and you did not add them, they will not appear in the git diff, hence missing when running from the agent. Does that sound like your case?

why are there indefinitely growing anonymous tasks, even after i've closed the main schedulers.

The anonymous Tasks are The Dataset you are creating (a Dataset version is also a Task of a certain type with artifacts, the idea is usually Datasets are created from code, hence the need to combine the two).
Make sense ?

so moving b in to a won’t work if some subfolders are already there

I though that if they are already there you would merge / overwrite, isn't that what you need ?
a/b/c/2.txt seems like the result of moving b from dataset B into folder b in Dataset A, what am I missing?
(My assumption is that you have both datasets locally on the same machine and that you can just copy the files from b of Datasset B into b folder of Dataset A)

Bottom line the driver version in the host machine does not support the CUDA version you have in the docker container

Hi ScantChimpanzee51
btw: this seems like an S3 internal error
https://github.com/boto/s3transfer/issues/197