Hi @<1523709807092043776:profile|GrittyKangaroo27>

some of my completed datasets,

This only has an effect on the dataset when it is being uploaded, if completed it is there for logging purposes only. What is exactly the use case? (just to be verify, once a Task/Dataset is completed you cannot edit it)

can configuration objects refer to one-another internally in ClearML?

Interesting, please explain?

Working on it as we speak 🙂 probably a day worst case 2. This is quite strange and we are not sure where is the fault, as nothing in the code itself changed...

GrotesqueDog77 this should just work, decorate the functions with @PipelineDecorator.component and call the functions one after the other
paths = step_one() step_two(paths)ClearML will make sure it serializes the strings and pass them to step two (of course step two should actually run on a machine with access to the same folder, but this is another issue 🙂 )

Hi @<1603198134261911552:profile|ColossalReindeer77>

Hello! does anyone know how to do

HPO

when your parameters are in a

Hydra

Basically hydra parameters are overridden with "Hydra/param"
(this is equivalent to the "override" option of hydra in CLI)

SoreDragonfly16 . In the hyper parameters Tab, you have "parallel coordinates" (next to the "add experiment" the button saying "values" press on it and there should be " parallel coordinates")
Is that it?

👍

ElegantCoyote26
parser = get_parser() args_ = vars(parser.parse_args()) task.connect(args_)There is no need to connect args_ Task.init will automatically catch the argparser.

So far my local and remote gitlab repositories are synchronized, I suspect, that 

Failed applying git diff, see diff above

 error is caused by cached repository from which clearml tries to run the process. I've cleaned the cache, but it haven't helped.

Hmm can you test with empty "uncommitted changes" ?
Just making sure when you say still does n't work, you are not trying to run the Task with the git diff that includes teh binary data right?

Yes, but where I can fi...

I think you are correct 😞 Let me make sure we add that (docstring and documentation)

looks like at the end of the day we removed 

proxy_set_header Host $host;

  and use the fqdn for the proxy_pass line

And did that solve the issue?

I do not think this is the upload timeout, it makes no sense to me for GCP package (we do not pass any timeout, it's their internal default for the argument) to include a 60sec timeout for upload...
I'm also not sure where is the origin of the timeout (I'm assuming the initial GCP handshake connection could not actually timeout, as the response should be relatively quick, so 60sec is more than enough)

. Could you clarify the question for me, please?
...
Could you please point me to the piece of ClearML code related to the downloading process?

I think I mean this part:
https://github.com/allegroai/clearml/blob/e3547cd89770c6d73f92d9a05696018957c3fd62/clearml/datasets/dataset.py#L2134

Sounds good to me 🙂

SlipperyDove40 Yes there is
TRAINS_CONFIG_FILEhttps://allegro.ai/docs/faq/faq/#trains-configuration

Good news a dedicated class for exactly that will be out in a few days 🙂
Basically task scheduler and task trigger scheduler, running as a service cloning/launching tasks either based on time (cron alike) or based on a trigger).
wdyt?

LOL totally 🙂

@<1562610699555835904:profile|VirtuousHedgehong97>

source_url="s3:...", 

This means your data is already on S3 bucket, it will not "upload" it it will just register it.
If you want to upload files, then they should be local and then when you call upload you can specify the target S3 bucket, and the data will be stored in a unique folder in the bucket
Does that make sense ?

DilapidatedDucks58 I'm assuming clearml-server 1.7 ?
I think both are fixed in 1.8 (due to be released wither next week, or the one after)

Yes it should
here is fastai example, just in case 🙂
https://github.com/allegroai/clearml/blob/master/examples/frameworks/fastai/fastai_with_tensorboard_example.py

Also, how do pipelines compare here?

Pipelines are a type of Task, so like Tasks you can clone and enqueue them, or set them as the target of the trigger.

the most flexible solution would be to have some way of triggering the execution of a script in the parent task environment,

This is the exact idea of the TriggerScheduler None
What am I missing here?

Hi IrritableJellyfish76
If you are running a code that uses clearml from kubeflow, you have out of the box integration between the two, what am I missing?

Yes
Are you trying to upload_artifact to a Task that is already completed ?

TrickyRaccoon92 Thanks you so much! 😊

The only weird thing to me is not getting any "connection warnings" if this is indeed a network issue ...

Why does ClearML hide the dataset task from the main WebUI?

Basically you have the details from the Dataset page, why should it be mixed with the others ?

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.

This maybe a request for "Dataset" tab under project, why would you need the Dataset Task itself is the main question?

Not all dataset objects are equal, and perhap...

It said the command --aux-config got invalid input

This seems like an interface bug.. let me see if we can fix that 🙂

BTW: this seems like a triton LSTM configuration issue, we might want to move the discussion to the Triton server issue, wdyt?

Definitely!

Could you start an issue https://github.com/triton-inference-server/server/issues , and I'll jump join the conversation?

. Is there any reference about integrating kafka data streaming directly to clearml-serving...

ElegantCoyote26 It means we need to have a keras logger that logs everything to trains, then we need to hook it automatically.
Do you feel like PR-ing the logger (the hooking I can take care of 🙂 )?

I'll make sure they get back to you

DefeatedMoth52 how many agents do you have running on the same GPU ?