Hey IntriguedRat44 ,
Is this what you are after?
https://github.com/allegroai/trains/issues/181

That didn’t gave useful infos, was that docker was not installed in the agent machine x)

JitteryCoyote63 you mean "docker" was not installed and it did not throw an error ?

So essentially, the server helm chart creates randomly generated secret pair and deploys it as a shared k8 secret that pods can access.

This is the tricky part, for the helm chart to be able to create it, it means it can login to the server it means there is a secret embedded in the helm chart that lets you access the default server. you see my point ?

From code ? or the CLI ?
In both cases the dataset needs to upload the parent version somewhere, azure blob supported.

yes that will work 🙂

basically @<1554638166823014400:profile|ExuberantBat24> you can think of hyper-datasets as a "feature-store for unstructured data"

I want to optimizer hyperparameters with trains.automation but: ...

Yes you are correct, in case of the example code, it should be "General/..." if you have ArgParser, it should be "Args/..." Yes it looks like the metric is wrong, it should be "epoch_accuracy" & "epoch_accuracy"

Hi @<1645597514990096384:profile|GrievingFish90>
You mean the agent itself inside a docker then the agent spins sibling dockers for the Tasks ?

Hi @<1610808279263350784:profile|FriendlyShrimp96>

Is there a way to get a list of variants given a metric, or even just a full list of metrics and variants for a given task id?

Try this
None

from clearml.backend_api.session.client import APIClient

c = APIClient()
metrics = c.events.get_task_metrics(tasks=["TASK_ID_HERE"], event_type="training_debug_image")
print(metrics)

I think API ...

You mean from code externally ?

WackyRabbit7 I guess we are discussing this one on a diff thread 🙂 but yes, should totally work, that's the idea

That wasn't scheduled by ClearML).

This means that from Clearml perspective they are "manual" i.e the job it self (by calling Task.init) create the experiment in the system, and fills in all the fields.

But for a k8s job, I'm still unsuccessful.

HelpfulDeer76 When you say "unsuccessful" what exactly do you mean ?
Could it be they are reported to the clearml demo server (the default server if no configuration is found) ?

Hi @<1569858449813016576:profile|JumpyRaven4> could you test the fix? just pull & run

allegroai/clearml-serving-triton:1.3.1
allegroai/clearml-serving-inference:1.3.1

EnviousPanda91 this seems like a specific issue with the clearml-task cli, could that be ?
Can you send a full clearml-task command-line to test ?

ShinyPuppy47 the code that is being launched, does it call task.init?

Are you sure you passed add_task_init_call=True to task create?

Hi AstonishingWorm64
Is this the same ?
https://github.com/allegroai/clearml-serving/issues/1
(I think it was fixed on the later branch, we are releasing 0.3.2 later today with a fix)
Can you try:
pip install git+

I have to admit, I'm not sure...
Let me talk to backend guys, in theory you are correct the "initial secret" can be injected via the helm env var, but I'm not sure how that would work in this specific case

EmbarrassedSpider34

Sync_folder and upload
Several times along the code and then

Do notice they overwrite one another...

Hi DeliciousKoala34
This means the pycharm plugin was not able to run git on your local machine.
Whats your OS ?
could it be that if you open cmd / shell "git" is not in the path ?

You could change infrastructure or hosting, and now your data is associated with the wrong URL

Yeah that makes sense, so have it on a specific dns name? (this is usually the case with k8s deployments)

Yeah that make sense 🙂

Hi LonelyMoth90 , where exactly are you getting the error ? Is it trains-agent running your experiment ?

Can you test with the latest RC:
pip install clearml==1.0.3rc0

Hi CooperativeFox72 ,
From the backend guys, long story short, upgrade your machine => more cpu cores , more processes , it is that easy 🙂

Hi GiganticTurtle0
Sure, OutputModel can be manually connected:
model = OutputModel(task=Task.current_task()) model.update_weights(weights_filename='localfile.pkl')

Also, I just wanted to say thanks for the tool! I'm managing a small data science practice and it's going to be really nice to have a view of all of the experiments we've got and know our GPU utilization, all without having to give every data scientist access to each box where the workflows are run. Incredibly stoked.

♥ ❤ ♥

i’m working on creating a custom config with istio

That is awesome! let me know if we could help 🙂
Also please consider PRing it, I'm sure other users will appreciate the option

That is correct.
Obviously once it is in the system, you can just clone/edit/enqueue it.
Running it once is a mean to populate the trains-server.
Make sense ?

yes, TrickySheep9 use the k8s glue from here:
https://github.com/allegroai/clearml-agent/blob/master/examples/k8s_glue_example.py