Nice SoreHorse95 !
BTW: you can edit the entire omegaconf yaml externally with set/get configuration object (name = OmegaConf) , do notice you will need to change Hydra/allow_omegaconf_edit to true

@<1587253076522176512:profile|HollowPeacock33>
Is this a commercial ad? this seems like out of scope for this channel
Can you expand?

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

CheekyFox58 what do you have in the plots Tab?

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

BroadSeaturtle49 btw: RC with a fix should be out after the weekend

But I am considreing just failing the task.

This will of course work, just raise exception in the Task itself, and protect the call from the pipeline logic function with try/except

regrading the second option, try to nullify the hash on the Component Task:

# running the Task component here
# if we do not want someone to use us
Task.current_task()._set_runtime_properties({"pipeline_job_hash": None})

This would be a good example?
https://github.com/allegroai/clearml/blob/master/examples/services/monitoring/slack_alerts.py

Hi CleanWhale17 let me see if I can address them all

Email Alert for finished Job(I'm not sure if it's already there).

Slack integration will be public by the end of the weekend 🙂
It is fully customization / extendable, I'll be happy to help .

DVC

Full dataset tracking is supported using the artifacts and the ability to integrate to any central storage (shared folders/ S3 / GS / Azure etc.)
From my experience, it is easier to work with artifacts from Data-Processing Tasks...

Hi @<1533982060639686656:profile|AdorableSeaurchin58>
Notice the scalars and console are stored on the elasticsearch DB, this is usually under
/opt/clearml/data/elastic_7

PricklyJellyfish35 yes that's kind of what I was thinking 🙂
I still wonder if we should configure it or just have both.
Could I ask you to open a GitHub issue on this feature request, I'd love to get some input on what would make more sense to implement. Regardless it is not a major change and should be very quick to implement

ShaggyHare67 in the HPO the learning should be (based on the above):
General/training_config/optimizer_params/Adam/learning_rate
Notice the "General" prefix (notice it is case sensitive)

GleamingGrasshopper63 can you ping to your api server ?
!ping api.server.hereAlso what's the api server you configured ? (ip:8008 ?)
Any chance this is a Local machine, i.e. the colab machine cannot get back into the clearml server cunning locally ?

so I guess this could be one reason to start about thinking upgrading ....

Wait you mean the clearml-server ? (there is no reason not to upgrade the python package)

As I'm a Full-stack developer at Core. I'd be looking to extend the TRAINS Frontend and Backend APIs to suit my need of On-Prem data storage integration and lots of other customization for Job Scheduler(CRON)/Dataset Augmentation/Custom Annot. tool etc.

That is awesome! Feel free to post a specific question here, and I'll try to direct to the right place 🙂

Can you guide me to one such tutorial that's teaching how to customize the backend/front end with an example?

You mean l...

How come the second one is one line?

Where do you have your Task.init ?

@<1651395720067944448:profile|GiddyHedgehong81> just to be clear, Dataset.get_local_copy returns a path to your files,
You have to Manually add the additional path to the specific files you need to use. It does Not know that in advance.
That was the initial issue you had, and I assume it is the same one here. does that make sense ?

I see in the UI are 5 drafts

What's the status of these 5 experiments? draft ?

ShaggyHare67 I'm just making sure I understand the setup:
First "manual" run of the base experiment. It creates an experiment in the system, you see all the hyper parameters under General section. trains-agent running on a machine HPO example is executed with the above HP as optimization paamateres HPO creates clones of the original experiment, with different configurations (verified in the UI) trains-agent executes said experiments, aand they are not completed.But it seems the paramete...

Hi ShallowArcticwolf27

Does the 

clearml-task

 cli command currently support remote repositories with that are intended to be used with ssh

It does 🙂

but the 

git@

 prefix used for gitlab's ssh it seems to default to looking for the repository locally

git@ is always the prefix for SSH repositories (it does not actually mean it uses it, it's what git will return when asked on the origin of the repository. The agent knows (if SSH credentials ...

Hi LazyLeopard18
I remember someone deploying , specifically on the AZURE k8s (can't remember now how they call it).
What is exactly the feedback you are after?

Hi @<1543766544847212544:profile|SorePelican79>
You want the pipeline configuration itself, not the pipeline component, correct?

pipeline = Task.get_task(Task.current_task().parent)
conf_text = pipeline.get_configuration_object(name="config name")

conf_dict = pipeline.get_configuration_object_as_dict(name="config name")

(currently I think the implementation expects that if the download completed, it was successful)

👍

MelancholyElk85 can you provide some logs on the pip install fails ?

Is this reproducible? I tried to run the same example code on my machine, and it started training ...
Do you have issues with other pytorch examples? Could you try simple reporting example:
https://github.com/allegroai/clearml/blob/master/examples/reporting/scalar_reporting.py

MelancholyElk85 what do you have under "Installed Packages" for this specific Task ?

SmarmyDolphin68 , All looks okay to me...
Could you verify you still get the plot on debug samples as image with the latest trains RC
pip install trains==0.16.4rc0

So I have a task that just loads a model, but I don't see it as an artifact in the UI

You should see it under Artifacts, Input model if you are calling Keras load function (or similar)