Hi SmallDeer34
Hmm I'm not sure you can, the code will by default use rglob with the last part of the path as wildcard selection
😞
You can of course manually create a zip file...
How would you change the interface to support it ?

And the server itself? is it http or https ?

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

can you tell me what the serving example is in terms of the explanation above and what the triton serving engine is,

Great idea!

This line actually creates the control Task (2)
clearml-serving triton --project "serving" --name "serving example"
This line configures the control Task (the idea is that you can do that even when the control Task is already running, but in this case it is still in draft mode).
Notice the actual model serving configuration is already stored on the crea...

So I wonder - why should an agent be related to a specific user's credentials? Is the right way to go about this is to create a "fake user" for the sake of the agent?

Very true you have to have credentials for the trains-agent, so it can "report" to the trains-server, that said, the creator of the Task (i.e. the person who cloned it) will be registered as the "user" in the UI.
I would recommend to create an "agent" user and put it's credentials on the trains-agent machine (the same way...

I see now, give me a minute I'll check

Correct

What if I register the artifact manually?

task.upload_artifact('local folder', artifact_object=' ')This one should be quite quick, it's updating the experiment

With pleasure 🙂

You are doing great 🙂 don't worry about it

Can you send the full log ?

And I think the default is 100 entries, so it should not get cleaned.

and then they are all removed and for a particular task it even happens before my task is done

Is this reproducible ? Who is cleaning it and when?

Will do! 🙂

Amazing! 🎉
Let me know how we can help 🙂

Let me see if I can reproduce something

kubectl get pods -n {namespace} -o=JSONWhat are you getting when running the above on your cluster ?

DisgustedDove53 , TrickySheep9
I'm all for it!
I can think of two options here, (1) use the k8s glue + apply template with ports mode see discussion https://clearml.slack.com/archives/CTK20V944/p1628091020175100
(2) create an interface (queue) to launch arbitrary job on the k8s cluster, with the full pod definition on the Task. This will allow the clearml-session to setup everything from the get go.
How would you interface with the k8s operator, and what exactly will it do?
(BTW: the reas...

p.s. you should remove this line 🙂
extra_index_url: ["git@github.com:salimmj/xxxx"]

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

Hmm let me check I think you are correct here

SweetGiraffe8 Task.init will autolog everything (git/python packages/console etc), for your existing process.
Task.create purely creates a new Task in the system, and lets' you manually fill in all the details on that Task
Make sense ?

NICE! CurvedHedgehog15 cool stuff! and my pleasure 🙂

Yep 😞

Are you asking regrading the k8s integration ?
(This is not a must, you can run the clearml-agent bare-metal on any OS)

The problem is of course filling in all the configuration details, so that they are viewable.
Other than that, check out:
https://allegro.ai/docs/task.html#trains.task.Task.export_task
https://allegro.ai/docs/task.html#trains.task.Task.import_task
Sounds good ?

DilapidatedDucks58 I see ...
This might be more complicated that one would imagine, a simple solution might be to store a snapshot of the values every-time we reach a new maximum, a quick hack might be to add it as text on one of the task's parameters or properties (that we can later add to the table as custom column).
wdyt?

👍

Hi ReassuredTiger98
So let's assume we call:
logger.report_image(title='training', series='sample_1', iteration=1, ...)And we report every iteration (keeping the same title.series names). Then in the UI we could iterate back on the last 100 images (back in time) for this title / series.
We could also report a second image with:
logger.report_image(title='training', series='sample_2', iteration=1, ...)which means that for each one we will have 100 past images to review ( i.e. same ti...

true 😞

is there a built in programmatic way to adjust 

development.default_output_uri

?

How about: In your Task.init(output_uri='...')