We are working hard on release 1.7 once that is out we will push an RC for review (I hope) 🙂

Hi @<1576381444509405184:profile|ManiacalLizard2>
Yeah that should work, assuming credentials are set in your clearml.conf

Hi MistakenDragonfly51
Notice that Models are their own entity, you can query them based on tags/projects/names etc.
Querying and getting Models is done by Model class:
https://clear.ml/docs/latest/docs/references/sdk/model_model#modelquery_models

task.get_models()

is always empty. (edited)

How come there are no Models on the Task? (in other words how come this is empty?)

UnevenDolphin73 are you positive, is this reproducible? What are you getting?

My model files are also there, just placed in some usual non-shared linux directory.

So this is the issue, How would the container Get to these models? you either need to mount the folder to the container,
or you push them to ClearML model repo with the OutputModel class , does that make sense ?

Yep, that would do it ...
You can disable it with:
Task.init(..., auto_connect_frameworks={'scikit': False})

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

which was trained on jupyter notebook.

Hmm that might be the issue, it assumes a local script running, let me verify that

NICE!

Hmm I just noticed:

'--rm', '', 'bash'

This is odd this is an extra argument passed as "empty text" how did that end up there? could it be you did not provide any docker image or default docker container?

is it possible to change an existing model's URL?

Edit the DBs ... That's basically the only way 😞

It works. However, still, it sometimes takes a strangely long time for the agent to pick up the next task (or process it), even if it is only "Hello World".

The agent check every 2/5 seconds if there is a new Task to be launched, could that be it?

First let's verify the conf:

from clearml.config import config_obj
import json
print(json.dumps(config_obj.get("sdk"), indent=2))

what are you getting

I have a model and hundreds of thousands of inference records for that model.

What would be the query ? Are you reporting 100+ diff scalars ?

Also, I did also accidentally create multiple services via

clearml-serving create --name <> --project <>

, and cannot get rid of them.

find them in the UI (you can go to All Projects, then in the search bar put their UIs) and archive / delete them

So the part that is confusing to me is: None
who / how is this Task running? did you also setup a "service" queue (as part of the clear...

Correct 🙂

Thanks @<1523701601770934272:profile|GiganticMole91> !
(As usual MS decided to invent a new "standard")
I'll make sure the guys looks at it and get an RC with a fix

Oh sure that makes sense, clone the experiment in the UI (right click, clone) then everything is editable :) both uncommitted changes, and branch / commit

Yes MuddySquid7 it is automatically detects it (regardless of you uploading DF as an artifact).
How are you saving the dataframe ?
(it will auto log any joblib.save call, is that it?)

Hi @<1671689437261598720:profile|FranticWhale40>
You mean the download just fails on the remote serving node becuause it takes too long to download the model?
(basically not a serving issue per-se but a download issue)

Thanks Martin, so does it mean I won’t be able to see the data hosted on S3 bucket in ClearMl dashboard under datasets tab after registering it?

Sure you can, let's assume you have everything in your local /mnt/my/data you can just add this folder with add_files then upload to your S3 bucket with upload(output_uri=" None ",...)
make sense ?

but now serving is not able to locate the model file itself,

from your screen shot the file seems to be in local folder somewhere "file://" it should be in the file server or in object storage, how did it get there? how is the file server configured

Hi SubstantialElk6
Could you test with the latest RC6 ?
pip install clearml==0.17.5rc6

Out of curiosity, if Task flush worked, when did you get the error, at the end of the process ?

I see if this is the case, use this example:
None
It should cover the entire thing, no?
None

👍

This means that in your "Installed packages" you should see the line:
Notice that this is not a pypi artifactory (i.e. a server to add to the extra index url for pip), this is a direct pip install from a git repository, hence it should be listed in the "installed packages".
If this is the way the package was installed locally, you should have had this line in the installed packages.
The clearml agent should take care of the authentication for you (specifically here, it should do nothing).
If ...

DefiantCrab67
Where will you copy it from ?

SmugOx94 could you please open a GitHub issue with this request, otherwise we might forget 🙂
We might also get some feedback from other users

I'm not sure about the intended use of

connect_configuration

now.

Basically here is the rationale behind it:
I have a config file that I want to log on the Task, and I Also want to be able to change this configuration file externally when launching using an agent (i.e. edit the content) I have a nested dictionary that I do not want to flatten and push as hyper-parameters because it is not very readble, so I want to store it in a more human readable form and edit it a...