GrumpyPenguin23 Hi, thanks for your instruction!

Putting some metadata into the model sounds nice.
I was exactly wondering how to take care of labels and being afraid of handling them as a dataset even when inferring.

I think it would be nicer if the CLI had a subcommand to show the content of ~/.clearml_data.json .
In that way, users can be more confident to query the dataset id on which the CLI currently focusing.
My scripts will keep working when the CLI changed how to store the dataset id in the future.

Well, yeah, it would be cleaner if we could go fully python.
But our system is already built and running, and now we are planning to add some training functionality.
The training part can be written in Python but the sample collecting part will be deeply connected to the existing system which is not written in python.
For now using CLI looks much reasonable for that part.

But maybe we should have a cmd line that just outputs the current datasetid, this means it will be easier to grab and pipe

That sounds good.
It definitely helps!

will try it.

Yeah, what I have done is uploaded:
https://github.com/kayhide/PyTorch-YOLOv3/tree/clearml
This is a fork of well-known torch yolo sample and adapted to clearml.

I tried clearml.model.InputModel and successfully downloaded a model.
Is this an expected way to consume a trained model for inference?

Are you talking about the public demo server?

If so, it says:
This server is reset daily at 24:00 PST.

Relating to it but another question.

With that task, which is running under an agent, task.connect_label_enumeration does not look working.

I will run it right away.

For the agent run, I posted only the following params:
name project script typeto tasks.create endpoint and let an agent to pick it.

Um..

and if you clone the local task run and enqueue it to the agent?

It failed.
Saying: Could not read from remote repository.

The log says the repository is:
ssh://github.com/allegroai/clearml

I will set repository url as https and retry.

By the way, we found that when I added labels param and post a tasks.create request, it worked.

The labels are attached for that clone task output model.

Okay, it scceeded.

yes

Can you run this one -

 ?

Do you get the labels for both local and clearml-agent run?

Okay, I did the example.
For the local run, I got the labels.
For the agent run, I did not get the labels.

BTW why using the api calls and not clearml sdk?

Because the training part is only the sub system of our whole system.
And the python stuff is not facing to the web, where training request is coming.

just a sec...

now I am going AFK.
Thanks for your support!

Sounds good, thanks!

I mean, the output model comes with the labels which is posted.

Ah, great!
It worked!

Even though I called task.connect_label_enumeration , there is no data to show on the output model.

Hi AgitatedDove14
Thanks, that is it!

Yeah, I have noticed the --id option.
What I wanted is to automate making dataset from some set of files.
And it requires the dataset id after running clearml-data create ... .
Reading ~/.clearml_data.json looks much better than parsing the command output.

I confirmed that worked if it is not started by an agent.

welcome

No, I have checked it on the web frontend, following the model link and the LABELS tab.