I basically go to the model from the experiment first, then when in the model, I'm trying to download it but can't. I've screenshotted the situation.

In your ~/clearml.conf you can specify the following to force the model to upload with the following setting:
sdk.development.default_output_uri

The server is on a different machine. I'm experimenting on the same machine though.

For anyone facing a similar issue to mine and wanting the model to uploaded just like data is uploaded,

in the Task.init, set the output_uri = True.

This basically makes it use the default file server for clearml that you define in the clearml.conf file. Ty.

After the previous code, I got the model uploaded by the previous code using its ID. Now when I add tags here, they were visible in the UI

VexedCat68 , it looks like it is being saved locally. Are you running all from the same machine?

Thank you, I found the solution to my issue, when I started reading at default output uri.

How are you saving the model?

Since I want to save the model to the clearml server? What should the port be alongside the url?

And in that case, if I do, model.save('test'), it will also save the model to the clearml server?

CostlyOstrich36 I'm observing some weird behavior. Before when I added tags to the model before publishing it, it worked fine and I could see the tags in the UI.

Now when I do it this way, tags aren't set. If I then run another code which gets the model, using ID, and then set tags, it worked fine. Let me share the codes.

Can you access the model in the UI and see the uri there?

The tags I add here aren't added.

tensorflow model.save, it says the model locally in saved model format.

Basically saving a model on the client machine and publishing it, then trying to download it from the server.

Basically want the model to be uploaded to the server alongside the experiment results.