no, I’m providing the id of task which generated the model as a “hyperparam”

also, I don’t see an edit button near input models

to know why exactly ssh tunneling fails

I think we need logging here: https://github.com/allegroai/clearml-session/blob/bf1851cd3831c19cc0eadd9b2ffc0613f97f16e1/clearml_session/main.py#L564

that’s a pity =(

clearml==1.5.0
WebApp: 1.5.0-192 Server: 1.5.0-192 API: 2.18

and the problem is with system_tags=[“dataset”]

I tried both, didn’t work

For others, who haven’t heard about ngrok:
Ngrok exposes local servers behind NATs and firewalls to the public internet over secure tunnels.

well, I first run clearml-session to start everything on the remote machine, then I close the local process (while Interactive is still running on the remote machine)

but I don’t get to this line, because my task is already of type data_processing

I guess so, lemme double-check

and this can break a lot of things, when somebody start the scheduler with an older version of clearml, saves the state, then upgrades and new clearml expects the state in another format

I tried this, but didn’t help:
input_models = current_task.models["input"] if len(input_models) == 1: input_model_as_input = {"name": input_models[0].name, "type": ModelTypeEnum.input} response = current_task.send(DeleteModelsRequest( task=current_task.task_id, models=[input_model_as_input] ))

https://github.com/allegroai/clearml-session/issues/1

yes, I’ll try it out

yeah, I think I’ll go with schedule_function right now, but your proposed idea would make it even clearer.

like replace a model in staging seldon with this model from clearml; push this model to prod seldon, but in shadow mode

I think I’ll skip with PR: there is a related problem, that makes the fix (and especially its testing much more difficult): https://github.com/allegroai/clearml/issues/648#issuecomment-1102595620

gotcha, thanks!

I am not registering a model explicitly in apply_model . I guess it is done automatically when I do this:
output_models = train_task_with_model.models["output"] model_descriptor = output_models[0] model_filename = model_descriptor.get_local_copy()

Then I ssh into the remote machine using ngrok hostname and tunnel the port for Jupyter

` Remote machine is ready
Setting up connection to remote session
Starting SSH tunnel
Warning: Permanently added '<CENSORED>' (ECDSA) to the list of known hosts.
Enter passphrase for key '/Users/jevgenimartjushev/.ssh/id_rsa': <CENSORED>

SSH tunneling failed, retrying in 3 seconds `

Did a small update: added a workaround and renamed the issue to include more client_facing condition
limit_execution_time is presentinstead of an implementation detail condition
timeout_jobs are present

I can try, but difficult to verify correctness without a publicly available test suite

ideally, I want to hardcode, e.g. use_staging = True, enqueue it; and then via clone-edit_user_properties-enqueue in UI start the second instance

slightly related follow-up question: can I add user properties to a scheduler configuration?

not sure I fully get it. Where will the connection between task and scheduler appear?

so I assume it’s somehow related to remote connection form VS Code

we are just entering the research phase for a centralized serving solution. Main reasons against clearml-serving triton are: 1) no support for kafka 2)no support for shadow deployments (both of these are supported by Seldon, which is currently the best=looking option for us)