This solve this issue. However it does not interpolate values

This seems to be working:
t.connect_configuration(OmegaConf.to_container(conf, resolve=True))

Casting the configuration into a dict does not solve the problem as clearml does not capture the nested aspect of the configuration object. This is how it looks on your example:

sorry for the delay. ClearML capture the command line arguments but they are hydra parameters (mulitrun, config_dir, config_name, config_path, etc). I append and override some hyper parameters of the model but they are all stored as a string under "overrides".

This is unfortunate as OmegaConf behaves like dict

I see

but I have no idea what's behing 1 , 2 and 3 compare to the first execution

but despite the naming it's working quite well actually

on one experiment it overlays the same metrics (not taking into account the run number)

And this is when I compare two tasks

I meant the omegaconf

it's a single taks which contains metrics for all 4 executions

ClearML does

Thanks for doing that ! :i_love_you_hand_sign:

yes

Below is an example with one metric reported using multirun. This is taken from a single experiment result page as all runs feed the same experiment. Unfortunately I have no idea what 1 refers to for example. Is it possible to name each run or to break them into several experiments ?

https://github.com/allegroai/clearml/issues/306

the import order does is not related to the problem

but when I compare experiments the run numbers are taken into account comparing "1:loss" with "1:loss" and putting "2:loss"s in a different graph

That is what I was hoping at first

I am not really familiar with TB internal mechanics. For this project we are using Pytorch Lightning

between Hydra, PL, TB and clearml I am not quite sure who is adding the prefix for each run

issue created

yes. As you can see this one has the hydra section reported in the config

the previous image was from the dashboard of one experiment

but to go back to your question, I think it would make sense to have one task per run to make the comparison on hyper-parameters easier