ClearML does

Thanks for doing that ! :i_love_you_hand_sign:

issue created

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

And this is when I compare two tasks

I assume it is reported into TB, right ?

That is what I was hoping at first

Thanks GloriousPanda26 !

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

It's a running number because PL is creating the same TB file for every run

but despite the naming it's working quite well actually

Right, I think the naming is a by-product of Hydra / TB

I think it would make sense to have one task per run to make the comparison on hyper-parameters easier

I agree. Could you maybe open a GitHub issue on it, I want to make sure we solve this issue 🙂

yes

GloriousPanda26 Are you getting multiple Tasks or is it a single Task ?

GloriousPanda26 wouldn't it make more sense that multi run would create multiple experiments ?

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

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

So the naming is a by product of the many TB created (one per experiment), if you add different naming ot the TB files, then this is what you'll be seeing in the UI. Make sense ?

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

I meant the omegaconf

I see

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

the import order does is not related to the problem

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

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

ClearML does

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 ?

but I have no idea what's behing 

1

, 

2

  and 

3

 compare to the first execution

This is why I would think multiple experiments, since it will store all the arguments (and I think these arguments are somehow being lost.
wdyt?

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