and latest pre release hydra

I have the latest clearml version fresh from PyPi

"does not support running with no server connection." this is what I was afraid of..I'll need to figure out if I can use trains at all 😞

yes I will be happy to, its gonna be my first time

thanks SuccessfulKoala55 , the question arose after trying to follow the instructions you attached. it seems that installing a docker on windows 10 Home is somewhat problematic

I think the latter. the specific use-case I'm talking about is running experiments on one machine, and using a local server on another machine to read the "logs" \ artifacts

by WebApp you mean the public online one? I might be confusing stuff

by communication that the artifacts are streamed from the machine running the experiments to the local server?
can it be done "offline" after the experiments run view them in my local server?

I refer to all the info that accessible through the webApp

yes, I have limited access to the machine that is running the experiment. I can't setup a server there. but I want to collect the results and view them later

if I don't have internet connection on the other machine, can I just copy the artifacts and transfer them to my local machine?

?

and I will also be happy to see if I can contribute maybe to this specific feature or maybe others

TypeError: 'bool' object is not callable

AgitatedDove14 it is happening on an offline network, would be tricky to set it up we will try. so far the errors we observed were either:
Calling upload callback when starting upload: maximum recursion depth exceeded
Or
something like pending for upload (might be because we archived a run while it was uploading)

it worked!

thanks!

AgitatedDove14 , I want multiple machines to access the synced state of the optimizer. which is part of the internals of the optimizer... and then report the results back to the optimizer such that the study object of the optimizer keeps track of the results and the next sample will be aware of all previous studies

So I can avoid running unnecessary common heavy setup, for a light weight experiment

to put it a bit differently, I am looking for a way to manually sample and report from and to the optimizer

Hi AgitatedDove14 the thing I had in mind is having access to trains logger exclusive features like the https://allegro.ai/docs/logger.html#trains.logger.Logger.report_plotly and .report_table for example.. It can be done by explicitly getting the trains default logger, but I was wondered if there is some kind of combined interface to capture properties of both in one object especially because I came across the deprecated TrainsLogger

yes that's what I meant.. this is good, thanks

Thanks! I'll have a look and see if I have some useful ideas

AgitatedDove14 the option you mentioned just before sounds much better for me, I must admit I find the name of the method confusing. I came across it before but thought its only relevant for credentials

thanks for helping again 🙂

awesome!

AgitatedDove14 The use case is conditional choice of a server config, when ran locally or on the cloud..
I was trying to do exactly as you mentioned setting the environment variable before any trains import but it didn't work (and also its a mess in terms of my code).. I was hoping there is another way to go about it.. if not I'll try to create a minimal reproducible example..

AgitatedDove14 it does, and it did, but for some reason I couldn't make it to work this way..
I require some additional imports before to infer the config path dynamically.. but even when I stripped down the code and made sure there is no other trains imports anywhere it still didn't work..

hi AgitatedDove14 , when I'm using the set_credentials approach does it mean the trains.conf is redundant? if the file doesn't exists on the machine, will it be an issue? if not, so what defaults should I assume for the rest of the values?

I didn't get to test it on the cloud yet and trying to make final adjustments