Hi RipeGoose2

when I'm using the set_credentials approach does it mean the trains.conf is redundant? if

Yes this means there is no need for trains.conf , all the important stuff (i.e. server + credentials you provide from code).
BTW: When you execute the same code (i.e. code with set_credentials call) the agent's coniguration will override what you have there, so you will be able to run the Task later either on prem/cloud without needing to change the code itself 🙂

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

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 came across it before but thought its only relevant for credentials

We are working on improving the docs, hopefully it will get clearer 😉

I'll try to go with this option, I think its actually perfect for my needs

Great!

thanks!

I'll try to go with this option, I think its actually perfect for my needs

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

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..

I was trying to do exactly as you mentioned setting the environment variable 

before

 any trains import but it didn't work

In your entry point script, (even if you do not call trains/ Task.init ) add:
import os os.environ['TRAINS_CONFIG_FILE']='~/my_new_trains.conf' import trainsThen when you actually import trains, everything is already set and it will not read the configuration again.
Make sense ?

RipeGoose2 https://github.com/allegroai/trains/blob/f27aed767cb3aa3ea83d8f273e48460dd79a90df/trains/task.py#L2072

The problem is, the configuration is loaded at import time, so there is no "time" to pass anything other than environment variable.
That said if the only difference is server config you can use
Task.set_credentials

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..

Programmatically before , importing the package, set os.environ['TRAINS_CONFIG_FILE']='~/my_new_trains.conf'
BTW: What's the use case for doing so?

thanks for helping again

My pleasure :)

thanks for helping again 🙂

Hi AgitatedDove14 , path to the config file for trains manual execution

Hi RipeGoose2

when creating a task the default path is still there

What do you mean by "PATH" do you want to provide path for the config file? is it for trains manual execution or the agent ?