yes But i want two graphs with title as train loss and test loss and they should be under main category "loss"

like in the sidebar there should be a title called "loss" and under that two different plots should be there named as "train_loss" and "test_loss"

so I want loss should be my main title and I want two different graphs of train and test loss under that loss

so , it will create a task when i will run it first time

main will initialize parent task and then my multiprocessing occurs which call combined function with parameters as project_name and exp_name

and that function creates Task and log them

If you one each "main" process as a single experiment, just don't call Task.init in the scheduler

I have 100 experiments and I have to log them and update those experiments every 5 minutes

I have one more question?

Hi @<1523701205467926528:profile|AgitatedDove14> , I wanted to ask you something. Is it possible that we can talk over voice somewhere so that I can explain my problem better?

but this gives the results in the same graph

@<1523720500038078464:profile|MotionlessSeagull22> you cannot have two graphs with the same title, the left side panel presents graph titles. That means that you cannot have a title=loss series=train & title=loss series=test on two diff graphs, they will always be displayed on the same graph.
That said, when comparing experiments, all graph pairs (i.e. title+series) will be displayed as a single graph, where the diff series are the experiments.

logger.report_scalar("loss", "train", iteration=0, value=100)
logger.report_scalar("loss", "test", iteration=0, value=200)

its like main title will be loss

Sure @<1523720500038078464:profile|MotionlessSeagull22> DM me 🙂

so what I have done is rather than reading sequentially I am reading those experiments through multiprocessing and for each experiment I am creating new task with specified project_name and task_name

like in the above picture

Can my request be made as new feature so that we can tag same type of graphs under one main tag

Just call the Task.init before you create the subprocess, that's it 🙂 they will all automatically log to the same Task. You can also call the Task.init again from within the subprocess task, it will not create a new experiment but use the main process experiment.

i mean all 100 experiments in one project

@<1523701205467926528:profile|AgitatedDove14> I want to log directly to trains using logger.report_scalar

Just so I understand,
scheduler executes main every 60sec
main spins X sub-processes
Each subprocess needs to report scalars ?

and it should log it into the same task and same project

then my combined function create a sub task using Task.create(task_name=exp_name)

Is this u meant?

And you want all of them to log into the same experiment ? or do you want an experiment per 60sec (i.e. like the scheduler)

You can do:
task = Task.get_task(task_id='uuid_of_experiment')
task.get_logger().report_scalar(...)

Now the only question is who will create the initial Task, so that the others can report to it. Do you have like a "master" process ?

Create one experiment (I guess in the scheduler)
task = Task.init('test', 'one big experiment')
Then make sure the the scheduler creates the "main" process as subprocess, basically the default behavior)
Then the sub process can call Task.init and it will get the scheduler Task (i.e. it will not create a new task). Just make sure they all call Task init with the same task name and the same project name.

Are you using tensorboard or do you want to log directly to trains ?

so, like if validation loss appears then there will be three sub-tags under one main tag loss