each subprocess logs one experiment as task

What do you mean by "tag" / "sub-tags"?

like if u see in above image my project name is abcd18 and under that there are experiments Experiment1, Experiment2 etc.

and then log using logger

This code gives me the graph that I displayed above

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

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

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"

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.

It will not create another 100 tasks, they will all use the main Task. Think of it as they "inherit" it from the main process. If the main process never created a task (i.e. no call to Tasl.init) then they will create their own tasks (i.e. each one will create its own task and you will end up with 100 tasks)

now after 1st iteration is completed then after 5 minutes my script runs automatically and then again it logs into trains server

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.

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

I didn't got it.

No. since you are using Pool. there is no need to call task init again. Just call it once before you create the Pool, then when you want to use it, just do task = Task.current_task()

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

Before this line, call Task.init

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

Like here in the sidebar I am getting three different plots named as loss, train_loss and test_loss

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

then if there are 100 experiments how it will create 100 tasks?

i mean all 100 experiments in one project

@<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.

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 ?

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

so, if I call Task.init() before that line there is no need of calling Task.init() on line number 92

I have one more question?

So you want these two on two different graphs ?

"validate

then if there are 10 experiments then I have to call Task.create() for those 10 experiments