i mean all 100 experiments in one project

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

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)

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

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 ?

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

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

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

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

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

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

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()

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

def combined(path,exp_name,project_name):
temp = Task.create(task_name="exp_name")

logger = temp.current_logger()
logger.report_scalar()

def main():
task=Task.init(project_name="test")
[pool.apply_async(combined, args = (row['Path'], row['exp_name'], row['project_name'])) for index,row in temp_df.iterrows()]

scheduler = BlockingScheduler()
scheduler.add_job(main, 'interval', seconds=60, max_instances=3)
scheduler.start()

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"

You can always click on the name of the series and remove it for display.
Why would you need three graphs?

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

its like main title will be loss

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

Sure

yes

and that function creates Task and log them

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

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

This code gives me the graph that I displayed above

what changes should I make here?