I didn't got it.

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

i mean all 100 experiments in one project

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.

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

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

my scheduler will be running every 60 seconds and calling main function

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

I will share my script u can see it what I am doing

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

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?

and under that there will be three graphs with title as train test and loss

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

So you want these two on two different graphs ?

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)

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

Before this line, call Task.init

no i want all of them in the same experiment

each subprocess logs one experiment as task

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

but this gives the results in the same graph

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()