no i want all of them in the same experiment

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

"validate

and then log using logger

what changes should I make here?

This code gives me the graph that I displayed above

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?

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

yes

I have one more question?

I didn't got it.

each subprocess logs one experiment as task

I have to create a main task for example named as main

okay, Thanks @<1523701205467926528:profile|AgitatedDove14> for the help.

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 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

its like main title will be loss

can we delete the models and then upload it again?

but this gives the results in the same graph

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

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

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

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

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

i mean all 100 experiments in one project

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()

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

First I set no_proxy to 127.0.0.1

Then I did set CURL_CA_BUNDLE to empty string