and it should log it into the same task and same project
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 will give you one graph titled "loss" with two series: (1) trains (2) loss
Can my request be made as new feature so that we can tag same type of graphs under one main tag
Sure, open a Git Issue :)
Just so I understand,
scheduler executes main every 60sec
main spins X sub-processes
Each subprocess needs to report scalars ?
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()
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()