In AzureML, start_logging will start asynchronous execution or synchronous execution?

Question

It was written in the Microsoft AzureML documentation, "A run represents a single trial of an experiment. Runs are used to monitor the asynchronous execution of a trial" and A Run object is also created when you submit or start_logging with the Experiment class."

Related to start_logging, as far as I know, when we have simply started the run by executing this start logging method. We have to stop, or complete by complete method when the run is completed. This is because start_logging is a synchronized way of creating an experiment. However, Run object created from start_logging is to monitor the asynchronous execution of a trial.

Can anyone clarify whether start_logging will start asynchronous execution or synchronous execution?

Answer 1

@Mo Zein start_logging will create a run object similar to experiment.submit() but in the case of start_logging you can create an interactive logging session and create an interactive run in the specified experiment and in either case, the logging methods on the returned run object work the same.

For example,

run = exp.start_logging() #Will give you the context of run object directly  
run_id = run.id # access the run id for use later  

Run = experiment.submit()   
run = Run.get_context() #To access the current run context of your code  

I think this notebook explains the usage of logging better in this context.

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