Simplified base logger's API

🚀 Feature

Idea is to simplify current logger’s API by creating a shortcut methods: Currently, to log output parameters and optmizer’s params we do (example is for TB, but is generic for others too):

    logger = TensorboardLogger(...)
    logger.attach(
        trainer,
        log_handler=OutputHandler(tag="training", metric_names="all"),
        event_name=Events.ITERATION_COMPLETED(every=log_every_iters),
    )
    logger.attach(
        trainer,
        log_handler=OptimizerParamsHandler(optimizer, param_name="lr", tag=k),
        event_name=Events.ITERATION_STARTED(every=log_every_iters),
    )

Proposed API:

    logger = TensorboardLogger(...)
    logger.attach_output_handler(
        trainer,
        tag="training", metric_names="all",  # other args, kwargs of OutputHandler
        event_name=Events.ITERATION_COMPLETED(every=log_every_iters),
    )
    logger.attach_opt_params_handler(
        trainer,
        optimizer, param_name="lr", tag=k, # other args, kwargs of OutputHandler
        event_name=Events.ITERATION_STARTED(every=log_every_iters),
    )

In addition, this can fix “bad-practice” current implementation of setup_any_logging taking as input logger and logging_module: https://github.com/pytorch/ignite/blob/d1aa5fd5e71676032c952aa7470a651c216e2dd9/ignite/contrib/engines/common.py#L209

[vfdev-5]

@sdesrozis it’s too hacky for me, such approach.

Let’s implement the methods logger.attach_output_handler and logger.attach_opt_params_handler. Anyone would like to work on it ?

[sdesrozis]

I will do it this week-end if nobody does 😉