Filter ignite stacktrace

Should we consider filtering out the frames that come from ignite? Whenever there's a bug in my code I have to go through a lot of frames to find where ignite trace ends, and my code starts.

I think it is possible to do in Python.

E.g. I'd like to turn

/Mine/ml-mine/code.../lm/train.py in main(train, dev, nn_type, ninp, nhid, nlayers, dropout, dropouth, dropouti, dropoute, wdrop, tie_weights, bsz, bptt, max_epochs, lr, clip, adaptive, cutoffs, device, to_device, ckpt_embedding, lock_emb, kernel_size, num_levels, seed)
    238 
    239     logger.debug(f'Invoking trainer.run for {max_epochs} epochs')
--> 240     return trainer.run(data=trn_dset, max_epochs=max_epochs)

.../ignite/ignite/engine/engine.py in run(self, data, max_epochs)
    264         except BaseException as e:
    265             self._logger.error("Engine run is terminating due to exception: %s", str(e))
--> 266             self._handle_exception(e)
    267 
    268         return self.state

.../ignite/ignite/engine/engine.py in _handle_exception(self, e)
    222     def _handle_exception(self, e):
    223         if Events.EXCEPTION_RAISED in self._event_handlers:
--> 224             self._fire_event(Events.EXCEPTION_RAISED, e)
    225         else:
    226             raise e

.../ignite/ignite/engine/engine.py in _fire_event(self, event_name, *event_args)
    182             self._logger.debug("firing handlers for event %s ", event_name)
    183             for func, args, kwargs in self._event_handlers[event_name]:
--> 184                 func(self, *(event_args + args), **kwargs)
    185 
    186     def terminate(self):

.../lm/train.py in handle_exception(engine, e)
    178 
    179         else:
--> 180             raise e
    181 
    182     @validator.process_function()

.../ignite/ignite/engine/engine.py in run(self, data, max_epochs)
    251                 self.state.epoch += 1
    252                 self._fire_event(Events.EPOCH_STARTED)
--> 253                 hours, mins, secs = self._run_once_on_dataset()
    254                 self._logger.info("Epoch[%s] Complete. Time taken: %02d:%02d:%02d", self.state.epoch, hours, mins, secs)
    255                 if self.should_terminate:

.../ignite/ignite/engine/engine.py in _run_once_on_dataset(self)
    213         except BaseException as e:
    214             self._logger.error("Current run is terminating due to exception: %s", str(e))
--> 215             self._handle_exception(e)
    216 
    217         time_taken = time.time() - start_time

.../ignite/ignite/engine/engine.py in _handle_exception(self, e)
    222     def _handle_exception(self, e):
    223         if Events.EXCEPTION_RAISED in self._event_handlers:
--> 224             self._fire_event(Events.EXCEPTION_RAISED, e)
    225         else:
    226             raise e

.../ignite/ignite/engine/engine.py in _fire_event(self, event_name, *event_args)
    182             self._logger.debug("firing handlers for event %s ", event_name)
    183             for func, args, kwargs in self._event_handlers[event_name]:
--> 184                 func(self, *(event_args + args), **kwargs)
    185 
    186     def terminate(self):

.../lm/train.py in handle_exception(engine, e)
    178 
    179         else:
--> 180             raise e
    181 
    182     @validator.process_function()

.../ignite/ignite/engine/engine.py in _run_once_on_dataset(self)
    205                 self.state.iteration += 1
    206                 self._fire_event(Events.ITERATION_STARTED)
--> 207                 self.state.output = self._process_function(self, batch)
    208                 self._fire_event(Events.ITERATION_COMPLETED)
    209                 if self.should_terminate or self.should_terminate_single_epoch:

.../lm/train.py in step(engine, batch)
    107         output  = output.view(-1, output.size(2))
    108 
--> 109         loss = loss_fn(output, y)
    110         loss.backward()
    111 

into

/Mine/ml-mine/code.../lm/train.py in main(train, dev, nn_type, ninp, nhid, nlayers, dropout, dropouth, dropouti, dropoute, wdrop, tie_weights, bsz, bptt, max_epochs, lr, clip, adaptive, cutoffs, device, to_device, ckpt_embedding, lock_emb, kernel_size, num_levels, seed)
    238 
    239     logger.debug(f'Invoking trainer.run for {max_epochs} epochs')
--> 240     return trainer.run(data=trn_dset, max_epochs=max_epochs)

<PYTORCH-IGNITE STACKTRACE SKIPPED. USE `ignite.set_stacktrace(True)`>

.../lm/train.py in step(engine, batch)
    107         output  = output.view(-1, output.size(2))
    108 
--> 109         loss = loss_fn(output, y)
    110         loss.backward()
    111 

Start by tracing the Python exception flow in ignite/engine/engine.py, especially run, _run_once_on_dataset, _handle_exception, and _fire_event. Use the example stacktrace to determine the desired filtering boundary, and verify that Ignite frames can be omitted while ignite.set_stacktrace(True) preserves the full trace.