TypeError: cannot pickle '_thread.RLock' object

[wzyloveywh]

When I run the" voice_activity_detection.ipynb file" at the jupyter notebook.
The last piece of code in the "Training "module reported an error: TypeError: cannot pickle '_thread.RLock' object

How should I solve this problem?

TypeError Traceback (most recent call last)
Cell In[9], line 3
1 import pytorch_lightning as pl
2 trainer = pl.Trainer(devices=1, accelerator="gpu", max_epochs=2)
----> 3 trainer.fit(model)

File D:\Anaconda3\lib\site-packages\pytorch_lightning\trainer\trainer.py:770, in Trainer.fit(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)
751 r"""
752 Runs the full optimization routine.
753
(...)
767 datamodule: An instance of :class:~pytorch_lightning.core.datamodule.LightningDataModule.
768 """
769 self.strategy.model = model
--> 770 self._call_and_handle_interrupt(
771 self._fit_impl, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path
772 )

File D:\Anaconda3\lib\site-packages\pytorch_lightning\trainer\trainer.py:723, in Trainer._call_and_handle_interrupt(self, trainer_fn, *args, **kwargs)
721 return self.strategy.launcher.launch(trainer_fn, *args, trainer=self, **kwargs)
722 else:
--> 723 return trainer_fn(*args, **kwargs)
724 # TODO: treat KeyboardInterrupt as BaseException (delete the code below) in v1.7
725 except KeyboardInterrupt as exception:

File D:\Anaconda3\lib\site-packages\pytorch_lightning\trainer\trainer.py:811, in Trainer._fit_impl(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)
807 ckpt_path = ckpt_path or self.resume_from_checkpoint
808 self._ckpt_path = self.__set_ckpt_path(
809 ckpt_path, model_provided=True, model_connected=self.lightning_module is not None
810 )
--> 811 results = self._run(model, ckpt_path=self.ckpt_path)
813 assert self.state.stopped
814 self.training = False

File D:\Anaconda3\lib\site-packages\pytorch_lightning\trainer\trainer.py:1236, in Trainer._run(self, model, ckpt_path)
1232 self._checkpoint_connector.restore_training_state()
1234 self._checkpoint_connector.resume_end()
-> 1236 results = self._run_stage()
1238 log.detail(f"{self.class.name}: trainer tearing down")
1239 self._teardown()

File D:\Anaconda3\lib\site-packages\pytorch_lightning\trainer\trainer.py:1323, in Trainer._run_stage(self)
1321 if self.predicting:
1322 return self._run_predict()
-> 1323 return self._run_train()

File D:\Anaconda3\lib\site-packages\pytorch_lightning\trainer\trainer.py:1345, in Trainer._run_train(self)
1342 self._pre_training_routine()
1344 with isolate_rng():
-> 1345 self._run_sanity_check()
1347 # enable train mode
1348 self.model.train()

File D:\Anaconda3\lib\site-packages\pytorch_lightning\trainer\trainer.py:1413, in Trainer._run_sanity_check(self)
1411 # run eval step
1412 with torch.no_grad():
-> 1413 val_loop.run()
1415 self._call_callback_hooks("on_sanity_check_end")
1417 # reset logger connector

File D:\Anaconda3\lib\site-packages\pytorch_lightning\loops\base.py:204, in Loop.run(self, *args, **kwargs)
202 try:
203 self.on_advance_start(*args, **kwargs)
--> 204 self.advance(*args, **kwargs)
205 self.on_advance_end()
206 self._restarting = False

File D:\Anaconda3\lib\site-packages\pytorch_lightning\loops\dataloader\evaluation_loop.py:155, in EvaluationLoop.advance(self, *args, **kwargs)
153 if self.num_dataloaders > 1:
154 kwargs["dataloader_idx"] = dataloader_idx
--> 155 dl_outputs = self.epoch_loop.run(self._data_fetcher, dl_max_batches, kwargs)
157 # store batch level output per dataloader
158 self._outputs.append(dl_outputs)

File D:\Anaconda3\lib\site-packages\pytorch_lightning\loops\base.py:199, in Loop.run(self, *args, **kwargs)
195 return self.on_skip()
197 self.reset()
--> 199 self.on_run_start(*args, **kwargs)
201 while not self.done:
202 try:

File D:\Anaconda3\lib\site-packages\pytorch_lightning\loops\epoch\evaluation_epoch_loop.py:88, in EvaluationEpochLoop.on_run_start(self, data_fetcher, dl_max_batches, kwargs)
86 self._reload_dataloader_state_dict(data_fetcher)
87 # creates the iterator inside the fetcher but returns self
---> 88 self._data_fetcher = iter(data_fetcher)
89 # add the previous fetched value to properly track is_last_batch with no prefetching
90 data_fetcher.fetched += self.batch_progress.current.ready

File D:\Anaconda3\lib\site-packages\pytorch_lightning\utilities\fetching.py:178, in AbstractDataFetcher.iter(self)
176 def iter(self) -> "AbstractDataFetcher":
177 self.reset()
--> 178 self.dataloader_iter = iter(self.dataloader)
179 self._apply_patch()
180 self.prefetching()

File D:\Anaconda3\lib\site-packages\torch\utils\data\dataloader.py:438, in DataLoader.iter(self)
436 return self._iterator
437 else:
--> 438 return self._get_iterator()

File D:\Anaconda3\lib\site-packages\torch\utils\data\dataloader.py:384, in DataLoader._get_iterator(self)
382 else:
383 self.check_worker_number_rationality()
--> 384 return _MultiProcessingDataLoaderIter(self)

File D:\Anaconda3\lib\site-packages\torch\utils\data\dataloader.py:1048, in _MultiProcessingDataLoaderIter.init(self, loader)
1041 w.daemon = True
1042 # NB: Process.start() actually take some time as it needs to
1043 # start a process and pass the arguments over via a pipe.
1044 # Therefore, we only add a worker to self._workers list after
1045 # it started, so that we do not call .join() if program dies
1046 # before it starts, and del tries to join but will get:
1047 # AssertionError: can only join a started process.
-> 1048 w.start()
1049 self._index_queues.append(index_queue)
1050 self._workers.append(w)

File D:\Anaconda3\lib\multiprocessing\process.py:121, in BaseProcess.start(self)
118 assert not _current_process._config.get('daemon'),
119 'daemonic processes are not allowed to have children'
120 _cleanup()
--> 121 self._popen = self._Popen(self)
122 self._sentinel = self._popen.sentinel
123 # Avoid a refcycle if the target function holds an indirect
124 # reference to the process object (see bpo-30775)

File D:\Anaconda3\lib\multiprocessing\context.py:224, in Process._Popen(process_obj)
222 @staticmethod
223 def _Popen(process_obj):
--> 224 return _default_context.get_context().Process._Popen(process_obj)

File D:\Anaconda3\lib\multiprocessing\context.py:327, in SpawnProcess._Popen(process_obj)
324 @staticmethod
325 def _Popen(process_obj):
326 from .popen_spawn_win32 import Popen
--> 327 return Popen(process_obj)

File D:\Anaconda3\lib\multiprocessing\popen_spawn_win32.py:93, in Popen.init(self, process_obj)
91 try:
92 reduction.dump(prep_data, to_child)
---> 93 reduction.dump(process_obj, to_child)
94 finally:
95 set_spawning_popen(None)

File D:\Anaconda3\lib\multiprocessing\reduction.py:60, in dump(obj, file, protocol)
58 def dump(obj, file, protocol=None):
59 '''Replacement for pickle.dump() using ForkingPickler.'''
---> 60 ForkingPickler(file, protocol).dump(obj)

TypeError: cannot pickle '_thread.RLock' object

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[hbredin]

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