As of today (16 October 2020), you need to install the pytorch-nightly version to use the card. This can be done with conda install pytorch torchvision cudatoolkit=11.0 -c pytorch-nightly. See this issue for more details.
When running the pets tutorial, I run into the following error when trying to call dls.show_batch():
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-69-ad768ac4dd9a> in <module>
----> 1 tst_dl.show_batch(max_n=9)
~/git/fastai/fastai/data/core.py in show_batch(self, b, max_n, ctxs, show, unique, **kwargs)
98 old_get_idxs = self.get_idxs
99 self.get_idxs = lambda: Inf.zeros
--> 100 if b is None: b = self.one_batch()
101 if not show: return self._pre_show_batch(b, max_n=max_n)
102 show_batch(*self._pre_show_batch(b, max_n=max_n), ctxs=ctxs, max_n=max_n, **kwargs)
~/git/fastai/fastai/data/load.py in one_batch(self)
134 def one_batch(self):
135 if self.n is not None and len(self)==0: raise ValueError(f'This DataLoader does not contain any batches')
--> 136 with self.fake_l.no_multiproc(): res = first(self)
137 if hasattr(self, 'it'): delattr(self, 'it')
138 return res
~/git/fastcore/fastcore/foundation.py in first(x)
236 def first(x):
237 "First element of `x`, or None if missing"
--> 238 try: return next(iter(x))
239 except StopIteration: return None
240
~/git/fastai/fastai/data/load.py in __iter__(self)
100 self.before_iter()
101 self.__idxs=self.get_idxs() # called in context of main process (not workers/subprocesses)
--> 102 for b in _loaders[self.fake_l.num_workers==0](self.fake_l):
103 if self.device is not None: b = to_device(b, self.device)
104 yield self.after_batch(b)
~/miniconda3/envs/bleeding-edge/lib/python3.7/site-packages/torch/utils/data/dataloader.py in __init__(self, loader)
467 class _SingleProcessDataLoaderIter(_BaseDataLoaderIter):
468 def __init__(self, loader):
--> 469 super(_SingleProcessDataLoaderIter, self).__init__(loader)
470 assert self._timeout == 0
471 assert self._num_workers == 0
~/miniconda3/envs/bleeding-edge/lib/python3.7/site-packages/torch/utils/data/dataloader.py in __init__(self, loader)
416 self._sampler_iter = iter(self._index_sampler)
417 self._base_seed = torch.empty((), dtype=torch.int64).random_(generator=loader.generator).item()
--> 418 self._persistent_workers = loader.persistent_workers
419 self._num_yielded = 0
420
AttributeError: '_FakeLoader' object has no attribute 'persistent_workers'
I also tried the vision tutorial and ran into the same error with a different trace when calling learn.fine_tune():
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-11-4b9dee10b079> in <module>
1 learn = cnn_learner(dls, resnet34, metrics=error_rate)
----> 2 learn.fine_tune(1)
~/git/fastcore/fastcore/logargs.py in _f(*args, **kwargs)
54 init_args.update(log)
55 setattr(inst, 'init_args', init_args)
---> 56 return inst if to_return else f(*args, **kwargs)
57 return _f
~/git/fastai/fastai/callback/schedule.py in fine_tune(self, epochs, base_lr, freeze_epochs, lr_mult, pct_start, div, **kwargs)
159 "Fine tune with `freeze` for `freeze_epochs` then with `unfreeze` from `epochs` using discriminative LR"
160 self.freeze()
--> 161 self.fit_one_cycle(freeze_epochs, slice(base_lr), pct_start=0.99, **kwargs)
162 base_lr /= 2
163 self.unfreeze()
~/git/fastcore/fastcore/logargs.py in _f(*args, **kwargs)
54 init_args.update(log)
55 setattr(inst, 'init_args', init_args)
---> 56 return inst if to_return else f(*args, **kwargs)
57 return _f
~/git/fastai/fastai/callback/schedule.py in fit_one_cycle(self, n_epoch, lr_max, div, div_final, pct_start, wd, moms, cbs, reset_opt)
111 scheds = {'lr': combined_cos(pct_start, lr_max/div, lr_max, lr_max/div_final),
112 'mom': combined_cos(pct_start, *(self.moms if moms is None else moms))}
--> 113 self.fit(n_epoch, cbs=ParamScheduler(scheds)+L(cbs), reset_opt=reset_opt, wd=wd)
114
115 # Cell
~/git/fastcore/fastcore/logargs.py in _f(*args, **kwargs)
54 init_args.update(log)
55 setattr(inst, 'init_args', init_args)
---> 56 return inst if to_return else f(*args, **kwargs)
57 return _f
~/git/fastai/fastai/learner.py in fit(self, n_epoch, lr, wd, cbs, reset_opt)
205 self.opt.set_hypers(lr=self.lr if lr is None else lr)
206 self.n_epoch = n_epoch
--> 207 self._with_events(self._do_fit, 'fit', CancelFitException, self._end_cleanup)
208
209 def _end_cleanup(self): self.dl,self.xb,self.yb,self.pred,self.loss = None,(None,),(None,),None,None
~/git/fastai/fastai/learner.py in _with_events(self, f, event_type, ex, final)
153
154 def _with_events(self, f, event_type, ex, final=noop):
--> 155 try: self(f'before_{event_type}') ;f()
156 except ex: self(f'after_cancel_{event_type}')
157 finally: self(f'after_{event_type}') ;final()
~/git/fastai/fastai/learner.py in _do_fit(self)
195 for epoch in range(self.n_epoch):
196 self.epoch=epoch
--> 197 self._with_events(self._do_epoch, 'epoch', CancelEpochException)
198
199 @log_args(but='cbs')
~/git/fastai/fastai/learner.py in _with_events(self, f, event_type, ex, final)
153
154 def _with_events(self, f, event_type, ex, final=noop):
--> 155 try: self(f'before_{event_type}') ;f()
156 except ex: self(f'after_cancel_{event_type}')
157 finally: self(f'after_{event_type}') ;final()
~/git/fastai/fastai/learner.py in _do_epoch(self)
189
190 def _do_epoch(self):
--> 191 self._do_epoch_train()
192 self._do_epoch_validate()
193
~/git/fastai/fastai/learner.py in _do_epoch_train(self)
181 def _do_epoch_train(self):
182 self.dl = self.dls.train
--> 183 self._with_events(self.all_batches, 'train', CancelTrainException)
184
185 def _do_epoch_validate(self, ds_idx=1, dl=None):
~/git/fastai/fastai/learner.py in _with_events(self, f, event_type, ex, final)
153
154 def _with_events(self, f, event_type, ex, final=noop):
--> 155 try: self(f'before_{event_type}') ;f()
156 except ex: self(f'after_cancel_{event_type}')
157 finally: self(f'after_{event_type}') ;final()
~/git/fastai/fastai/learner.py in all_batches(self)
159 def all_batches(self):
160 self.n_iter = len(self.dl)
--> 161 for o in enumerate(self.dl): self.one_batch(*o)
162
163 def _do_one_batch(self):
~/git/fastai/fastai/data/load.py in __iter__(self)
100 self.before_iter()
101 self.__idxs=self.get_idxs() # called in context of main process (not workers/subprocesses)
--> 102 for b in _loaders[self.fake_l.num_workers==0](self.fake_l):
103 if self.device is not None: b = to_device(b, self.device)
104 yield self.after_batch(b)
~/miniconda3/envs/bleeding-edge/lib/python3.7/site-packages/torch/utils/data/dataloader.py in __init__(self, loader)
764
765 def __init__(self, loader):
--> 766 super(_MultiProcessingDataLoaderIter, self).__init__(loader)
767
768 assert self._num_workers > 0
~/miniconda3/envs/bleeding-edge/lib/python3.7/site-packages/torch/utils/data/dataloader.py in __init__(self, loader)
416 self._sampler_iter = iter(self._index_sampler)
417 self._base_seed = torch.empty((), dtype=torch.int64).random_(generator=loader.generator).item()
--> 418 self._persistent_workers = loader.persistent_workers
419 self._num_yielded = 0
420
AttributeError: '_FakeLoader' object has no attribute 'persistent_workers'
There seems to be some new additions in PyTorch’s dataloaders.py module that causes these errors.
Any clue how to fix this / is this a trivial fix? Is there a hacky workaround?
Thanks!