AttributeError: 'NBMasterBar' object has no attribute 'out'

[0tist]

from the 10_nlp.ipynb, trying to fine tune the language model

learn.fit_one_cycle(1, 2e-2)

---

AssertionError Traceback (most recent call last)
/opt/conda/lib/python3.7/site-packages/fastai2/learner.py in fit(self, n_epoch, lr, wd, cbs, reset_opt)
187 try:
--> 188 self._do_begin_fit(n_epoch)
189 for epoch in range(n_epoch):

/opt/conda/lib/python3.7/site-packages/fastai2/learner.py in _do_begin_fit(self, n_epoch)
159 def _do_begin_fit(self, n_epoch):
--> 160 self.n_epoch,self.loss = n_epoch,tensor(0.); self('begin_fit')
161

/opt/conda/lib/python3.7/site-packages/fastai2/learner.py in call(self, event_name)
123
--> 124 def call(self, event_name): L(event_name).map(self._call_one)
125 def _call_one(self, event_name):

/opt/conda/lib/python3.7/site-packages/fastcore/foundation.py in map(self, f, *args, **kwargs)
371 else f.getitem)
--> 372 return self._new(map(g, self))
373

/opt/conda/lib/python3.7/site-packages/fastcore/foundation.py in _new(self, items, *args, **kwargs)
322 def _xtra(self): return None
--> 323 def _new(self, items, *args, **kwargs): return type(self)(items, *args, use_list=None, **kwargs)
324 def getitem(self, idx): return self._get(idx) if is_indexer(idx) else L(self._get(idx), use_list=None)

/opt/conda/lib/python3.7/site-packages/fastcore/foundation.py in call(cls, x, args, **kwargs)
40
---> 41 res = super().call(((x,) + args), **kwargs)
42 res._newchk = 0

/opt/conda/lib/python3.7/site-packages/fastcore/foundation.py in init(self, items, use_list, match, *rest)
313 if (use_list is not None) or not _is_array(items):
--> 314 items = list(items) if use_list else _listify(items)
315 if match is not None:

/opt/conda/lib/python3.7/site-packages/fastcore/foundation.py in _listify(o)
249 if isinstance(o, str) or _is_array(o): return [o]
--> 250 if is_iter(o): return list(o)
251 return [o]

/opt/conda/lib/python3.7/site-packages/fastcore/foundation.py in call(self, *args, **kwargs)
215 fargs = [args[x.i] if isinstance(x, _Arg) else x for x in self.pargs] + args[self.maxi+1:]
--> 216 return self.fn(*fargs, **kwargs)
217

/opt/conda/lib/python3.7/site-packages/fastai2/learner.py in _call_one(self, event_name)
126 assert hasattr(event, event_name)
--> 127 [cb(event_name) for cb in sort_by_run(self.cbs)]
128

/opt/conda/lib/python3.7/site-packages/fastai2/learner.py in (.0)
126 assert hasattr(event, event_name)
--> 127 [cb(event_name) for cb in sort_by_run(self.cbs)]
128

/opt/conda/lib/python3.7/site-packages/fastai2/callback/core.py in call(self, event_name)
23 (self.run_valid and not getattr(self, 'training', False)))
---> 24 if self.run and _run: getattr(self, event_name, noop)()
25 if event_name=='after_fit': self.run=True #Reset self.run to True at each end of fit

/opt/conda/lib/python3.7/site-packages/fastai2/callback/fp16.py in begin_fit(self)
83 def begin_fit(self):
---> 84 assert self.dls.device.type == 'cuda', "Mixed-precision training requires a GPU, remove the call to_fp16"
85 if self.learn.opt is None: self.learn.create_opt()

AssertionError: Mixed-precision training requires a GPU, remove the call to_fp16

During handling of the above exception, another exception occurred:

AttributeError Traceback (most recent call last)
in
----> 1 learn.fit_one_cycle(1, 2e-2)

/opt/conda/lib/python3.7/site-packages/fastai2/callback/schedule.py in fit_one_cycle(self, n_epoch, lr_max, div, div_final, pct_start, wd, moms, cbs, reset_opt)
110 scheds = {'lr': combined_cos(pct_start, lr_max/div, lr_max, lr_max/div_final),
111 'mom': combined_cos(pct_start, *(self.moms if moms is None else moms))}
--> 112 self.fit(n_epoch, cbs=ParamScheduler(scheds)+L(cbs), reset_opt=reset_opt, wd=wd)
113
114 # Cell

/opt/conda/lib/python3.7/site-packages/fastai2/learner.py in fit(self, n_epoch, lr, wd, cbs, reset_opt)
196
197 except CancelFitException: self('after_cancel_fit')
--> 198 finally: self('after_fit')
199
200 def validate(self, ds_idx=1, dl=None, cbs=None):

/opt/conda/lib/python3.7/site-packages/fastai2/learner.py in call(self, event_name)
122 def ordered_cbs(self, event): return [cb for cb in sort_by_run(self.cbs) if hasattr(cb, event)]
123
--> 124 def call(self, event_name): L(event_name).map(self._call_one)
125 def _call_one(self, event_name):
126 assert hasattr(event, event_name)

/opt/conda/lib/python3.7/site-packages/fastcore/foundation.py in map(self, f, *args, **kwargs)
370 else f.format if isinstance(f,str)
371 else f.getitem)
--> 372 return self._new(map(g, self))
373
374 def filter(self, f, negate=False, **kwargs):

/opt/conda/lib/python3.7/site-packages/fastcore/foundation.py in _new(self, items, *args, **kwargs)
321 @Property
322 def _xtra(self): return None
--> 323 def _new(self, items, *args, **kwargs): return type(self)(items, *args, use_list=None, **kwargs)
324 def getitem(self, idx): return self._get(idx) if is_indexer(idx) else L(self._get(idx), use_list=None)
325 def copy(self): return self._new(self.items.copy())

/opt/conda/lib/python3.7/site-packages/fastcore/foundation.py in call(cls, x, args, **kwargs)
39 return x
40
---> 41 res = super().call(((x,) + args), **kwargs)
42 res._newchk = 0
43 return res

/opt/conda/lib/python3.7/site-packages/fastcore/foundation.py in init(self, items, use_list, match, *rest)
312 if items is None: items = []
313 if (use_list is not None) or not _is_array(items):
--> 314 items = list(items) if use_list else _listify(items)
315 if match is not None:
316 if is_coll(match): match = len(match)

/opt/conda/lib/python3.7/site-packages/fastcore/foundation.py in _listify(o)
248 if isinstance(o, list): return o
249 if isinstance(o, str) or _is_array(o): return [o]
--> 250 if is_iter(o): return list(o)
251 return [o]
252

/opt/conda/lib/python3.7/site-packages/fastcore/foundation.py in call(self, *args, **kwargs)
214 if isinstance(v,_Arg): kwargs[k] = args.pop(v.i)
215 fargs = [args[x.i] if isinstance(x, _Arg) else x for x in self.pargs] + args[self.maxi+1:]
--> 216 return self.fn(*fargs, **kwargs)
217
218 # Cell

/opt/conda/lib/python3.7/site-packages/fastai2/learner.py in _call_one(self, event_name)
125 def _call_one(self, event_name):
126 assert hasattr(event, event_name)
--> 127 [cb(event_name) for cb in sort_by_run(self.cbs)]
128
129 def _bn_bias_state(self, with_bias): return bn_bias_params(self.model, with_bias).map(self.opt.state)

/opt/conda/lib/python3.7/site-packages/fastai2/learner.py in (.0)
125 def _call_one(self, event_name):
126 assert hasattr(event, event_name)
--> 127 [cb(event_name) for cb in sort_by_run(self.cbs)]
128
129 def _bn_bias_state(self, with_bias): return bn_bias_params(self.model, with_bias).map(self.opt.state)

/opt/conda/lib/python3.7/site-packages/fastai2/callback/core.py in call(self, event_name)
22 _run = (event_name not in _inner_loop or (self.run_train and getattr(self, 'training', True)) or
23 (self.run_valid and not getattr(self, 'training', False)))
---> 24 if self.run and _run: getattr(self, event_name, noop)()
25 if event_name=='after_fit': self.run=True #Reset self.run to True at each end of fit
26

/opt/conda/lib/python3.7/site-packages/fastai2/callback/progress.py in after_fit(self)
37 def after_fit(self):
38 if getattr(self, 'mbar', False):
---> 39 self.mbar.on_iter_end()
40 delattr(self, 'mbar')
41 self.learn.logger = self.old_logger

/opt/conda/lib/python3.7/site-packages/fastprogress/fastprogress.py in on_iter_end(self)
155 total_time = format_time(time.time() - self.main_bar.start_t)
156 self.text = f'Total time: {total_time}

' + self.text
--> 157 self.out.update(HTML(self.text))
158
159 def add_child(self, child):

AttributeError: 'NBMasterBar' object has no attribute 'out'

[0tist]

As I removed the callback .to_fp16(), the error was gone

[sgugger]

Yes, because as the stack trace indicated, the error comes from

Mixed-precision training requires a GPU, remove the call to_fp16"

the rest is just a consequence of that. You can't use to_fp16 if you don't have a GPU, this is unrelated to fastprogress.