I am facing this issue now. Have you got any idea how to fix this?
Hi,
I am a course-v3 student and have a few questions for lesson 2,
- Should we be running
pip install fastai --upgradein our notebooks? (Considering there’s a new version of fastai now that is incompatible with course-v3 fastai. - How can train and valid loss be > 1? Isn’t 1 == 100% loss? What does a loss greater than 100% mean?
- I noticed in the notebook
learn.export()andload_learner(...)is used; how is this different fromlearn.save(...)andlearn.load(...)?
Thank you.
Regards,
I am going through Chapter 2’s notebook, and remaking the example bear classifier. I get this error when running learn.fine_tune4():
RuntimeError: DataLoader worker (pid 19862) is killed by signal: Killed.
I am using Gradient. Any help will be appreciated! Thank you.
The full error message is:
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-12-672a4803901c> in <module>
1 learn = cnn_learner(dls, resnet18, metrics=error_rate)
----> 2 learn.fine_tune(4)
/opt/conda/envs/fastai/lib/python3.8/site-packages/fastcore/utils.py in _f(*args, **kwargs)
470 init_args.update(log)
471 setattr(inst, 'init_args', init_args)
--> 472 return inst if to_return else f(*args, **kwargs)
473 return _f
474
/opt/conda/envs/fastai/lib/python3.8/site-packages/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()
/opt/conda/envs/fastai/lib/python3.8/site-packages/fastcore/utils.py in _f(*args, **kwargs)
470 init_args.update(log)
471 setattr(inst, 'init_args', init_args)
--> 472 return inst if to_return else f(*args, **kwargs)
473 return _f
474
/opt/conda/envs/fastai/lib/python3.8/site-packages/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
/opt/conda/envs/fastai/lib/python3.8/site-packages/fastcore/utils.py in _f(*args, **kwargs)
470 init_args.update(log)
471 setattr(inst, 'init_args', init_args)
--> 472 return inst if to_return else f(*args, **kwargs)
473 return _f
474
/opt/conda/envs/fastai/lib/python3.8/site-packages/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,self.loss = n_epoch,tensor(0.)
--> 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
/opt/conda/envs/fastai/lib/python3.8/site-packages/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()
/opt/conda/envs/fastai/lib/python3.8/site-packages/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')
/opt/conda/envs/fastai/lib/python3.8/site-packages/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()
/opt/conda/envs/fastai/lib/python3.8/site-packages/fastai/learner.py in _do_epoch(self)
189
190 def _do_epoch(self):
--> 191 self._do_epoch_train()
192 self._do_epoch_validate()
193
/opt/conda/envs/fastai/lib/python3.8/site-packages/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):
/opt/conda/envs/fastai/lib/python3.8/site-packages/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()
/opt/conda/envs/fastai/lib/python3.8/site-packages/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):
/opt/conda/envs/fastai/lib/python3.8/site-packages/fastai/learner.py in one_batch(self, i, b)
177 self.iter = i
178 self._split(b)
--> 179 self._with_events(self._do_one_batch, 'batch', CancelBatchException)
180
181 def _do_epoch_train(self):
/opt/conda/envs/fastai/lib/python3.8/site-packages/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()
/opt/conda/envs/fastai/lib/python3.8/site-packages/fastai/learner.py in _do_one_batch(self)
162
163 def _do_one_batch(self):
--> 164 self.pred = self.model(*self.xb)
165 self('after_pred')
166 if len(self.yb): self.loss = self.loss_func(self.pred, *self.yb)
/opt/conda/envs/fastai/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
720 result = self._slow_forward(*input, **kwargs)
721 else:
--> 722 result = self.forward(*input, **kwargs)
723 for hook in itertools.chain(
724 _global_forward_hooks.values(),
/opt/conda/envs/fastai/lib/python3.8/site-packages/torch/nn/modules/container.py in forward(self, input)
115 def forward(self, input):
116 for module in self:
--> 117 input = module(input)
118 return input
119
/opt/conda/envs/fastai/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
720 result = self._slow_forward(*input, **kwargs)
721 else:
--> 722 result = self.forward(*input, **kwargs)
723 for hook in itertools.chain(
724 _global_forward_hooks.values(),
/opt/conda/envs/fastai/lib/python3.8/site-packages/torch/nn/modules/container.py in forward(self, input)
115 def forward(self, input):
116 for module in self:
--> 117 input = module(input)
118 return input
119
/opt/conda/envs/fastai/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
720 result = self._slow_forward(*input, **kwargs)
721 else:
--> 722 result = self.forward(*input, **kwargs)
723 for hook in itertools.chain(
724 _global_forward_hooks.values(),
/opt/conda/envs/fastai/lib/python3.8/site-packages/torch/nn/modules/container.py in forward(self, input)
115 def forward(self, input):
116 for module in self:
--> 117 input = module(input)
118 return input
119
/opt/conda/envs/fastai/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
720 result = self._slow_forward(*input, **kwargs)
721 else:
--> 722 result = self.forward(*input, **kwargs)
723 for hook in itertools.chain(
724 _global_forward_hooks.values(),
/opt/conda/envs/fastai/lib/python3.8/site-packages/torchvision/models/resnet.py in forward(self, x)
61 out = self.relu(out)
62
---> 63 out = self.conv2(out)
64 out = self.bn2(out)
65
/opt/conda/envs/fastai/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
720 result = self._slow_forward(*input, **kwargs)
721 else:
--> 722 result = self.forward(*input, **kwargs)
723 for hook in itertools.chain(
724 _global_forward_hooks.values(),
/opt/conda/envs/fastai/lib/python3.8/site-packages/torch/nn/modules/conv.py in forward(self, input)
417
418 def forward(self, input: Tensor) -> Tensor:
--> 419 return self._conv_forward(input, self.weight)
420
421 class Conv3d(_ConvNd):
/opt/conda/envs/fastai/lib/python3.8/site-packages/torch/nn/modules/conv.py in _conv_forward(self, input, weight)
413 weight, self.bias, self.stride,
414 _pair(0), self.dilation, self.groups)
--> 415 return F.conv2d(input, weight, self.bias, self.stride,
416 self.padding, self.dilation, self.groups)
417
/opt/conda/envs/fastai/lib/python3.8/site-packages/torch/utils/data/_utils/signal_handling.py in handler(signum, frame)
64 # This following call uses `waitid` with WNOHANG from C side. Therefore,
65 # Python can still get and update the process status successfully.
---> 66 _error_if_any_worker_fails()
67 if previous_handler is not None:
68 previous_handler(signum, frame)
RuntimeError: DataLoader worker (pid 19862) is killed by signal: Killed.
Managed to get my first production model! Felt like it took far too long to get it all to work, but I got there in the end…
1 Like
For the first one, I would recomend use conda enviroments or another way to install fastai v2 that yes is not backward compatible in API, but some knowledge could be translated from one to the other, only things things changed from name and got another organization.
And yes, if you will not use the old one, you should do --upgrade.
This is typically a memory problem. How much RAM do you have? Try reducing the number of concurrent processes, it might help you.
1 Like
Thank you, I want to continue using fastai-v1, is there a safe way to upgrade to the latest version of fastai-v1 without corrupting the environment with fastai-v2?
I think I know the mistake now. All the free GPU at Gradient was used up and so I was unknowingly using CPU.
If you sign up for an account and use the discount code, you’ll magically get the free GPUs on a more regular basis than trying to get by using a free account.
1 Like
Hi,
The dls.valid.show_batch and dls.train.show_batch does not show any images when I execute the command. Screenshot attach. The same is the case with the confusion matrix.
Is any specific package required? How can I resolve the issue?
Having same problem here with paperspace free instance too
Sorry, misspeled…
In my case in paperspace (don’t know why) the notebook code had the simbol dash larger (
) so substituted with two regular – did the thing. Maybe it helps you too if that’s the case also¿?
Does Jeremy recommend we go through the course lesson and then the related book chapter? Or the other way around?
I am not sure what jeremy recommends so someone else who does know can jump in here.
I think jeremy made the book to be used in a hands-on manner: I would read the book, and as you go, run the notebooks in parallel.
1 Like
Hi,
Is there a way to ensure that the validation set in the datablock has equal set of samples from each class assuming a balanced class for classification?
Regards
Ganesh Bhat
Yes, you can use the stratify option in the TrainTestSplitter. Under the hood it uses the train_test_split function of scikit-learn.
2 Likes
Thanks.
Hi Everyone,
I have been trying to launch a bear classifier on binder since last 3 days, not sure where I am getting stuck.
Have been able to launch the web app locally with voila, but not working with binder.
Github repository link: https://github.com/Avinash1419/Voila_bear
The app when launched locally on voila looks like this:
It works perfectly fine for basic options as expected.
But when tried on binder, without mentioning anything in the file section on mybinder, it loads as a jupyter notebook hosted online. But with URL as required for voila, with necessary form fields, it always comes up with 404 error issue as shown below:
The form fields filled are as shown in the figure here:
Please suggest ways to resolve this issue!!
Things I felt might have been the issue, but I couldn’t resolve them:
- I have stated required packages in the requirements.txt after finding out the locally used packages in the Jupyter notebook. List of packages mentioned are listed here:
voila==0.2.2
torchvision==0.7.0
torch==1.6.0
sentencepiece==0.1.86
scipy==1.4.1
requests==2.22.0
Pillow==7.2.0
path==13.1.0
pandas==1.0.1
numpy==1.18.1
msrest==0.6.19
matplotlib==3.1.3
ipywidgets==7.5.1
fastprogress==1.0.0
fastcore==1.0.9
fastai==2.0.13
The list was generated by a basic code (included in the main .ipynb file in the last cell). Voila was included manually (it wasn’t generated in the code). But I am not sure if any other packages are to be listed.
- On running the beginning part of the code for importing fastai modules, faced a small issue as shown in the image:
It didn’t create issue in voila launch, not sure if it could have been an issue in binder.
-
On mentioning the URL in binder site, I wasn’t sure of the format of URL. I used the one shown in the image above of mybinder, with and without ‘/’ in the beginning, i.e. before “voila”. Both of them didn’t work. Let me know if I should try some other way.
-
I have commented these two commands in the .ipynb file.
!pip install voila
!jupyter serverextension enable voila -sys-prefix
Basically, since all the requirements of installation are being given through requirements.txt, I thought these aren’t needed in the .ipynb file.
This is the basic context of how I tried to work on the problem, and concerns I felt were relevant. Please let me know if this can be resolved one way or the other!
1 Like
Greetings All,
I am currently unable to deploy my drone classifier notebook onto Binder. I get the error below (please refer to the screenshot). Was wondering if anyone could share where I might have gone wrong here.
Link to my github for the drone classifier code: https://github.com/SantoshYadaw/drone_class
Thank you in advance 
You’ve told it to use a branch called master but you don’t have one, just one branch called main
2 Likes