Please use this topic to discuss issues related to fastai2 on Windows. This is a wiki post, so feel free to add any FAQs or resources directly to this top post.
I use Linux more than Windows these days; my home system I recently upgraded to Ryzen. I usually use a GPU passthrough setup but I donāt have a spare GPU to use without an iGPU and I donāt have any spare drives to dual boot Ubuntu or similar fromā¦ So trying my best to get Windows working at the moment! If it turns out to be an impossibility, Iāll have to get a drive or something but Iād really like to make a good effort of getting it going. Iām just perplexed I have seemingly zero obvious reason as to why the GPU isnāt being utilised, itās very odd indeed.
Tired of the frustrating limitations of free Paperspace/Colab and I have a 1080Ti sitting on my desk that I may as well be using!
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Full disclosure - I work for Microsoft - but I tend to use a data science VM on Azure running Ubuntu, but also donāt like my Win10 + 1080ti doing nothing - so I did aim to get v2 running for the course once it had settled down.
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Not sure whatās going on in your case, but fastai2 is training using my GPU on Windows 10. Using pytorch 1.4 installed with the default settings conda install pytorch torchvision cudatoolkit=10.1 -c pytorch. Worked on both the 441.x and 442.x drivers. Have to set num_workers=0 for the dataloader, of course.
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Same here - GPU was showing cuda activity. On Win10 (insiders preview 19564), just did the install exactly as on Github (not editable) and ran 22_tutorial.imagenette.ipynb. Just needed the num_workers=0. I have cuda 10, 10.1 and 10.2 - drivers - looks like pytorch 1.4.0 targets 10.1. NVidia drivers 450.12. 1080 Ti.
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I am using fastai v1 atm I believe, good old dim me this is the v2 topic haha.
I will try v2 however - those steps are pretty much identical to what I did with v1.
Hi @Russbo, 10 days ago, my GPU couldnāt be used on my Windows laptop. I ran nvidia-smi and the output was the following:
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 417.98 Driver Version: 417.98 CUDA Version: 10.0 |
|-------------------------------+----------------------+----------------------+
Then I updated my nvidia driver by downloading and installing the file from:
CUDA Toolkit 10.2 Download
Now, nvidia-smi shows the new driver version (441.22 ), and the new cuda version (10.2)
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 441.22 Driver Version: 441.22 CUDA Version: 10.2 |
|-------------------------------+----------------------+----------------------+
Since then, the GPU is used.
default_device()
device(type='cuda', index=0)
I am presently using this version of torch
# Name Version Build Channel
pytorch 1.3.1 py3.6_cuda101_cudnn7_0 pytorch
torchvision 0.5.0 pypi_0 pypi
and this version of fastai2
# Name Version Build Channel
fastai2 0.0.11 dev_0 <develop>
I hope this information will help.
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I Can run it on windows nvidia driver is 442.19 and CUDA 10.2, but have to set num_workers=0 in the dataloader.
Out of curiosity i ran it on ubuntu and its 3.5-4x quicker.
Is it because of num_workers?
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Me too, so I gave up - far too old to learn Ubuntu and command line typing so I gave up! Bloody sad; My 2080Ti does render text very well.
Looking forward to developments for windows.
Cheers and good luck, Peter Kelly, Oz.
Iām facing the following error while running on Windows 7, CUDA Toolkit 10.2
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
~\Anaconda3\lib\site-packages\fastai2\learner.py in one_batch(self, i, b)
134 if len(self.yb) == 0: return
--> 135 self.loss = self.loss_func(self.pred, *self.yb); self('after_loss')
136 if not self.training: return
~\Anaconda3\lib\site-packages\fastai2\learner.py in __call__(self, event_name)
107
--> 108 def __call__(self, event_name): L(event_name).map(self._call_one)
109 def _call_one(self, event_name):
~\Anaconda3\lib\site-packages\fastcore\foundation.py in map(self, f, *args, **kwargs)
361 else f.__getitem__)
--> 362 return self._new(map(g, self))
363
~\Anaconda3\lib\site-packages\fastcore\foundation.py in _new(self, items, *args, **kwargs)
314 def _xtra(self): return None
--> 315 def _new(self, items, *args, **kwargs): return type(self)(items, *args, use_list=None, **kwargs)
316 def __getitem__(self, idx): return self._get(idx) if is_indexer(idx) else L(self._get(idx), use_list=None)
~\Anaconda3\lib\site-packages\fastcore\foundation.py in __call__(cls, x, *args, **kwargs)
40
---> 41 res = super().__call__(*((x,) + args), **kwargs)
42 res._newchk = 0
~\Anaconda3\lib\site-packages\fastcore\foundation.py in __init__(self, items, use_list, match, *rest)
305 if (use_list is not None) or not _is_array(items):
--> 306 items = list(items) if use_list else _listify(items)
307 if match is not None:
~\Anaconda3\lib\site-packages\fastcore\foundation.py in _listify(o)
241 if isinstance(o, str) or _is_array(o): return [o]
--> 242 if is_iter(o): return list(o)
243 return [o]
~\Anaconda3\lib\site-packages\fastcore\foundation.py in __call__(self, *args, **kwargs)
207 fargs = [args[x.i] if isinstance(x, _Arg) else x for x in self.pargs] + args[self.maxi+1:]
--> 208 return self.fn(*fargs, **kwargs)
209
~\Anaconda3\lib\site-packages\fastai2\learner.py in _call_one(self, event_name)
110 assert hasattr(event, event_name)
--> 111 [cb(event_name) for cb in sort_by_run(self.cbs)]
112
~\Anaconda3\lib\site-packages\fastai2\learner.py in <listcomp>(.0)
110 assert hasattr(event, event_name)
--> 111 [cb(event_name) for cb in sort_by_run(self.cbs)]
112
~\Anaconda3\lib\site-packages\fastai2\callback\core.py in __call__(self, event_name)
22 (self.run_valid and not getattr(self, 'training', False)))
---> 23 if self.run and _run: getattr(self, event_name, noop)()
24 if event_name=='after_fit': self.run=True #Reset self.run to True at each end of fit
<ipython-input-13-aa0bc7c3e6b5> in after_loss(self)
24 def after_loss(self):
---> 25 loss0 = self.loss_func0(self.learn.pred, *self.learn.yb[0], reduction='none')
26 loss1 = self.loss_func0(self.learn.pred, *self.learn.yb[1], reduction='none')
~\Anaconda3\lib\site-packages\torch\nn\modules\module.py in __call__(self, *input, **kwargs)
531 else:
--> 532 result = self.forward(*input, **kwargs)
533 for hook in self._forward_hooks.values():
<ipython-input-11-4dd00339d91c> in forward(self, xs, reduction, *ys)
7 for i, w, x, y in zip(range(len(xs)), self.w, xs, ys):
----> 8 if i == 0: loss = w*self.func(x, y, reduction=reduction)
9 else: loss += w*self.func(x, y, reduction=reduction)
~\Anaconda3\lib\site-packages\torch\nn\functional.py in cross_entropy(input, target, weight, size_average, ignore_index, reduce, reduction)
2020 reduction = _Reduction.legacy_get_string(size_average, reduce)
-> 2021 return nll_loss(log_softmax(input, 1), target, weight, None, ignore_index, None, reduction)
2022
~\Anaconda3\lib\site-packages\torch\nn\functional.py in nll_loss(input, target, weight, size_average, ignore_index, reduce, reduction)
1837 if dim == 2:
-> 1838 ret = torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
1839 elif dim == 4:
RuntimeError: Expected object of scalar type Long but got scalar type Int for argument #2 'target' in call to _thnn_nll_loss_forward
During handling of the above exception, another exception occurred:
AttributeError Traceback (most recent call last)
<ipython-input-18-d81c6bd29d71> in <module>
----> 1 learn.lr_find()
~\Anaconda3\lib\site-packages\fastai2\callback\schedule.py in lr_find(self, start_lr, end_lr, num_it, stop_div, show_plot, suggestions)
217 n_epoch = num_it//len(self.dls.train) + 1
218 cb=LRFinder(start_lr=start_lr, end_lr=end_lr, num_it=num_it, stop_div=stop_div)
--> 219 with self.no_logging(): self.fit(n_epoch, cbs=cb)
220 if show_plot: self.recorder.plot_lr_find()
221 if suggestions:
~\Anaconda3\lib\site-packages\fastai2\learner.py in fit(self, n_epoch, lr, wd, cbs, reset_opt)
174 try:
175 self.epoch=epoch; self('begin_epoch')
--> 176 self._do_epoch_train()
177 self._do_epoch_validate()
178 except CancelEpochException: self('after_cancel_epoch')
~\Anaconda3\lib\site-packages\fastai2\learner.py in _do_epoch_train(self)
147 try:
148 self.dl = self.dls.train; self('begin_train')
--> 149 self.all_batches()
150 except CancelTrainException: self('after_cancel_train')
151 finally: self('after_train')
~\Anaconda3\lib\site-packages\fastai2\learner.py in all_batches(self)
125 def all_batches(self):
126 self.n_iter = len(self.dl)
--> 127 for o in enumerate(self.dl): self.one_batch(*o)
128
129 def one_batch(self, i, b):
~\Anaconda3\lib\site-packages\fastai2\learner.py in one_batch(self, i, b)
139 self.opt.zero_grad()
140 except CancelBatchException: self('after_cancel_batch')
--> 141 finally: self('after_batch')
142
143 def _do_begin_fit(self, n_epoch):
~\Anaconda3\lib\site-packages\fastai2\learner.py in __call__(self, event_name)
106 def ordered_cbs(self, cb_func): return [cb for cb in sort_by_run(self.cbs) if hasattr(cb, cb_func)]
107
--> 108 def __call__(self, event_name): L(event_name).map(self._call_one)
109 def _call_one(self, event_name):
110 assert hasattr(event, event_name)
~\Anaconda3\lib\site-packages\fastcore\foundation.py in map(self, f, *args, **kwargs)
360 else f.format if isinstance(f,str)
361 else f.__getitem__)
--> 362 return self._new(map(g, self))
363
364 def filter(self, f, negate=False, **kwargs):
~\Anaconda3\lib\site-packages\fastcore\foundation.py in _new(self, items, *args, **kwargs)
313 @property
314 def _xtra(self): return None
--> 315 def _new(self, items, *args, **kwargs): return type(self)(items, *args, use_list=None, **kwargs)
316 def __getitem__(self, idx): return self._get(idx) if is_indexer(idx) else L(self._get(idx), use_list=None)
317 def copy(self): return self._new(self.items.copy())
~\Anaconda3\lib\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
~\Anaconda3\lib\site-packages\fastcore\foundation.py in __init__(self, items, use_list, match, *rest)
304 if items is None: items = []
305 if (use_list is not None) or not _is_array(items):
--> 306 items = list(items) if use_list else _listify(items)
307 if match is not None:
308 if is_coll(match): match = len(match)
~\Anaconda3\lib\site-packages\fastcore\foundation.py in _listify(o)
240 if isinstance(o, list): return o
241 if isinstance(o, str) or _is_array(o): return [o]
--> 242 if is_iter(o): return list(o)
243 return [o]
244
~\Anaconda3\lib\site-packages\fastcore\foundation.py in __call__(self, *args, **kwargs)
206 if isinstance(v,_Arg): kwargs[k] = args.pop(v.i)
207 fargs = [args[x.i] if isinstance(x, _Arg) else x for x in self.pargs] + args[self.maxi+1:]
--> 208 return self.fn(*fargs, **kwargs)
209
210 # Cell
~\Anaconda3\lib\site-packages\fastai2\learner.py in _call_one(self, event_name)
109 def _call_one(self, event_name):
110 assert hasattr(event, event_name)
--> 111 [cb(event_name) for cb in sort_by_run(self.cbs)]
112
113 def _bn_bias_state(self, with_bias): return bn_bias_params(self.model, with_bias).map(self.opt.state)
~\Anaconda3\lib\site-packages\fastai2\learner.py in <listcomp>(.0)
109 def _call_one(self, event_name):
110 assert hasattr(event, event_name)
--> 111 [cb(event_name) for cb in sort_by_run(self.cbs)]
112
113 def _bn_bias_state(self, with_bias): return bn_bias_params(self.model, with_bias).map(self.opt.state)
~\Anaconda3\lib\site-packages\fastai2\callback\core.py in __call__(self, event_name)
21 _run = (event_name not in _inner_loop or (self.run_train and getattr(self, 'training', True)) or
22 (self.run_valid and not getattr(self, 'training', False)))
---> 23 if self.run and _run: getattr(self, event_name, noop)()
24 if event_name=='after_fit': self.run=True #Reset self.run to True at each end of fit
25
~\Anaconda3\lib\site-packages\fastai2\learner.py in after_batch(self)
387 if len(self.yb) == 0: return
388 mets = self._train_mets if self.training else self._valid_mets
--> 389 for met in mets: met.accumulate(self.learn)
390 if not self.training: return
391 self.lrs.append(self.opt.hypers[-1]['lr'])
~\Anaconda3\lib\site-packages\fastai2\learner.py in accumulate(self, learn)
350 def accumulate(self, learn):
351 self.count += 1
--> 352 self.val = torch.lerp(to_detach(learn.loss.mean(), gather=False), self.val, self.beta)
353 @property
354 def value(self): return self.val/(1-self.beta**self.count)
AttributeError: 'NoneType' object has no attribute 'mean'
This is my code:
# Create databunch
db = DataBlock(blocks=(ImageBlock(cls=PILImageBW), *(3*[CategoryBlock])),
getters=[ColReader('image_id', pref=TRAIN, suff='.png'), # input
ColReader('species'),
ColReader('genus'),
ColReader('geo')],
splitter=IndexSplitter(df.loc[df.fold==fold].index))
tfms = aug_transforms(do_flip=False, size=sz)
tfms += [Normalize.from_stats(*imagenet_stats)]
dls = db.dataloaders(df, bs=bs, batch_tfms=tfms, num_workers=0)
dls.n_inp = 1 # number of inputs
learn = Learner(dls, model, loss_func=Loss_combine(), cbs=[MixUp(), CSVLogger()],
metrics=[RecallPartial(a=i) for i in range(len(dls.c))] + [RecallCombine()],
splitter=lambda m: [list(m.body.parameters()), list(m.tails.parameters())])
learn.lr_find() --> **this is where it fails**
torch.cuda.is_available() ā True
torch.backends.cudnn.enabled ā True
Any pointers?
Thanks!
%debug
Although my Windows isnāt running very fast - for the course it will do anything you need it to - particularly with a 2080Ti. Iāll also be documenting how to get an Azure VM running Ubuntu up and running. Once you are in Jupyter Notebooks then really not much difference in the user experience. Same for the other vendors too. Iām old enough that I started on UNIX - so not as much of a learning curve - but plenty of people willing to help here Peter - you donāt have to give up :).
Hi Brian, I really appreciate your encouragement, interest and effort to contact me; THANK YOU VERY MUCH;
it may sound trite and hackneyed but I am sincerely humbled and actually feel a slight pang at the generosity that you, and some others, display and extend so selflessly.
Contrary to my perceived profile, Iām generally not a quitter. I did actually get all of pjhās (professor Jeremy Howardās) course stuff working; that is from all 3 previous courses. It is when I yeilded to my intrinsic stupidity and tried to install Cuda and all the other ādangerous to childrenā stuff that I was ground into alternative deprecating and self-abusing avoidance behavior. My tiny mind cannot tolerate hours of mindless downloading (Australian steam powered internet) and monkey-see-monkey-do behavior with the mandatory requisites of exacting attention to wide version control, compatability, path maintenance and other dogmatical installation and set-up requirements. I feel tired already!
I also suffer substantial pride, a daunting ego and tireless impatience.
Apart from the above, I am really keen and ready to go.
Determined to properly ādoā pjhās next online course in a few weeks, my preparations are underway, so I may call upon you, as invited - so be warned and have your excuses ready; I will understand.
As part of my preliminaries Iām considering buying (and reading) Jason Brownleeās ebook āDeep Learning for Computer Visionā for US$37 from " http://machinelearningmastery.com/", and I would appreciate your opinion if you have seen it. Pjhās courses are just so well researched and prepared, and they are presented so well with fantastic detail and invaluable anecdotal nuggets for all entry levels that such a diversion at this stage may be counterproductive.
My real interest is in āFeature Recognition and Extractionā and that which I can best describe as its complementary dual āFeature Definition and Descriptionā. This may appear a bit esoteric but in essence it refers to the idea of DESCRIBING a feature to look for, and this ādescriptionā would not be restricted to verbal - Iām sorry if this sounds sloppy, it is - the idea is to allow the user to describe the objects in question or being sought so as to guide the process of Feature Extraction; the features could comprise base classes, derivatives (children) and ātransfer featuresā from other libraries. This Feature Extraction idea seems best setup with a Graphical User Interface and libraries. The GUI description can consist of dialog boxes, check boxes, radio buttons and can be augmented by pictures or any other media and feature libraries. Yep, Iām proposing an Object Oriented approach to feature definition. I donāt know if this has been tried or is even a good idea and seek your comments if you are interested. The result could bb to enable a search for faces with blue eyes and big ears etc. similar to a police identikit that we see on tv. Does this interest you? What do you think? Please be frank.
End of rant!
Brian, back to reality, again I sincerely thank you for your mail and kind offer.
Sorry for the rant, but I thought you might be interested.
Cheers Brian, p.
On Mar 3, 2020, at 13:34, Brian Smith via Deep Learning Course Forums < do-not-reply@fast.ai> wrote:
ļæ¼brismith Brian Smith
March 3
Although my Windows isnāt running very fast - for the course it will do anything you need it to - particularly with a 2080Ti. Iāll also be documenting how to get an Azure VM running Ubuntu up and running. Once you are in Jupyter Notebooks then really not much difference in the user experience. Same for the other vendors too. Iām old enough that I started on UNIX - so not as much of a learning curve - but plenty of people willing to help here Peter - you donāt have to give up :).
ļæ¼ PeterKelly:
far too old to learn Ubuntu and command line typing so I gave up!
Visit Topic to respond.
In Reply To
ļæ¼PeterKelly Peter Kelly (OZ)
February 24
Me too, so I gave up - far too old to learn Ubuntu and command line typing so I gave up! Bloody sad; My 2080Ti does render text very well. Looking forward to developments for windows. Cheers and good luck, Peter Kelly, Oz.
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Anyone had success in installing and using fastai2 on Windows WSL Ubuntu ?
i didnāt read that book. i know machinelearning mastery have very good resources, so i think the book will be very good. one thing to note is that the book is using Keras, which is TensorFlow and Jeremy uses Pytorch for his course. It may be confusing to learn two frameworks at the same timeā¦
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I installed fastai2 in WSL (ubuntu), and I use Ananconda 3. I created 2 virtual environments (a regular one, and editable one). I use the editable one on a regular basis.
If you have any problem installing it, you can post your questions here and I will try to walk you through the steps in order to be able to install it on your local machine.
3 Likes
thank you Farid, that would be fantastic. i am just starting to use WSL Ubuntu on Windowsā¦ i have lots of issues, some silly questions expected xD
i installed miniconda 3 and did editable fastai2 intall and tried to run jupyter notebook but got error message below. however the notebook runs when i copy and pase the localhost url, so thatās OK for now 
also tried to install cuda drivers as per this instruction but it run out of free space on this Ubuntu WSL. not sure how to increase the disk size on the WSL Ubuntu. like for example i canāt see my other drives that i can access via Windows.
Edit: i was simply running out of space on my disk xD, but as explained below WSL at the moment doesnāt support GPU so will not work anyway.
Windows C drive is mounted at /mnt/c/ on WSL 
thanks a lot!
@miwojc, I would uninstall the miniconda 3 version, and would install the full version of Anaconda.
Here is a video tutorial on how to install Anaconda on ubuntu.
and Here are the instructions that I wrote down while installing mine:
How to install Anaconda 3 on Windows Subsystem Linux
====================================================
$ curl -O https://repo.anaconda.com/archive/Anaconda3-2019.10-Linux-x86_64.sh
$ sha256sum Anaconda3-2019.10-Linux-x86_64.sh
it will print this :
46d762284d252e51cd58a8ca6c8adc9da2eadc82c342927b2f66ed011d1d8b53 Anaconda3-2019.10-Linux-x86_64.sh
$ bash Anaconda3-2019.10-Linux-x86_64.sh
$ source ~/.bashrc
$ conda list
If you want to install the latest version of Anaconda (for ubuntu), check this link
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thank you.
for some reason (anaconda loads a lot of stuff i donāt need) i prefer to use miniconda, which is same but with less packages loaded by default. i use miniconda on native ubuntu installs and it works great with fastai. however if this would fix my issue i am fine to install anaconda!
apart from installing Anaconda and fastai2 did you have to install cuda drivers separately?
Unfortunately, I never used miniconda.
You donāt have to install cuda because it isnāt supported by WSL for now. When, I need to use the GPU, I use the Anaconda version that I installed on Windows: I have the 2 versions installed on the same machine. I also use VSCode, and that way I can open my 2 repos side by side. I always test my project on the 2 platforms.
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