pyTorch not working with an old NVidia card

(Abenezer) #21

After a long search i found the solution
The problem is that if the GPU is old the pytorch version before 0.4.0 doesn’t work, so you have to install the newest pytorch,
run this - conda install -c pytorch pytorch
After you install the newest pytorch you will face torch._C import * DLL load failed problem
to fix that run the ff code

conda install -c pytorch pytorch

(Raaj) #22

Hello. I had faced done the setup locally until I faced the same issue because I have NVidia GeForce 830M. I followed these steps to install pytorch from source. In the last step when I ran python install, it gave me an error.
Gcc runtime error 1. It said ‘CuDNN v5 found, but need at least CuDNN v6’. I am new to setting up. I will be grateful if anyone could help me through this.

The problem is that in my fastai environment there is a conda package of cudnn v7.2.1 which is confusing me.

(Raaj) #23

Followed the steps. Why am i getting ‘CuDNN version not supported’ error after last step?


I have installed pytorch 0.4, the old gpu problem has gone but a new error came out:

UnicodeDecodeError Traceback (most recent call last)
in ()
----> 1 learn = ConvLearner.pretrained(arch, data, precompute=True)

~/fastai/courses/dl1/fastai/ in pretrained(cls, f, data, ps, xtra_fc, xtra_cut, custom_head, precompute, pretrained, **kwargs)
112 models = ConvnetBuilder(f, data.c, data.is_multi, data.is_reg,
113 ps=ps, xtra_fc=xtra_fc, xtra_cut=xtra_cut, custom_head=custom_head, pretrained=pretrained)
–> 114 return cls(data, models, precompute, **kwargs)
116 @classmethod

~/fastai/courses/dl1/fastai/ in init(self, data, models, precompute, **kwargs)
98 if hasattr(data, ‘is_multi’) and not data.is_reg and self.metrics is None:
99 self.metrics = [accuracy_thresh(0.5)] if else [accuracy]
–> 100 if precompute: self.save_fc1()
101 self.freeze()
102 self.precompute = precompute

~/fastai/courses/dl1/fastai/ in save_fc1(self)
177 m=self.models.top_model
178 if len(self.activations[0])!=len(
–> 179 predict_to_bcolz(m,, act)
180 if len(self.activations[1])!=len(
181 predict_to_bcolz(m,, val_act)

~/fastai/courses/dl1/fastai/ in predict_to_bcolz(m, gen, arr, workers)
15 lock=threading.Lock()
16 m.eval()
—> 17 for x,*_ in tqdm(gen):
18 y = to_np(m(VV(x)).data)
19 with lock:

~/anaconda3/envs/fastai/lib/python3.6/site-packages/tqdm/ in iter(self)
935 “”", fp_write=getattr(self.fp, ‘write’, sys.stderr.write))
–> 937 for obj in iterable:
938 yield obj
939 # Update and possibly print the progressbar.

~/fastai/courses/dl1/fastai/ in iter(self)
86 # avoid py3.6 issue where queue is infinite and can result in memory exhaustion
87 for c in chunk_iter(iter(self.batch_sampler), self.num_workers*10):
—> 88 for batch in, c):
89 yield get_tensor(batch, self.pin_memory, self.half)

~/anaconda3/envs/fastai/lib/python3.6/concurrent/futures/ in result_iterator()
584 # Careful not to keep a reference to the popped future
585 if timeout is None:
–> 586 yield fs.pop().result()
587 else:
588 yield fs.pop().result(end_time - time.time())

~/anaconda3/envs/fastai/lib/python3.6/concurrent/futures/ in result(self, timeout)
423 raise CancelledError()
424 elif self._state == FINISHED:
–> 425 return self.__get_result()
427 self._condition.wait(timeout)

~/anaconda3/envs/fastai/lib/python3.6/concurrent/futures/ in __get_result(self)
382 def __get_result(self):
383 if self._exception:
–> 384 raise self._exception
385 else:
386 return self._result

~/anaconda3/envs/fastai/lib/python3.6/concurrent/futures/ in run(self)
55 try:
—> 56 result = self.fn(*self.args, **self.kwargs)
57 except BaseException as exc:
58 self.future.set_exception(exc)

~/fastai/courses/dl1/fastai/ in get_batch(self, indices)
74 def get_batch(self, indices):
—> 75 res = self.np_collate([self.dataset[i] for i in indices])
76 if self.transpose: res[0] = res[0].T
77 if self.transpose_y: res[1] = res[1].T

~/fastai/courses/dl1/fastai/ in (.0)
74 def get_batch(self, indices):
—> 75 res = self.np_collate([self.dataset[i] for i in indices])
76 if self.transpose: res[0] = res[0].T
77 if self.transpose_y: res[1] = res[1].T

~/fastai/courses/dl1/fastai/ in getitem(self, idx)
201 xs,ys = zip(*[self.get1item(i) for i in range(*idx.indices(self.n))])
202 return np.stack(xs),ys
–> 203 return self.get1item(idx)
205 def len(self): return self.n

~/fastai/courses/dl1/fastai/ in get1item(self, idx)
195 def get1item(self, idx):
–> 196 x,y = self.get_x(idx),self.get_y(idx)
197 return self.get(self.transform, x, y)

~/fastai/courses/dl1/fastai/ in get_x(self, i)
297 super().init(transform)
298 def get_sz(self): return
–> 299 def get_x(self, i): return open_image(os.path.join(self.path, self.fnames[i]))
300 def get_n(self): return len(self.fnames)

~/fastai/courses/dl1/fastai/ in open_image(fn)
266 elif os.path.isdir(fn) and not str(fn).startswith(“http”):
267 raise OSError(‘Is a directory: {}’.format(fn))
–> 268 elif isdicom(fn):
269 slice = pydicom.read_file(fn)
270 if slice.PhotometricInterpretation.startswith(‘MONOCHROME’):

~/fastai/courses/dl1/fastai/ in isdicom(fn)
250 with open(fn) as fh:
–> 252 return‘DICM’
254 def open_image(fn):

~/anaconda3/envs/fastai/lib/python3.6/ in decode(self, input, final)
319 # decode input (taking the buffer into account)
320 data = self.buffer + input
–> 321 (result, consumed) = self._buffer_decode(data, self.errors, final)
322 # keep undecoded input until the next call
323 self.buffer = data[consumed:]

UnicodeDecodeError: ‘utf-8’ codec can’t decode byte 0xff in position 30: invalid start byte

I have also tried pytorch 0.3 and it gives the same UnicodeDecodeError…
Have you seen this error in the process or do you have any suggestions? Thank you.

(Dobrik) #25

I got the same error and could not work on the course. Help needed!

(Dobrik) #26

OK, managed to fix it!
do a git pull. they’ve seem to have fixed the files.
Now I get the warning but at last I can run stuff.
(It’s horribly slow though)


Yes, works now. Thank you for reminding!