Ubuntu.
CUDNN is a library of primitives which are used in CUDA if they’re present. It’s at a lower level than pytorch - on which fastai is written over. And it’s not required to have, but having them makes many computations run faster. And as you might have guessed by now, fastai runs with or without them.
Ive done exactly these steps. I can successfully build it but i still get the message,
Found GPU0 GeForce GTX 680 which is of cuda capability 3.0. PyTorch no longer supports this GPU because it is too old.warnings.warn(old_gpu_warn % (d, name, major, capability[1]))
Traceback (most recent call last):
File “generate.py”, line 154, in
main()
File “generate.py”, line 135, in main
model.load_state_dict(torch.load(args.model))
File “/home/server/miniconda2/envs/env123/lib/python3.5/site-packages/torch/nn/modules/module.py”, line 522, in load_state_dict
.format(name))
KeyError: ‘unexpected key “rnn_0.weight_ih_l0” in state_dict’
what can i do else ?
1 Like
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
set PYTORCH_BUILD_VERSION=0.4.1
conda install -c pytorch pytorch
set PYTORCH_BUILD_VERSION=0.4.1
Hello. I had faced done the fast.ai 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 setup.py 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.
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/conv_learner.py 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)
115
116 @classmethod~/fastai/courses/dl1/fastai/conv_learner.py 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 self.data.is_multi else [accuracy]
–> 100 if precompute: self.save_fc1()
101 self.freeze()
102 self.precompute = precompute~/fastai/courses/dl1/fastai/conv_learner.py in save_fc1(self)
177 m=self.models.top_model
178 if len(self.activations[0])!=len(self.data.trn_ds):
–> 179 predict_to_bcolz(m, self.data.fix_dl, act)
180 if len(self.activations[1])!=len(self.data.val_ds):
181 predict_to_bcolz(m, self.data.val_dl, val_act)~/fastai/courses/dl1/fastai/model.py 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/_tqdm.py in iter(self)
935 “”", fp_write=getattr(self.fp, ‘write’, sys.stderr.write))
936
–> 937 for obj in iterable:
938 yield obj
939 # Update and possibly print the progressbar.~/fastai/courses/dl1/fastai/dataloader.py 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 e.map(self.get_batch, c):
89 yield get_tensor(batch, self.pin_memory, self.half)
90~/anaconda3/envs/fastai/lib/python3.6/concurrent/futures/_base.py 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/_base.py in result(self, timeout)
423 raise CancelledError()
424 elif self._state == FINISHED:
–> 425 return self.__get_result()
426
427 self._condition.wait(timeout)~/anaconda3/envs/fastai/lib/python3.6/concurrent/futures/_base.py 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/thread.py in run(self)
54
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/dataloader.py in get_batch(self, indices)
73
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/dataloader.py in (.0)
73
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/dataset.py 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)
204
205 def len(self): return self.n~/fastai/courses/dl1/fastai/dataset.py in get1item(self, idx)
194
195 def get1item(self, idx):
–> 196 x,y = self.get_x(idx),self.get_y(idx)
197 return self.get(self.transform, x, y)
198~/fastai/courses/dl1/fastai/dataset.py in get_x(self, i)
297 super().init(transform)
298 def get_sz(self): return self.transform.sz
–> 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)
301~/fastai/courses/dl1/fastai/dataset.py 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/dataset.py in isdicom(fn)
250 with open(fn) as fh:
251 fh.seek(0x80)
–> 252 return fh.read(4)==‘DICM’
253
254 def open_image(fn):~/anaconda3/envs/fastai/lib/python3.6/codecs.py 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.
Hey,
I got the same error and could not work on the course. Help needed!
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!
So I am facing a similar problem. My GTX 960M is unable to run because it’s too old. I have downloaded the zip file from the Drive. May I ask how to install via conda?
Is there a some kind of map what GPU is work with what version of pytorch?
Just for reference, but the solution already suggested above by some people to conda update pytorch=0.4.0 will solve the ‘too old’ GPU, at least it did for my GTX960, I then get an out-of-memory error, but that’s a separate post 
I compiled my Pytorch from source. Tedious, but didn’t give me any CUDA errors.
Hi All,
I have windows machine with old GPU 960M
i am having problem with running fastai lesson 1
I get following error
RuntimeError: cuda runtime error (48) : no kernel image is available for execution on the device at c:\anaconda2\conda-bld\pytorch_1519501749874\work\torch\lib\thc\generic/THCTensorMath.cu:15
I tried following steps:
- i dont know how to install PyTorch 0.4.0
- i tried this solution (conda install -c peterjc123 pytorch)
nothing is working for me. please help.
thanks
Has anybody came across this error while compiling Pytorch from source?
[ 6%] Building CXX object third_party/protobuf/cmake/CMakeFiles/libprotobuf.dir/__/src/google/protobuf/wrappers.pb.cc.o
[ 6%] Linking CXX static library ../../../lib/libprotobuf.a
[ 6%] Built target libprotobuf
make: *** [Makefile:141: all] Error 2
Failed to run 'bash ../tools/build_pytorch_libs.sh --use-cuda --use-nnpack --use-mkldnn --use-qnnpack caffe2'
My GCC version is superior to 7 and I got some warnings.
My system is Fedora 29 and GPU Nvidia 940m.
I have CUDA and cudnn all ok and can run the CUDA examples.
For anyone running Fedora Linux I would reccomend you to read this articles:
This one covers installation of CUDA, Cudnn, Nccl and other libraries, setting up environment and variables and compiling Pytorch.
There is a very important point about GCC version: you need a specific (older) version of GCC and G++ covered by the article.
This will provide some support also:
http://gibbalog.blogspot.com/
I’m compiling PyTorch 0.4.1 with CUDA 10 for my mobile 940M… 69% done and crossing my fingers…
This is what ultimately fixed the message related to not working on my old card:
install pytorch cuda91 -c pytorch
I found it here Howto: installation on Windows
Right after that I found an Out of Memory on my card. Wrote about it here: https://medium.com/@andresesfm/fast-ai-deep-learning-class-notes-917a2e188e2d
1 Like
Thanks for the solution.
… and same here - Out of memory with my GTX750ti 2GB ram
probably time for a 1060 / 1070? or any work around?
Very well!
Finally I made it. To recap, when you see the warning of “PyTorch no longer supports this GPU because it is too old.”. Please perform the followings:
- Activate the virtual env fastai:
source activate fastai
- Uninstall pyTorch
conda uninstall pytorch
- Reinstall pyTorch
conda install -c pytorch pytorch
- Start the Jupyter Notebook and locate the line, for example in lesson1:
data = ImageClassifierData.from_paths(PATH, tfms=tfms_from_model(arch, sz), bs=16)
The extra parameter bs (batch size) let you specify a small process batch that can fit with your GPU memory. Here I take the value of 16 that is working well with my GTX750ti 2GB RAM.
It took me around 6 minutes to complete the 2 epochs 
Happy learning!!!