I am trying to run this notebook on Apple M1 (1st gen) running MacOS 12.4,
libs freeze:
>pip3 freeze
anyio @ file:///private/tmp/jupyterlab--anyio-20211211-70040-1yv1wmx/anyio-3.4.0
appnope==0.1.2
argon2-cffi @ file:///private/tmp/jupyterlab--argon2-cffi-20211211-70040-1er07d0/argon2-cffi-21.2.0
argon2-cffi-bindings @ file:///private/tmp/jupyterlab--argon2-cffi-bindings-20211211-70040-o64kwi/argon2-cffi-bindings-21.2.0
asttokens==2.0.5
attrs @ file:///private/tmp/jupyterlab--attrs-20211211-70040-6u3qxt/attrs-21.2.0
Babel==2.9.1
backcall @ file:///private/tmp/jupyterlab--backcall-20211211-70040-acdr42/backcall-0.2.0
beniget==0.4.1
black==21.12b0
bleach==4.1.0
certifi==2022.5.18.1
cffi==1.15.0
charset-normalizer==2.0.12
click==8.0.3
cycler==0.10.0
Cython==0.29.24
debugpy @ file:///private/tmp/jupyterlab--debugpy-20211211-70040-2j9lay/debugpy-1.5.1
decorator==5.1.0
defusedxml @ file:///private/tmp/jupyterlab--defusedxml-20211211-70040-uowur4/defusedxml-0.7.1
entrypoints @ file:///private/tmp/jupyterlab--entrypoints-20211211-70040-1r2y5g4/entrypoints-0.3
et-xmlfile==1.1.0
executing==0.8.2
finnhub-python==2.4.5
gast==0.5.2
GDAL==3.4.0
gensim==4.1.2
graphviz==0.19.1
idna==3.3
imageio==2.13.5
ipykernel==6.6.0
ipython==7.30.1
ipython-genutils==0.2.0
ipywidgets==7.6.5
jedi==0.18.1
Jinja2==3.0.3
joblib==1.1.0
json5==0.9.6
jsonschema @ file:///private/tmp/jupyterlab--jsonschema-20211211-70040-1np642r/jsonschema-4.2.1
jupyter==1.0.0
jupyter-client==7.1.0
jupyter-console==6.4.0
jupyter-core==4.9.1
jupyter-server @ file:///private/tmp/jupyterlab--jupyter-server-20211211-70040-1u7h7vl/jupyter_server-1.13.1
jupyterlab @ file:///private/tmp/jupyterlab-20211211-70040-1ltrjpx/jupyterlab-3.2.5
jupyterlab-pygments==0.1.2
jupyterlab-server @ file:///private/tmp/jupyterlab--jupyterlab-server-20211211-70040-iufjhi/jupyterlab_server-2.8.2
jupyterlab-widgets==1.0.2
kiwisolver==1.3.2
lxml==4.6.3
MarkupSafe==2.0.1
matplotlib==3.4.3
matplotlib-inline==0.1.3
midi @ git+https://github.com/vishnubob/python-midi.git@abb85028c97b433f74621be899a0b399cd100aaa
midi-to-dataframe @ git+https://github.com/TaylorPeer/midi-to-dataframe@35347f787f01a2326234ad278d8c40bee3817f1d
mido==1.2.10
mistune==0.8.4
multitasking==0.0.9
mypy-extensions==0.4.3
nbclassic @ file:///private/tmp/jupyterlab--nbclassic-20211211-70040-1fah2fe/nbclassic-0.3.4
nbclient @ file:///private/tmp/jupyterlab--nbclient-20211211-70040-ptwp5d/nbclient-0.5.9
nbconvert==6.3.0
nbformat==5.1.3
nest-asyncio @ file:///private/tmp/jupyterlab--nest-asyncio-20211211-70040-72pz5e/nest_asyncio-1.5.4
networkx==2.6.3
notebook==6.4.6
numpy==1.23.0rc1
openpyxl==3.0.9
packaging @ file:///private/tmp/jupyterlab--packaging-20211211-70040-1f14ddt/packaging-21.3
pandas==1.4.2
pandocfilters==1.5.0
parso==0.8.3
pathspec==0.9.0
pexpect==4.8.0
pickleshare==0.7.5
Pillow==9.1.1
platformdirs==2.4.1
ply==3.11
prometheus-client==0.12.0
prompt-toolkit @ file:///private/tmp/jupyterlab--prompt-toolkit-20211211-70040-hcpjwc/prompt_toolkit-3.0.24
ptyprocess @ file:///private/tmp/jupyterlab--ptyprocess-20211211-70040-wjbvpa/ptyprocess-0.7.0
pure-eval==0.2.1
pybind11==2.8.0
pycparser==2.21
Pygments==2.10.0
pyparsing==3.0.6
pyrsistent @ file:///private/tmp/jupyterlab--pyrsistent-20211211-70040-1fnadg/pyrsistent-0.18.0
python-dateutil==2.8.2
pythran==0.10.0
pytz==2022.1
PyWavelets==1.2.0
PyYAML==6.0
pyzmq @ file:///private/tmp/jupyterlab--pyzmq-20211211-70040-2xtuon/pyzmq-22.3.0
qtconsole==5.2.2
QtPy==2.0.0
requests==2.27.1
scikit-image==0.19.1
scikit-learn==1.1.dev0
scipy==1.8.1
seaborn==0.11.2
Send2Trash==1.8.0
six==1.16.0
smart-open==5.2.1
sniffio @ file:///private/tmp/jupyterlab--sniffio-20211211-70040-wu3dri/sniffio-1.2.0
squarify==0.4.3
stack-data==0.1.4
terminado @ file:///private/tmp/jupyterlab--terminado-20211211-70040-dw1vl6/terminado-0.12.1
testpath @ file:///private/tmp/jupyterlab--testpath-20211211-70040-895z1/testpath-0.5.0
threadpoolctl==3.0.0
tifffile==2021.11.2
tomli==1.2.3
torch==1.13.0.dev20220528
torchaudio==0.11.0
torchsummary==1.5.1
torchtext==0.10.0
torchvision==0.14.0a0+f0f8a3c
torchviz==0.0.2
tornado==6.1
tqdm==4.62.3
traitlets @ file:///private/tmp/jupyterlab--traitlets-20211211-70040-ru76xv/traitlets-5.1.1
typing_extensions==4.2.0
urllib3==1.26.9
wcwidth==0.2.5
webencodings==0.5.1
websocket-client==1.2.3
wget==3.2
widgetsnbextension==3.5.2
yfinance==0.1.64
in the code , am setting device = torch.device('mps')
at this line: history = [evaluate(model, valid_dl)] am getting runtime error
Input type (MPSFloatType) and weight type (torch.FloatTensor) should be the same
Trace:
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<timed exec> in <module>
/opt/homebrew/Cellar/jupyterlab/3.2.5/libexec/lib/python3.9/site-packages/torch/autograd/grad_mode.py in decorate_context(*args, **kwargs)
25 def decorate_context(*args, **kwargs):
26 with self.clone():
---> 27 return func(*args, **kwargs)
28 return cast(F, decorate_context)
29
/var/folders/mz/qfpvpvf550s039lrnxg70whh0000gn/T/ipykernel_11483/1143432410.py in evaluate(model, val_loader)
3 def evaluate(model, val_loader):
4 model.eval()
----> 5 outputs = [model.validation_step(batch) for batch in val_loader]
6 return model.validation_epoch_end(outputs)
7
/var/folders/mz/qfpvpvf550s039lrnxg70whh0000gn/T/ipykernel_11483/1143432410.py in <listcomp>(.0)
3 def evaluate(model, val_loader):
4 model.eval()
----> 5 outputs = [model.validation_step(batch) for batch in val_loader]
6 return model.validation_epoch_end(outputs)
7
/var/folders/mz/qfpvpvf550s039lrnxg70whh0000gn/T/ipykernel_11483/446280773.py in validation_step(self, batch)
16 def validation_step(self, batch):
17 images, labels = batch
---> 18 out = self(images) # Generate prediction
19 loss = F.cross_entropy(out, labels) # Calculate loss
20 acc = accuracy(out, labels) # Calculate accuracy
/opt/homebrew/Cellar/jupyterlab/3.2.5/libexec/lib/python3.9/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1128 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1129 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1130 return forward_call(*input, **kwargs)
1131 # Do not call functions when jit is used
1132 full_backward_hooks, non_full_backward_hooks = [], []
/var/folders/mz/qfpvpvf550s039lrnxg70whh0000gn/T/ipykernel_11483/3789274317.py in forward(self, xb)
29
30 def forward(self, xb): # xb is the loaded batch
---> 31 out = self.conv1(xb)
32 out = self.conv2(out)
33 out = self.res1(out) + out
/opt/homebrew/Cellar/jupyterlab/3.2.5/libexec/lib/python3.9/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1128 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1129 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1130 return forward_call(*input, **kwargs)
1131 # Do not call functions when jit is used
1132 full_backward_hooks, non_full_backward_hooks = [], []
/opt/homebrew/Cellar/jupyterlab/3.2.5/libexec/lib/python3.9/site-packages/torch/nn/modules/container.py in forward(self, input)
137 def forward(self, input):
138 for module in self:
--> 139 input = module(input)
140 return input
141
/opt/homebrew/Cellar/jupyterlab/3.2.5/libexec/lib/python3.9/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1128 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1129 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1130 return forward_call(*input, **kwargs)
1131 # Do not call functions when jit is used
1132 full_backward_hooks, non_full_backward_hooks = [], []
/opt/homebrew/Cellar/jupyterlab/3.2.5/libexec/lib/python3.9/site-packages/torch/nn/modules/conv.py in forward(self, input)
457
458 def forward(self, input: Tensor) -> Tensor:
--> 459 return self._conv_forward(input, self.weight, self.bias)
460
461 class Conv3d(_ConvNd):
/opt/homebrew/Cellar/jupyterlab/3.2.5/libexec/lib/python3.9/site-packages/torch/nn/modules/conv.py in _conv_forward(self, input, weight, bias)
453 weight, bias, self.stride,
454 _pair(0), self.dilation, self.groups)
--> 455 return F.conv2d(input, weight, bias, self.stride,
456 self.padding, self.dilation, self.groups)
457
RuntimeError: Input type (MPSFloatType) and weight type (torch.FloatTensor) should be the same
MPS is still new and am trying to figure out the cause here, any suggestions are welcome, the code runs fine if torch device is set to CPU - just takes so much time.
Thanks, Deep Kamal Singh