[Solved]Error while deploying the model on GCP

Hello all,
While trying to deploy my trained model on GCP, I am getting an error regarding weights not matching. The error I am getting is as follows:-

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Traceback (most recent call last):
File “app/server.py”, line 37, in
learn = loop.run_until_complete(asyncio.gather(*tasks))[0]
File “/usr/local/lib/python3.6/asyncio/base_events.py”, line 484, in run_until_complete
return future.result()
File “app/server.py”, line 32, in setup_learner
learn.load(model_file_name)
File “/usr/local/lib/python3.6/site-packages/fastai/basic_train.py”, line 248, in load
get_model(self.model).load_state_dict(state, strict=strict)
File “/usr/local/lib/python3.6/site-packages/torch/nn/modules/module.py”, line 769, in load_state_dict
self. class . name , “\n\t”.join(error_msgs)))
RuntimeError: Error(s) in loading state_dict for Sequential:
Missing key(s) in state_dict: “0.0.weight”, “0.1.weight”, “0.1.bias”, “0.1.running_mean”, “0.1.running_var”, “0.4.0.conv1.weight”, “0.4.0.bn1.weight”, “0.4.0.bn1.bias”, “0.4.0.bn1.running_mean”, “0.4.0.bn1.running_var”, “0.4.0.conv2.weight”, “0.4.0.bn2.weight”, “0.4.0.bn2.bias”, “0.4.0.bn2.running_mean”, “0.4.0.bn2.running_var”, “0.4.1.conv1.weight”, “0.4.1.bn1.weight”, “0.4.1.bn1.bias”, “0.4.1.bn1.running_mean”, “0.4.1.bn1.running_var”, “0.4.1.conv2.weight”, “0.4.1.bn2.weight”, “0.4.1.bn2.bias”, “0.4.1.bn2.running_mean”, “0.4.1.bn2.running_var”, “0.4.2.conv1.weight”, “0.4.2.bn1.weight”, “0.4.2.bn1.bias”, “0.4.2.bn1.running_mean”, “0.4.2.bn1.running_var”, “0.4.2.conv2.weight”, “0.4.2.bn2.weight”, “0.4.2.bn2.bias”, “0.4.2.bn2.running_mean”, “0.4.2.bn2.running_var”, “0.5.0.conv1.weight”, “0.5.0.bn1.weight”, “0.5.0.bn1.bias”, “0.5.0.bn1.running_mean”, “0.5.0.bn1.running_var”, “0.5.0.conv2.weight”, “0.5.0.bn2.weight”, “0.5.0.bn2.bias”, “0.5.0.bn2.running_mean”, “0.5.0.bn2.running_var”, “0.5.0.downsample.0.weight”, “0.5.0.downsample.1.weight”, “0.5.0.downsample.1.bias”, “0.5.0.downsample.1.running_mean”, “0.5.0.downsample.1.running_var”, “0.5.1.conv1.weight”, “0.5.1.bn1.weight”, “0.5.1.bn1.bias”, “0.5.1.bn1.running_mean”, “0.5.1.bn1.running_var”, “0.5.1.conv2.weight”, “0.5.1.bn2.weight”, “0.5.1.bn2.bias”, “0.5.1.bn2.running_mean”, “0.5.1.bn2.running_var”, “0.5.2.conv1.weight”, “0.5.2.bn1.weight”, “0.5.2.bn1.bias”, “0.5.2.bn1.running_mean”, “0.5.2.bn1.running_var”, “0.5.2.conv2.weight”, “0.5.2.bn2.weight”, “0.5.2.bn2.bias”, “0.5.2.bn2.running_mean”, “0.5.2.bn2.running_var”, “0.5.3.conv1.weight”, “0.5.3.bn1.weight”, “0.5.3.bn1.bias”, “0.5.3.bn1.running_mean”, “0.5.3.bn1.running_var”, “0.5.3.conv2.weight”, “0.5.3.bn2.weight”, “0.5.3.bn2.bias”, “0.5.3.bn2.running_mean”, “0.5.3.bn2.running_var”, “0.6.0.conv1.weight”, “0.6.0.bn1.weight”, “0.6.0.bn1.bias”, “0.6.0.bn1.running_mean”, “0.6.0.bn1.running_var”, “0.6.0.conv2.weight”, “0.6.0.bn2.weight”, “0.6.0.bn2.bias”, “0.6.0.bn2.running_mean”, “0.6.0.bn2.running_var”, “0.6.0.downsample.0.weight”, “0.6.0.downsample.1.weight”, “0.6.0.downsample.1.bias”, “0.6.0.downsample.1.running_mean”, “0.6.0.downsample.1.running_var”, “0.6.1.conv1.weight”, “0.6.1.bn1.weight”, “0.6.1.bn1.bias”, “0.6.1.bn1.running_mean”, “0.6.1.bn1.running_var”, “0.6.1.conv2.weight”, “0.6.1.bn2.weight”, “0.6.1.bn2.bias”, “0.6.1.bn2.running_mean”, “0.6.1.bn2.running_var”, “0.6.2.conv1.weight”, “0.6.2.bn1.weight”, “0.6.2.bn1.bias”, “0.6.2.bn1.running_mean”, “0.6.2.bn1.running_var”, “0.6.2.conv2.weight”, “0.6.2.bn2.weight”, “0.6.2.bn2.bias”, “0.6.2.bn2.running_mean”, “0.6.2.bn2.running_var”, “0.6.3.conv1.weight”, “0.6.3.bn1.weight”, “0.6.3.bn1.bias”, “0.6.3.bn1.running_mean”, “0.6.3.bn1.running_var”, “0.6.3.conv2.weight”, “0.6.3.bn2.weight”, “0.6.3.bn2.bias”, “0.6.3.bn2.running_mean”, “0.6.3.bn2.running_var”, “0.6.4.conv1.weight”, “0.6.4.bn1.weight”, “0.6.4.bn1.bias”, “0.6.4.bn1.running_mean”, “0.6.4.bn1.running_var”, “0.6.4.conv2.weight”, “0.6.4.bn2.weight”, “0.6.4.bn2.bias”, “0.6.4.bn2.running_mean”, “0.6.4.bn2.running_var”, “0.6.5.conv1.weight”, “0.6.5.bn1.weight”, “0.6.5.bn1.bias”, “0.6.5.bn1.running_mean”, “0.6.5.bn1.running_var”, “0.6.5.conv2.weight”, “0.6.5.bn2.weight”, “0.6.5.bn2.bias”, “0.6.5.bn2.running_mean”, “0.6.5.bn2.running_var”, “0.7.0.conv1.weight”, “0.7.0.bn1.weight”, “0.7.0.bn1.bias”, “0.7.0.bn1.running_mean”, “0.7.0.bn1.running_var”, “0.7.0.conv2.weight”, “0.7.0.bn2.weight”, “0.7.0.bn2.bias”, “0.7.0.bn2.running_mean”, “0.7.0.bn2.running_var”, “0.7.0.downsample.0.weight”, “0.7.0.downsample.1.weight”, “0.7.0.downsample.1.bias”, “0.7.0.downsample.1.running_mean”, “0.7.0.downsample.1.running_var”, “0.7.1.conv1.weight”, “0.7.1.bn1.weight”, “0.7.1.bn1.bias”, “0.7.1.bn1.running_mean”, “0.7.1.bn1.running_var”, “0.7.1.conv2.weight”, “0.7.1.bn2.weight”, “0.7.1.bn2.bias”, “0.7.1.bn2.running_mean”, “0.7.1.bn2.running_var”, “0.7.2.conv1.weight”, “0.7.2.bn1.weight”, “0.7.2.bn1.bias”, “0.7.2.bn1.running_mean”, “0.7.2.bn1.running_var”, “0.7.2.conv2.weight”, “0.7.2.bn2.weight”, “0.7.2.bn2.bias”, “0.7.2.bn2.running_mean”, “0.7.2.bn2.running_var”, “1.2.weight”, “1.2.bias”, “1.2.running_mean”, “1.2.running_var”, “1.4.weight”, “1.4.bias”, “1.6.weight”, “1.6.bias”, “1.6.running_mean”, “1.6.running_var”, “1.8.weight”, “1.8.bias”.
Unexpected key(s) in state_dict: “conv1.weight”, “bn1.running_mean”, “bn1.running_var”, “bn1.weight”, “bn1.bias”, “layer1.0.conv1.weight”, “layer1.0.bn1.running_mean”, “layer1.0.bn1.running_var”, “layer1.0.bn1.weight”, “layer1.0.bn1.bias”, “layer1.0.conv2.weight”, “layer1.0.bn2.running_mean”, “layer1.0.bn2.running_var”, “layer1.0.bn2.weight”, “layer1.0.bn2.bias”, “layer1.1.conv1.weight”, “layer1.1.bn1.running_mean”, “layer1.1.bn1.running_var”, “layer1.1.bn1.weight”, “layer1.1.bn1.bias”, “layer1.1.conv2.weight”, “layer1.1.bn2.running_mean”, “layer1.1.bn2.running_var”, “layer1.1.bn2.weight”, “layer1.1.bn2.bias”, “layer2.0.conv1.weight”, “layer2.0.bn1.running_mean”, “layer2.0.bn1.running_var”, “layer2.0.bn1.weight”, “layer2.0.bn1.bias”, “layer2.0.conv2.weight”, “layer2.0.bn2.running_mean”, “layer2.0.bn2.running_var”, “layer2.0.bn2.weight”, “layer2.0.bn2.bias”, “layer2.0.downsample.0.weight”, “layer2.0.downsample.1.running_mean”, “layer2.0.downsample.1.running_var”, “layer2.0.downsample.1.weight”, “layer2.0.downsample.1.bias”, “layer2.1.conv1.weight”, “layer2.1.bn1.running_mean”, “layer2.1.bn1.running_var”, “layer2.1.bn1.weight”, “layer2.1.bn1.bias”, “layer2.1.conv2.weight”, “layer2.1.bn2.running_mean”, “layer2.1.bn2.running_var”, “layer2.1.bn2.weight”, “layer2.1.bn2.bias”, “layer3.0.conv1.weight”, “layer3.0.bn1.running_mean”, “layer3.0.bn1.running_var”, “layer3.0.bn1.weight”, “layer3.0.bn1.bias”, “layer3.0.conv2.weight”, “layer3.0.bn2.running_mean”, “layer3.0.bn2.running_var”, “layer3.0.bn2.weight”, “layer3.0.bn2.bias”, “layer3.0.downsample.0.weight”, “layer3.0.downsample.1.running_mean”, “layer3.0.downsample.1.running_var”, “layer3.0.downsample.1.weight”, “layer3.0.downsample.1.bias”, “layer3.1.conv1.weight”, “layer3.1.bn1.running_mean”, “layer3.1.bn1.running_var”, “layer3.1.bn1.weight”, “layer3.1.bn1.bias”, “layer3.1.conv2.weight”, “layer3.1.bn2.running_mean”, “layer3.1.bn2.running_var”, “layer3.1.bn2.weight”, “layer3.1.bn2.bias”, “layer4.0.conv1.weight”, “layer4.0.bn1.running_mean”, “layer4.0.bn1.running_var”, “layer4.0.bn1.weight”, “layer4.0.bn1.bias”, “layer4.0.conv2.weight”, “layer4.0.bn2.running_mean”, “layer4.0.bn2.running_var”, “layer4.0.bn2.weight”, “layer4.0.bn2.bias”, “layer4.0.downsample.0.weight”, “layer4.0.downsample.1.running_mean”, “layer4.0.downsample.1.running_var”, “layer4.0.downsample.1.weight”, “layer4.0.downsample.1.bias”, “layer4.1.conv1.weight”, “layer4.1.bn1.running_mean”, “layer4.1.bn1.running_var”, “layer4.1.bn1.weight”, “layer4.1.bn1.bias”, “layer4.1.conv2.weight”, “layer4.1.bn2.running_mean”, “layer4.1.bn2.running_var”, “layer4.1.bn2.weight”, “layer4.1.bn2.bias”, “fc.weight”, “fc.bias”.
The command ‘/bin/sh -c python app/server.py’ returned a non-zero code: 1
ERROR
ERROR: build step 0 “gcr.io/cloud-builders/docker” failed: exit status 1

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I am using Fastai- version 3. I have used resnet18 model.
I am not able to get around this error. Any help would be appreciated.

Thanks,
Rajat

Solved.!! by wrapping the model in nn.DataParallel before saving it.