#on loading model with, Path having export.pkl file.
learn = load_learner(Path)
#on executing pet_classification.py and saving service
service = NormAbnormClassification.pack(export=learn)
saved_path = service.save()

I am getting following error on trying to predict result for an image.png file. File is of dimension 256x256 and model was also trained on same dimension file.
RuntimeError Traceback (most recent call last)
in
1 service = load(saved_path)
2
----> 3 print(service.predict(data.get(0)))

~/Richesh/AfterOctober2018/Notebooks/RnDNotebooksAndPythonScripts/normAbnormClassifier.py in predict(self, image)
11 def predict(self, image):
12 #result = self.artifacts.pet_classifer.predict(image)
---> 13 result = self.artifacts.export.predict(image)
14 return str(result)

~/anaconda3/envs/fastai/lib/python3.6/site-packages/fastai/basic_train.py in predict(self, item, return_x, batch_first, with_dropout, **kwargs)
369 "Return predicted class, label and probabilities for item."
370 batch = self.data.one_item(item)
--> 371 res = self.pred_batch(batch=batch, with_dropout=with_dropout)
372 raw_pred,x = grab_idx(res,0,batch_first=batch_first),batch[0]
373 norm = getattr(self.data,'norm',False)

~/anaconda3/envs/fastai/lib/python3.6/site-packages/fastai/basic_train.py in pred_batch(self, ds_type, batch, reconstruct, with_dropout, activ)
348 activ = ifnone(activ, _loss_func2activ(self.loss_func))
349 with torch.no_grad():
--> 350 if not with_dropout: preds = loss_batch(self.model.eval(), xb, yb, cb_handler=cb_handler)
351 else: preds = loss_batch(self.model.eval().apply(self.apply_dropout), xb, yb, cb_handler=cb_handler)
352 res = activ(preds[0])

~/anaconda3/envs/fastai/lib/python3.6/site-packages/fastai/basic_train.py in loss_batch(model, xb, yb, loss_func, opt, cb_handler)
24 if not is_listy(xb): xb = [xb]
25 if not is_listy(yb): yb = [yb]
---> 26 out = model(*xb)
27 out = cb_handler.on_loss_begin(out)
28

~/anaconda3/envs/fastai/lib/python3.6/site-packages/torch/nn/modules/module.py in call(self, *input, **kwargs)
539 result = self._slow_forward(*input, **kwargs)
540 else:
--> 541 result = self.forward(*input, **kwargs)
542 for hook in self._forward_hooks.values():
543 hook_result = hook(self, input, result)

~/anaconda3/envs/fastai/lib/python3.6/site-packages/torch/nn/modules/container.py in forward(self, input)
90 def forward(self, input):
91 for module in self._modules.values():
---> 92 input = module(input)
93 return input
94

~/anaconda3/envs/fastai/lib/python3.6/site-packages/torch/nn/modules/module.py in call(self, *input, **kwargs)
539 result = self._slow_forward(*input, **kwargs)
540 else:
--> 541 result = self.forward(*input, **kwargs)
542 for hook in self._forward_hooks.values():
543 hook_result = hook(self, input, result)

~/anaconda3/envs/fastai/lib/python3.6/site-packages/torch/nn/modules/container.py in forward(self, input)
90 def forward(self, input):
91 for module in self._modules.values():
---> 92 input = module(input)
93 return input
94

~/anaconda3/envs/fastai/lib/python3.6/site-packages/torch/nn/modules/module.py in call(self, *input, **kwargs)
539 result = self._slow_forward(*input, **kwargs)
540 else:
--> 541 result = self.forward(*input, **kwargs)
542 for hook in self._forward_hooks.values():
543 hook_result = hook(self, input, result)

~/anaconda3/envs/fastai/lib/python3.6/site-packages/torch/nn/modules/conv.py in forward(self, input)
343
344 def forward(self, input):
--> 345 return self.conv2d_forward(input, self.weight)
346
347 class Conv3d(_ConvNd):

~/anaconda3/envs/fastai/lib/python3.6/site-packages/torch/nn/modules/conv.py in conv2d_forward(self, input, weight)
340 _pair(0), self.dilation, self.groups)
341 return F.conv2d(input, weight, self.bias, self.stride,
--> 342 self.padding, self.dilation, self.groups)
343
344 def forward(self, input):

RuntimeError: Input type (torch.cuda.FloatTensor) and weight type (torch.cuda.HalfTensor) should be the same

Hello there!
I am trying to run the Fast AI Pet Image classification example but I'm facing the following issue

Please check it and let me know the solution for it to run. Thanks in advance.

• e2e
• fast-ai
• fasttext
• h2o
• legacy-keras
• lightbgm
• onnx
• pytorch
• sklearn
• spacy
• statsmodels_holt
• tensorflow
• xgboost

The links on the home page don't work.

I am new to this topic and exploring the example. By following all the instructions I am facing following issue:
Traceback (most recent call last):
File "/Users/awais/Python_Projects/TextClasificationTest/import_service.py", line 10, in
bento_svc = serviceClass.pack(model=model,word_index=word_index)
TypeError: pack() missing 1 required positional argument: 'name'
where is this missing argument 'name' and from where it has to pass? Please guide me on it. Thanks

Attempting to run pet image classification sample. Get error below. Can't find FastaiImageInput in bentoml.adapters code. Got "getting started" notebook to run. Using fresh env with only BentoML requirements.

Is it just the notebook that needs to be updated?

ImportError Traceback (most recent call last)
in
1 # 1) import the custom BentoService defined above
----> 2 from pet_classification import PetClassification
3
4 # 2) pack it with required artifacts
5 service = PetClassification()

/workspace/BentoML/gallery/fast-ai/pet-image-classification/pet_classification.py in
2 from bentoml import BentoService, api, env, artifacts
3 from bentoml.artifact import FastaiModelArtifact
----> 4 from bentoml.adapters import FastaiImageInput
5
6 @env(pip_dependencies=['fastai'])

ImportError: cannot import name 'FastaiImageInput' from 'bentoml.adapters' (/opt/conda/lib/python3.7/site-packages/bentoml/adapters/init.py)

Hi team,

I just tried the example in https://github.com/bentoml/gallery/blob/master/paddlehub/image-segmentation/image-segmentation.ipynb , only I ran the code in Pycharm Python Console (not jupyterbook).

Everything went fine till

# Start a dev model server to test out everything
bento_svc.start_dev_server()

I could get correct results in postman, the code was like

curl --location --request POST 'http://0.0.0.0:5000/predict' \
--header 'Content-Type: image/jpeg' \
--data-binary '@test_image.jpg'

But the code bento_svc.stop_dev_server() made Pycharm quit. Which was not expected. It should only stop bento_svc.

Another issue is that after saving the service with saved_path = bento_svc.save(), I could lauch API model serving with bentoml serve PaddleHubService:latest, but when I sent request with postman, it returned Service Unavailabel. However, in the meantime, bentoml run PaddleHubService:latest predict --input-file test_image.jpg returned the correct results.

One more thing, it looks like you are using original Flask as the rest api server, is it a good choice for production services?

Thanks a lot！

https://docs.bentoml.org/en/0.13-lts/examples.html

With the release of 0.8.4 BentoML supports --run-with-ngrok which makes it easy to test notebooks in colab. We should update all the applicable examples to use this.

Also, add ngrok to documentation.