the-ai-summer / deep-learning-in-production Goto Github PK

Hello,

Thanks for putting together such a nice and detailed tutorial on DL in production, I have learned a lot and it is clear you put a lot of effort in making everything concrete and clear.

I am now trying to solve an issue with the simple Flask application tutorial. After writing all the code, I always get a <Response [200]> error at the client side. I have debugged everything in the server and client side and all seems to be working: the input image from the client is correctly read and preprocessed, the network outputs correct values, but when the client gets a response I always get that error. The only message that appears on the server side is 127.0.0.1 - - [28/Mar/2021 10:17:00] "POST /infer HTTP/1.1" 200 -

I have even tried return "hello" in my infer method (inferrer.py) and the infer method (service.py) to make sure the return value is json serializable, but to no avail.

Do you have any suggestions on how to continue debugging? Thank you.

Hi @black0017 @SergiosKar
When i run this code from the article "Deploy a Deep Learning model as a web application using Flask and Tensorflow". I have a issue:
ModuleNotFoundError: No module named 'tensorflow_examples'
What should i do?
Best regards,
PeterPham

Hi @SergiosKar @black0017 ,
How to run the code for demo?
Best regards,
PeterPham

I have an issue with the following functions. "dummy_load_data" loads the dummy dataset.

#function of the module: model.tests.unet_test
  def dummy_load_data(*args, **kwargs):
      with tfds.testing.mock_data(num_examples=1):
          return tfds.load(CFG['data']['path'], with_info=True)

#function of the class: model.tests.unet_test.UnetTest
   @patch('model.unet.DataLoader.load_data')
    def test_load_data(self, mock_data_loader):
        mock_data_loader.side_effect = dummy_load_data
        shape = tf.TensorShape([None, self.unet.image_size, self.unet.image_size, 3])

        self.unet.load_data()
        mock_data_loader.assert_called()

        self.assertItemsEqual(self.unet.train_dataset.element_spec[0].shape, shape)
        self.assertItemsEqual(self.unet.test_dataset.element_spec[0].shape, shape)

#functions (which are supposed to be tested with test_load_data function above) "load_data" and  "_preprocess_data" of the class: model.unet.Unet 
    def load_data(self):
            """Loads and Preprocess data """
            self.dataset, self.info = DataLoader().load_data(self.config.data)
            self._preprocess_data()
    
    def _preprocess_data(self):
            """ Splits into training and test and set training parameters"""
            train = self.dataset['train'].map(self._load_image_train, num_parallel_calls=tf.data.experimental.AUTOTUNE)
            test = self.dataset['test'].map(self._load_image_test)
    
            self.train_dataset = train.cache().shuffle(self.buffer_size).batch(self.batch_size).repeat()
            self.train_dataset = self.train_dataset.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)
            self.test_dataset = test.batch(self.batch_size)

I want to test the load_data function of the class model.unet by using a dummy data , which is loaded with the function
dummy_load_data. (above)

When I´m testing the function "load_data" of the class model.unet, I get the following error:
train = self.dataset["train"].map(map_func =self._load_image_train, num_parallel_calls=tf.data.AUTOTUNE) KeyError: 'train'

I think its because the dummy_load_data function just loads one sample. So the returned dataset of the dummy_load_data function doesnt have the keys ["train"] and ["test"]. Therefore its not possible to access these keys in the _preprocess_data function.

But how can I create a mock dataset with the keys ["train"] and ["test"]?

It seems like the data loader is not being used in the model.py. Specificially, the preprocess_train and preprecess_data have been defined again in the model.py. I would appreciate little clarification

https://github.com/The-AI-Summer/Deep-Learning-In-Production/blob/master/10.%20How%20to%20use%20Docker%20containers%20and%20Docker%20Compose%20for%20Deep%20Learning%20applications/dataloader/dataloader.py#L11