Tensorflow 2.0 save trained model for serving

Question

Please help me. I am using Tensorflow 2.0 GPU. I train the model and save in.h5 format

model = keras.Sequential()
model.add(layers.Bidirectional(layers.CuDNNLSTM(self._window_size, return_sequences=True),
                               input_shape=(self._window_size, x_train.shape[-1])))
model.add(layers.Dropout(rate=self._dropout, seed=self._seed))
model.add(layers.Bidirectional(layers.CuDNNLSTM((self._window_size * 2), return_sequences=True)))
model.add(layers.Dropout(rate=self._dropout, seed=self._seed))
model.add(layers.Bidirectional(layers.CuDNNLSTM(self._window_size, return_sequences=False)))
model.add(layers.Dense(units=1))
model.add(layers.Activation('linear'))
model.summary()

model.compile(
    loss='mean_squared_error',
    optimizer='adam'
)
# обучаем модель
history = model.fit(
    x_train,
    y_train,
    epochs=self._epochs,
    batch_size=self._batch_size,
    shuffle=False,
    validation_split=0.1
)

model.save('rts.h5')

Then I load this model and use it for forecasting and everything works.

model = keras.models.load_model('rts.h5')
y_hat = model.predict(x_test)

But the question arose of using a trained model in Tensorflow Serving. And the model in.h5 format is not accepted. I run:

sudo docker run --gpus 1 -p 8501:8501 --mount type=bind,source=/home/alex/PycharmProjects/TensorflowServingTestData/RtsModel,target=/models/rts_model -e MODEL_NAME=rts_model -t tensorflow/serving:latest-gpu

But the question arose of using a trained model in Tensorflow Serving. And the model in.h5 format is not accepted. I run: And I get the error:

tensorflow_serving/sources/storage_path/file_system_storage_path_source.cc:267] No versions of servable rts_model found under base path /models/rts_model

I try to save the trained model as described here, https://www.tensorflow.org/guide/saved_model#using_savedmodel_with_estimators :

And I get the error:

ValueError: Layer has 2 states but was passed 0 initial states.

I tried to save the model as follows, https://www.tensorflow.org/api_docs/python/tf/keras/models/save_model :

And have the same error:

ValueError: Layer has 2 states but was passed 0 initial states.

The only thing that works to save the model in the format for Tensorflow Serving is:

keras.experimental.export_saved_model(model, 'saved_model/1/')

Saved model work in Serving. But I get a warning that this method is deprecated and will be removed in a future version.

Instructions for updating:
Please use `model.save(..., save_format="tf")` or `tf.keras.models.save_model(..., save_format="tf")`.

And it closed me. When I try to use these methods, it gives an error. When I use what works, writes that it is deprecated.

Please, help. How to save a trained model in Tensorflow 2.0. so that it can be used for Tensorflow Serving.

Answer 1

I was trying to fix this too!

According to the answer here the normal LSTM (ie tf.keras.layers.LSTM ) will use GPU, and should be used in general over the cuDNNLSTM class unless you specifically need the original implementation (not sure why you would).

According todocs the normal LSTM will use cuDNN implementation if some requirements are met (see below).

When using this LSTM layer, I could successfully save to the tf output type, just using model.save_model('output_path', save_format='tf')

Requirements for LSTM using cuDNN are as follows (note that all the requirements are met with the defaults):

If a GPU is available and all the arguments to the layer meet the requirement of the CuDNN kernel (see below for details), the layer will use a fast cuDNN implementation.

The requirements to use the cuDNN implementation are:

1. activation == tanh
2. recurrent_activation == sigmoid
3. recurrent_dropout == 0
4. unroll is False
5. use_bias is True Inputs are not masked or strictly right padded.