Using Keras model predict function from multiprocessing Pool

Question

I've seen some few similar posts on this topic, but none seem to address my issue.

I have trained a Keras model (CPU only) and want to call the predict function asynchronously using a multithreading.Pool. However, the call to predict just hangs. There is no exception thrown or anything. Calling it from the main thread works fine. I tried using model._make_predict_function() as suggested before, but this doesn't resolve this for me.

I've set up a Jupyter notebook to reproduce this (Keras==2.2.4, tensorflow==1.11.0):

In  [1]: from keras.models import Sequential
         from keras.layers import Dense
         from multiprocessing.pool import Pool

In  [2]: # Create sample model from Keras documentation
         model = Sequential()
         model.add(Dense(32, activation='relu', input_dim=100))
         model.add(Dense(1, activation='sigmoid'))
         model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy'])

         # Generate dummy data
         import numpy as np
         data = np.random.random((1000, 100))
         labels = np.random.randint(2, size=(1000, 1))

         # Train the model, iterating on the data in batches of 32 samples
         model.fit(data, labels, epochs=10, batch_size=32, verbose=0)

In  [3]: test_data = np.random.random((1,100))

         def predict(model, data):
             return model.predict(data)

         def do_predict(_=1):
             print('Prediction:', predict(model, test_data))
             print('Done')

In  [4]: do_predict()
Out [4]: Prediction: [[0.5553096]]
         Done

In  [5]: with Pool(1) as pool:
             pool.apply_async(do_predict, [1]).get()
             pool.close()
             pool.join()

At the last step it just hangs. Can anybody help me finding out what's going on here? Is it not possible to use predict asynchronously?