Можно ли тренировать модели Keras параллельно внутри al oop?

Question

Поток моей проблемы примерно такой. Я хочу обучать две отдельные модели параллельно (не обязательно параллельно), но параллельное обучение значительно ускорит мои циклы по актуальной проблеме) через равные промежутки времени (так называемые циклы здесь для простоты) на новых пакетах входящих данных.

Упрощенная схема задачи показана ниже. Я хотел знать, можно ли проводить индивидуальное обучение моделей Keras (1 и 2) параллельно, так как они не зависят друг от друга.

import numpy as np
from keras.models import Model
from keras.layers import Input, Dense, LSTM

"""ignore following 3 imports as they just illustrate in case someone wants to know
why I need to train in loops 
"""
# from stable_baselines.common.vec_env import SubprocVecEnv
# from stable_baselines.common import set_global_seeds, make_vec_env
# from mycustomfile import SomeImportedEnv, myRL_Agent


# representative data source coming in batches after regular 
# intervals of time for training model 1
def new_data_source_1():

    # generate test and train data for model 1
    X_train_model1 = np.random.rand(100,1,3)
    y_train_model1 = np.random.rand(100,1,2)
    X_test_model1 = np.random.rand(100,1,3)
    y_test_model1 = np.random.rand(100,1,2)

    return [X_train_model1, y_train_model1, X_test_model1,y_test_model1]


# representative data source coming in batches after regular 
# intervals of time for training model 2
def new_data_source_2():

    # generate test and train data for model 2
    X_train_model2 = np.random.rand(60,1,5)
    y_train_model2 = np.random.rand(60,1,2)
    X_test_model2 = np.random.rand(60,1,5)
    y_test_model2 = np.random.rand(60,1,2)

    return [X_train_model2, y_train_model2, X_test_model2, y_test_model2]


def create_keras_model(input_dim, outputdim, input_seq_length, output_seq_length):

    assert input_seq_length==output_seq_length, "This model can take input
       and output sequence of equal length only"

    input_layer = Input(batch_shape=(None, input_seq_length, input_dim))
    hidden_layer = LSTM(16, return_sequences = True)(input_layer)
    output_layer = Dense(outputdim, activation='relu')(hidden_layer)

    model = Model(inputs=input_layer, outputs=output_layer)

    model.compile(loss='mse', optimizer='adam')

    return model


"""What I am doing currently to train inside a loop, 2 Keras models, sequentially.
I want to make the model trainings parallel to save time."""
def main():

    model1_created = False
    model2_created = False

    """consider I have to train 10 incoming batches of data from source 1 and 2
    and I have to keep updating my 2 models regularly for subsequent use in a OpenAI Gym 
    environment"""
    for i in range(10):

        # get data from source 1
        X_train_model1, y_train_model1, X_test_model1,y_test_model1 = new_data_source_1()

        # create model for source 1
        if not model1_created:
            model1 = create_keras_model(X_train_model1.shape[-1], y_train_model1.shape[-1][-1],
                                    X_train_model1.shape[-2], y_train_model1.shape[-1][-2])
            model1_created = True

        # train the model1
        _ = model1.fit(X_train_model1, y_train_model1, epochs=100,  initial_epoch=0, 
                             batch_size=32, validation_data=(X_test_model1, y_test_model1))

        # save model1 weights for future use; reason: model1 is needed for 
        # subsequent use in another multiprocessing environment(see StableBaselines 
        # SubprocVecEnv) inside this main function but using the model1 directly 
        # leads to error
        model1.save('Latest_Model_1.hdf5')


        # get data from source 2
        X_train_model2, y_train_model2, X_test_model2,y_test_model2 = new_data_source_2()

        # create model for source 2
        if not model2_created:
            model2 = create_keras_model(X_train_model2.shape[-1], y_train_model2.shape[-1][-1],
                                    X_train_model2.shape[-2], y_train_model2.shape[-1][-2])
            model2_created = True

        # train the model2
        _ = model2.fit(X_train_model1, y_train_model1, epochs=100, initial_epoch=0, 
                             batch_size=32, validation_data=(X_test_model2, y_test_model2))

        # save model2 weights for future use; reason: model2 is needed for 
        # subsequent use in another multiprocessing environment(see StableBaselines 
        # SubprocVecEnv) inside this main function but using the model2 directly 
        # leads to error
        model2.save('Latest_Model_2.hdf5')

        """The following section is an informal representation of why I need
        the models 1 and 2; it is not related to solving my question. You can simply ignore 
        this from the main function in case you want to.
        I have seen people sometimes ask the rationale behind doing training in loops. This 
        entire main() function (with more augmentations above and below) will be running in 
        actual deployment without terminating.
        """
        # mycustomGymenv = SomeImportedEnv(model1path = 'Latest_Model_1.hdf5',
        #                                  model1path = 'Latest_Model_2.hdf5')
        # state = initial_state = mycustomGymenv.initial_state()
        # agent = myRL_Agent(mycustomGymenv)
        # for _ in range(100): # simulate for 100 time steps 
              # action = agent(state) # agent takes action
              # state, reward, over, etc = mycustomGymenv.simulate()  # env returns feedback
              # state, action - save to csv file etc,

if __name__ == '__main__':
    main()
    print("Done!")        

Любые предложения / изменения по улучшению ясности проблема приветствуется.