n_jobs = -1 equivalent in keras I have recently started learning deep learning. In machine learning using sklearn library with n_jobs = -1 all my cpu cores are used and this speeds the grid search. Now I am trying to fit an rnn model on training data, which is taking a lot of time. Is there a way I can speed up the training?

# Initialising the RNN
regressor = Sequential()

# Adding the first LSTM layer and some Dropout regularisation
regressor.add(LSTM(units = 50, return_sequences = True, input_shape = (X_train.shape[1], 7)))
regressor.add(Dropout(0.2))

# Adding a second LSTM layer and some Dropout regularisation
regressor.add(LSTM(units = 50, return_sequences = True))
regressor.add(Dropout(0.2))

# Adding a third LSTM layer and some Dropout regularisation
regressor.add(LSTM(units = 50, return_sequences = True))
regressor.add(Dropout(0.2))

# Adding a fourth LSTM layer and some Dropout regularisation
regressor.add(LSTM(units = 50))
regressor.add(Dropout(0.2))

# Adding the output layer
regressor.add(Dense(units = 1))

# Compiling the RNN
regressor.compile(optimizer = 'adam', loss = 'mean_squared_error')

# Fitting the RNN to the Training set
regressor.fit(X_train, y_train, epochs = 100, batch_size = 32,shuffle=False)
Here you do a single fit() of the model whose name tells for itself - Sequential. 

Unless you are doing cross-validation or some kind of distributed learning with multiple models, there is no benefit of running several fits in parallel.

However, you can have significant speed-up on iteration level, depending on how Keras backend is configured to work with your hardware.

Assuming you are using Keras with the tensorflow backend (default):


If you are running on CPU, tensorflow should already pick up all available cores by default (if not, it might help checking How to run Keras on multiple cores?); 
If you are running on GPU:

(1) make sure Keras backend can see your GPU

from keras import backend
print(backend.tensorflow_backend._get_available_gpus())


If your GPU is not showing up here, some configuration might be necessary
on your system (it deserves another topic);

(2) take a look at the CuDNNLSTM - it is the Keras LSTM implementation
optimized for GPU.