Multiple keras models parallel - time efficient
As was suggested by Erik van de Ven, it sounds like running each model on a different process should provide the requested parallelism.  

I guess you could either run the fit function for each model in a different process
Or you could even load them on different cpu cores:

with K.device('cpu0'):
    input1 = Input(inputShapeOfModel1)
    output1 = model1(input1)

with K.device('gpu0'):
    input2 = Input(inputShapeOfModel2)
    output2 = model2(input2)

model = Model([input1, input2], [output1, output2])


I haven't tried any of these though, so i'm not sure what would provide the best result