I understand that Gluon is much more flexible and more user friendly and you can even speed up by hybridizing the model, and after hybridizing your model would be almost 2x faster than before.
But the problem is that the symbolic graphs using symbol api is even 30% more faster than hybridized Gluon model, so why should we indulge in something which is slower? Moreover it is easy to make your own custom computation and backpropagate through it using symbol api, while in Gluon you have to make our own class using “block” or “hybrid block”.
I think the only use case of gluon is to use its imperative and flexible nature, through which we can do debugging for your model by changing the computation graph (which is not possible with symbol api) and then finally build our model using symbol api.
So its like using gluon for research and learning stuff, and symbol api for production.
PS: Plz correct me if I am wrong anywhere, I am just 18 years old and just started deep learning. I started using Mxnet when I found that doing research work in tensorflow is very difficult.