text: Title: Train using GPU, predict using CPU
  Body: Hi everyone!
  I'm new in deep learning, so sorry I ask a lot of questions.  I opened this issue: https://github.com/deepmipt/DeepPavlov/issues/1212 and now I can use GPU to train BERT models. I'm building chatbot system and for this reason I have to load 4 trained models: intent classification, two ner models, and bert embedder. But I have several problems with it:
  1) I want to train models one by one, but after first training, training process is still exist (nvidia-smi shows this). I trained 3 models this way: train model, then restart Jupyter's kernel (this clear training process from GPU) and then train another models the same way. But it is not flexible solution. I want to use .py file where all models will be trained. (when I use .py file also this problem)
  2) Loading trained models using GPU to Jupyter Notebook make Jupyter's kernel dead. I try to load them using .py file, also have error. I think, that may be not enough memory, but It's strange. When I use CPU to train, there is no problem with memory, I can load all this models and use them. May be good way is to train model using GPU, and then make predictions using CPU, is it possible?
  
  I feel that smth wrong with memory usage of GPU, but lack of experience in deep learning doesn't afford me to solve this by myself. 
  (p.s. возможно будет эффективней общаться на русском:) )
  
  
  