title: Combining multiple models trained in different parts of the dataset in PyTorch
body: <p>In PyTorch, is it theoretically possible to 'merge' multiple models together into one model - effectively combining all the data learnt so far? The models are exactly identical, however, are trained with different parts of the training data.</p>
  <p>If so, would it be possible to split a dataset into equal parts and distribute training between many computers in a similar manner to folding@home? Would the new model be as good as if it was not distributed?</p>
  <p><img src="https://i.stack.imgur.com/zunJt.png" alt="Diagram detailing this" /></p>