Training on a subset of data for few epochs and then proceeding to the next subset for few epochs and so on?
Training until convergence on a subset of data and starting again on another subset is not a good idea.

Gradients of loss will have high variance over your batches and so optimizing over it will not be useful (you basically start at a far new point on your loss for the next subset).

Instead, the good way to go in batch-training methods is to iteratively make training steps on differents subsets of your data until a global convergence.
Your may hear about an epoch, which consists of training steps on subsets until the overall data has been seen. 
You see how convergence is over the epochs and not over one subset.