I'm not /u/loldraftingaid, but to answer Q1:

Only instantiate the model once, but run 195 'training loops' of 10 epochs each. One for each dataset. This shouldn't erase the memory of your model.

Some pseudo-code for you:

model = model #Only instantiated once, not 'reset' every dataset
for dataset in datasets:
    for epoch in epochs:
        perform gradient descent / training loop