Experiments in optimising batch steps #180
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In order to extrapolate a long training process, and to better see the impact of the batch steps, it's important to see the amount of time spent on each epoch.
Instead of erroring, when too many batchsteps is set such that the final batch size would exceed dataset length, simply don't truncate the batch steps instead of throwing an error. This change enables experimenting with more aggressive batch steps, and also comes in handy when working with long-read data.
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