I find this part slightly confusing, as there are several options.
My concerns are in this new thread:

  
    
    
    Bulk Batch Sampling Options 
  
  
    I am essentially looking for the best-practice for loading bulk batches of data, and I find very little definitive information in the docs or online, even though I’d imagine this is a quite common use case. 
Task description
My specific use-case has two features of import 


The data does not fit into memory but needs to be fetched from a table / array style database. However, given that it is array-like, it can be indexed very easily (or in case of a DB, queried even non-sequentially). And adja…
  


The issue, as I see it, is that there are in fact several ways to set up “batch sampling” in the documentation. These include setting up a custom sample function, using the pre-defined batch sampler class  also as a custom sampler (both cases have batch_size=None), and finally just setting this batch sampling parameter.
However, just setting batch size and dropout, which is said to be equivalent in the docs, clearly does not return a set of indicies to the dataset, but rather queries single indicies which are then collated.
I vow to try all these options on my problem today or tomorrow, and report back in detail what produces the fastest performance when reading small, medium and large batches from a HDF5 database.
In particular, I am unsure what is better: Using a custom batch sampler and a map style dataset, or using an iterative dataset, where the iteration is determined by a custom batch size.
This is what I want to test - or find out if anyone knows…