Hello,

I have 4 questions for clarification:

1. Why we should pass the training_data to the deepspeed.initialize to generate a new trainloader rather than using a normal torch trainloader ?
2. Can we use a custom pytorch trainloader in case we have custom dataset that returns for example inputs, outputs and mask ?
3. If the actual batch size that is used to be passed to the model is different than the train_batch_size in the json file, what will happen ?
4. Can we just define gradient_accumulation_steps and train_micro_batch_size_per_gpu
   only and leave deepspeed to calculate train_batch_size automatically ?