[FEATURE] Transform custom dataset to deeplake dataset/database/vectorstore conveniently using DDP

Description

Here is my use case: I have 4 gpu nodes for training (including compute tensors) on aws. I want to save pre-computed tensors to deeplake (Dataset/database/vectorstore), aiming to save a lot of time for next training. I use accelerate as my distributed parallel framework. So my framework works like this:

deeplake_path = 'dataset_{}'.format(current_process_index)
ds = deeplake.dataset(deeplake_path, overwrite=False)
for index, data_dict in enumerate(my_pytorch_dataloader):
  with torch.no_grad():
    a = net_a_frozen(data_dict['a'])
    b = net_b_frozen(data_dict['b'])
  # loss = net_c_training(a, b)
  # the loss is only used in training.
  save_dict = {'data_dict': data_dict, 'a': a.detach().cpu().numpy(), 'b': b.detach().cpu().numpy()}
  append_to_deeplake(deeplake_path, save_dict)
  if index % 100 == 0:
    commit_to_deeplake(deeplake_path)

Note that I can use deeplake instead of computing the tensors i need again in the next training after the deeplake dataset construction. The problem includes:

1. I have to assign different deeplake dataset to different processes but i need to merge them into a dataset after this.
2. I need to design a proper for-loop/parallel workflow for deeplake dataset construction.
3. The frequent append and commit function takes me a lot of time.
4. detach() and to_cpu() function takes me a lot of time.

So Is there any feature to transform custom dataset to deeplake dataset? If we have a function which works like this:

ds.distributed_append_gpu_tensor_and_auto_commit(data_tensor)
ds.auto_transorm_pytorch_dataset(my_pytorch_dataloader)

or could you give me a standard workflow to solve this? I don't know which is the best method for this scenario. The document did not cover this problem. #2596 also indicates this problem.

Use Cases

Distributed parallel computing and saving to deeplake.