iteration resuming

[pluiez]

Hi, I'm wondering for each backend and each dataset, do they support saving iteration state and resume later to continue previous iteration where it stopped?

This feature is required for resuming from a checkpoint during model training.

for example:

dataloader = jdl.DataLoader(ds, backend, shuffle=True)
for i, batch in enumerate(dataloader):
     if i == 100:
         state = dataloader.state_dict()
# re-init the dataloader, and then try to resume from state
dataloader = jdl.DataLoader(ds, backend, shuffle=True)
dataloader.load_state_dict(state)

for batch in enumerate(dataloader):
    ....

[BirkhoffG]

Hi @pluiez All dataloader backends do not assume the statefulness. For now, you should manually store the states (e.g., batch index, arguments, seeds, etc), and load the states by re-init the dataloader and iterate to the target batch index.

I am not planning to support load_state_dict as it is not a part of API in the (official) pytorch dataloader. I might consider supporting statefuldataloader in the future, but this is still an experimental feature from pytorch team.

[pluiez]

Hi @pluiez All dataloader backends do not assume the statefulness. For now, you should manually store the states (e.g., batch index, arguments, seeds, etc), and load the states by re-init the dataloader and iterate to the target batch index.

I am not planning to support load_state_dict as it is not a part of API in the (official) pytorch dataloader. I might consider supporting statefuldataloader in the future, but this is still an experimental feature from pytorch team.

I have checked statefuldataloader implementation, it does support resuming by simply skipping the same number of yielded batches before checkpointing. This is an available though a bit inefficient solution. I'm looking for good practice to support a memory efficient and fast dataset iteration supporting shuffling and resumable iteration. Thank you for your information.