Should we always place the sharding filter as early as possible?

When using datapipes, it seems like you want to apply the sharding_filter as early as possible, in order to prevent data loader workers from doing duplicate work.

However, I’m struggling to understand how to use the sharding_filter in the following scenario:

def create_datapipe(s3_urls):
    pipe = dp.IterableWrapper(s3_urls)
    # a fake function that downloads from s3
    pipe =
    pipe = pipe.flatmap(chunk: [item for item in chunk])
    pipe = pipe.batch(batch_size=256)
    pipe =     

Ideally, I would place the sharding filter before do_expensive_computation, or before batch. The issue is; this parallelizes downloading from S3, which I don’t necessarily want.

Ideally, I’d have the downloading from S3 be on a single thread that is accessed from all workers, and then start parallelization after the flatmap stage.

The reason this matters is–imagine my chunks are big. (E.g, each chunk contains 5K samples), then if the number of chunks is not divisible by the number of workers, one worker will have to process an extra chunk. This worker, will take a very long time.

  • The generally recommendation is that you should shuffle before you shard (for randomness/determinism reasons).
  • Based on how DataLoader is currently implemented, each worker will duplicate the work of the DataPipe prior to sharding. In the future, when we update the implementation of DataLoader, we may be able to run certain operations on one process only and then other workers will read from it.

I suspect for your case, you still want to shard early to avoid duplicate downloading, but it is hard to know for sure without knowing the exact setup and running it.