Samplers always return the same number of batches in distributed mode #267
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csukuangfj
reviewed
Apr 13, 2021
lhotse/dataset/sampling.py
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| The formula used to determine which batches are returned is | ||
| ``(batch_idx + rank) % world_size == 0``. | ||
| This ensures that we can return an equal number of batches in all distributed workers | ||
| in spite of using a dynamic batch size, at the cost of skipping at most ``world_size`` batches. |
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Should it be
skipping at most ``world_size - 1`` batches.
?
csukuangfj
reviewed
Apr 13, 2021
lhotse/dataset/sampling.py
Outdated
| DistributedSampler -- instead of partitioning the underlying cuts into equally sized chunks, | ||
| it will return every N-th batch and skip the other batches (where ``N == world_size``). | ||
| The formula used to determine which batches are returned is | ||
| ``(batch_idx + rank) % world_size == 0``. |
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Should it be
(batch_idx + (world_size - rank)) % world_size == 0
?
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yes, you're right, thanks
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@danpovey @csukuangfj