wkcn commented on a change in pull request #16175: [Dataset] add shard API
URL: https://github.com/apache/incubator-mxnet/pull/16175#discussion_r324486007
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File path: python/mxnet/gluon/data/dataset.py
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@@ -64,6 +64,37 @@ def filter(self, fn):
from . import FilterSampler
return _SampledDataset(self, FilterSampler(fn, self))
+ def shard(self, num_shards, index):
+ """Returns a new dataset includes only 1/num_shards of this dataset.
+
+ For distributed training, be sure to shard before you randomize the
dataset
+ (such as shuffle), if you want each worker to reach a unique subset.
+
+ Parameters
+ ----------
+ num_shards : int
+ A integer representing the number of data shards.
+ index : int
+ A integer representing the index of the current shard.
+
+ Returns
+ -------
+ Dataset
+ The result dataset.
+ """
+ assert index < num_shards, 'Shard index of out bound: %d out of
%d'%(index, num_shards)
+ assert num_shards > 0, 'Number of shards must be greater than 0'
+ length = len(self)
+ shard_len = length // num_shards
+ # Compute the start index for this partition
+ start = shard_len * index
+ # Compute the end index for this partition
+ end = start + shard_len
+ if index == num_shards - 1:
+ end = length
Review comment:
The sampler is not uniform. If there are 199 samples and 100 partitions,
there will be `1+99` samples in the last partition, but `1` sample in other
partitions.
The following implementation will be more uniform.
```python
shared_len = length // num_shareds
rest = length % num_shareds
start = shared_len * index + min(index, rest)
end = start + shared_len + (index < rest)
```
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