diff --git a/python/mxnet/gluon/data/dataset.py b/python/mxnet/gluon/data/dataset.py
index a5399f259435..7f2c6342d595 100644
--- a/python/mxnet/gluon/data/dataset.py
+++ b/python/mxnet/gluon/data/dataset.py
@@ -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'
+        assert index >= 0, 'Index must be non-negative'
+        length = len(self)
+        shard_len = length // num_shards
+        rest = length % num_shards
+        # Compute the start index for this partition
+        start = shard_len * index + min(index, rest)
+        # Compute the end index for this partition
+        end = start + shard_len + (index < rest)
+        from . import SequentialSampler
+        return _SampledDataset(self, SequentialSampler(end - start, start))
+
     def take(self, count):
         """Returns a new dataset with at most `count` number of samples in it.
 
diff --git a/python/mxnet/gluon/data/sampler.py b/python/mxnet/gluon/data/sampler.py
index 980c63134fac..f53e636317df 100644
--- a/python/mxnet/gluon/data/sampler.py
+++ b/python/mxnet/gluon/data/sampler.py
@@ -36,23 +36,25 @@ def __len__(self):
 
 
 class SequentialSampler(Sampler):
-    """Samples elements from [0, length) sequentially.
+    """Samples elements from [start, start+length) sequentially.
 
     Parameters
     ----------
     length : int
         Length of the sequence.
+    start : int, default is 0
+        The start of the sequence index.
     """
-    def __init__(self, length):
+    def __init__(self, length, start=0):
         self._length = length
+        self._start = start
 
     def __iter__(self):
-        return iter(range(self._length))
+        return iter(range(self._start, self._start + self._length))
 
     def __len__(self):
         return self._length
 
-
 class RandomSampler(Sampler):
     """Samples elements from [0, length) randomly without replacement.
 
diff --git a/tests/python/unittest/test_gluon_data.py b/tests/python/unittest/test_gluon_data.py
index 62bf7c3db869..80bea2a511f2 100644
--- a/tests/python/unittest/test_gluon_data.py
+++ b/tests/python/unittest/test_gluon_data.py
@@ -296,6 +296,24 @@ def test_dataset_filter():
     for idx, sample in enumerate(a_xform_filtered):
         assert sample % 10 == 0
 
+def test_dataset_shard():
+    length = 9
+    a = mx.gluon.data.SimpleDataset([i for i in range(length)])
+    shard_0 = a.shard(4, 0)
+    shard_1 = a.shard(4, 1)
+    shard_2 = a.shard(4, 2)
+    shard_3 = a.shard(4, 3)
+    assert len(shard_0) + len(shard_1) + len(shard_2) + len(shard_3) == length
+    assert len(shard_0) == 3
+    assert len(shard_1) == 2
+    assert len(shard_2) == 2
+    assert len(shard_3) == 2
+    total = 0
+    for shard in [shard_0, shard_1, shard_2, shard_3]:
+        for idx, sample in enumerate(shard):
+            total += sample
+    assert total == sum(a)
+
 def test_dataset_take():
     length = 100
     a = mx.gluon.data.SimpleDataset([i for i in range(length)])