diff --git a/example/distributed_training/cifar10_kvstore_hvd.py b/example/distributed_training/cifar10_kvstore_hvd.py
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+#!/usr/bin/env python
+
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""cifar10_dist_hvd.py contains code that runs distributed training of a
+ResNet18 network using Horovod framework"""
+
+import argparse
+import logging
+import time
+import random
+import types
+import warnings
+
+import numpy as np
+import mxnet as mx
+from mxnet import autograd, gluon, kv, nd
+from mxnet.gluon.model_zoo import vision
+
+logging.basicConfig(level=logging.INFO)
+
+# Training settings
+parser = argparse.ArgumentParser(description='MXNet CIFAR Example')
+
+parser.add_argument('--batch-size', type=int, default=64,
+                    help='training batch size per worker (default: 64)')
+parser.add_argument('--epochs', type=int, default=5,
+                    help='number of training epochs (default: 5)')
+parser.add_argument('--lr', type=float, default=0.01,
+                    help='learning rate (default: 0.01)')
+parser.add_argument('--no-cuda', action='store_true', default=False,
+                    help='disable training on GPU (default: False)')
+args = parser.parse_args()
+
+if not args.no_cuda:
+    # Disable CUDA if there are no GPUs.
+    if mx.context.num_gpus() == 0:
+        args.no_cuda = True
+
+
+# Transform input data
+def transform(data, label):
+    return nd.transpose(data.astype(np.float32), (2, 0, 1))/255,\
+      label.astype(np.float32)
+
+
+# Train a batch using multiple GPUs
+def train(batch_list, context, network, gluon_trainer, metric):
+    """ Training with multiple GPUs
+
+    Parameters
+    ----------
+    batch_list: List
+      list of dataset
+    context: List
+      a list of all GPUs to be used for training
+    network:
+      ResNet
+    gluon_trainer:
+      rain module of gluon
+    """
+
+    # Run one forward and backward pass
+    def forward_backward(network, data, labels, metric):
+        with autograd.record():
+            # Compute outputs
+            outputs = [network(X) for X in data]
+            # Compute the loss
+            losses = [loss(yhat, y) for yhat, y in zip(outputs, labels)]
+
+        # Run the backward pass (calculate gradients)
+        for l in losses:
+            l.backward()
+
+        metric.update(preds=outputs, labels=labels)
+
+    # Use cross entropy loss
+    loss = gluon.loss.SoftmaxCrossEntropyLoss()
+
+    # Split and load data
+    data = batch_list[0]
+    data = gluon.utils.split_and_load(data, context)
+
+    # Split and load label
+    label = batch_list[1]
+    label = gluon.utils.split_and_load(label, context)
+
+    # Run the forward and backward pass
+    forward_backward(network, data, label, metric)
+
+    # Update the parameters
+    this_batch_size = batch_list[0].shape[0]
+    gluon_trainer.step(this_batch_size)
+
+
+# Evaluate accuracy of the given network using the given data
+def evaluate(data_iterator, network, context):
+    """ Measure the accuracy of ResNet
+
+    Parameters
+    ----------
+    data_iterator: Iter
+      examples of dataset
+    network:
+      ResNet
+
+    Returns
+    ----------
+    tuple of array element
+    """
+    acc = mx.metric.Accuracy()
+
+    # Iterate through data and label
+    for i, (data, label) in enumerate(data_iterator):
+
+        # Get the data and label into the GPU
+        data = data.as_in_context(context)
+        label = label.as_in_context(context)
+
+        # Get network's output which is a probability distribution
+        # Apply argmax on the probability distribution to get network's
+        # classification.
+        output = network(data)
+        predictions = nd.argmax(output, axis=1)
+
+        # Give network's prediction and the correct label to update the metric
+        acc.update(preds=predictions, labels=label)
+
+    # Return the accuracy
+    return acc.get()[1]
+
+
+class SplitSampler(gluon.data.sampler.Sampler):
+    """ Split the dataset into `num_parts` parts and sample from the part with
+    index `part_index`
+
+    Parameters
+    ----------
+    length: int
+      Number of examples in the dataset
+    num_parts: int
+      Partition the data into multiple parts
+    part_index: int
+      The index of the part to read from
+    """
+    def __init__(self, length, num_parts=1, part_index=0):
+        # Compute the length of each partition
+        self.part_len = length // num_parts
+        # Compute the start index for this partition
+        self.start = self.part_len * part_index
+        # Compute the end index for this partition
+        self.end = self.start + self.part_len
+
+    def __iter__(self):
+        # Extract examples between `start` and `end`, shuffle and return them.
+        indices = list(range(self.start, self.end))
+        random.shuffle(indices)
+        return iter(indices)
+
+    def __len__(self):
+        return self.part_len
+
+
+# Use Horovod as the KVStore
+store = kv.create('horovod')
+
+# Get the number of workers
+num_workers = store.num_workers
+
+# Create the context based on the local rank of the current process
+ctx = mx.cpu(store.local_rank) if args.no_cuda else mx.gpu(store.local_rank)
+
+# Load the training data
+train_data = gluon.data.DataLoader(gluon.data.vision.CIFAR10(train=True,
+                                   transform=transform), args.batch_size,
+                                   sampler=SplitSampler(50000,
+                                                        num_workers,
+                                                        store.rank))
+
+# Load the test data
+test_data = gluon.data.DataLoader(gluon.data.vision.CIFAR10(train=False,
+                                  transform=transform),
+                                  args.batch_size, shuffle=False)
+
+# Load ResNet18 model from GluonCV model zoo
+net = vision.resnet18_v1()
+
+# Initialize the parameters with Xavier initializer
+net.initialize(mx.init.Xavier(), ctx=ctx)
+
+# Use Adam optimizer. Ask trainer to use the distributor kv store.
+trainer = gluon.Trainer(net.collect_params(), optimizer='adam',
+                        optimizer_params={'learning_rate': args.lr},
+                        kvstore=store)
+
+train_metric = mx.metric.Accuracy()
+
+# Run as many epochs as required
+for epoch in range(args.epochs):
+    tic = time.time()
+    train_metric.reset()
+
+    # Iterate through batches and run training using multiple GPUs
+    batch_num = 1
+    btic = time.time()
+    for batch in train_data:
+        # Train the batch using multiple GPUs
+        train(batch, [ctx], net, trainer, train_metric)
+        if store.rank == 0 and batch_num % 100 == 0:
+            speed = args.batch_size / (time.time() - btic)
+            logging.info('Epoch[{}] Rank [{}] Batch[{}]\tSpeed: {:.2f} samples/sec'
+                         .format(epoch, store.rank, batch_num, speed))
+            logging.info('{} = {:.2f}'.format(*train_metric.get()))
+
+        btic = time.time()
+        batch_num += 1
+
+    elapsed = time.time() - tic
+    # Print test accuracy after every epoch
+    test_accuracy = evaluate(test_data, net, ctx)
+    if store.rank == 0:
+        logging.info("Epoch %d: Test_acc %f" % (epoch, test_accuracy))
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