indhub commented on a change in pull request #10955: [MXNET-422] Distributed 
training tutorial
URL: https://github.com/apache/incubator-mxnet/pull/10955#discussion_r189136422
 
 

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 File path: example/distributed_training/README.md
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+# Distributed Training using Gluon
+
+Deep learning models are usually trained using GPUs because GPUs can do a lot 
more computations in parallel that CPUs. But even with the modern GPUs, it 
could take several days to train big models. Training can be done faster by 
using multiple GPUs like described in 
[this](https://gluon.mxnet.io/chapter07_distributed-learning/multiple-gpus-gluon.html)
 tutorial. However only a certain number of GPUs can be attached to one host 
(typically 8 or 16). To make the training even faster, we can use multiple GPUs 
attached to multiple hosts.
+
+In this tutorial, we will show how to train a model faster using multi-host 
distributed training.
+
+![Multiple GPUs connected to multiple 
hosts](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/example/distributed_training/distributed_training.png)
+
+We will use data parallelism to distribute the training which involves 
splitting the training data across GPUs attached to multiple hosts. Since the 
hosts are working with different subset of the training data in parallel, the 
training completes lot faster.
+
+In this tutorial, we will train a LeNet network using MNIST dataset using two 
hosts each having four GPUs.
 
 Review comment:
   Valid point. I'll switch to CIFAR. 

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