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 # Distributed PyTorch
 
-To be Added
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+[PyTorch](https://pytorch.org/) is well supported in Occlum. Unmodified distributed PyTorch training (such as fasion-MNIST dataset training) could be enabled running in TEE by simple steps. 
+
+## Overview
+
+![Flow](../images/distributed_pytorch.png)
+
+Above is the flow chart which could be break down as below.
+
+1. Client starts the PyTorch Occlum instances, such as one master and two worker nodes.
+2. Every PyTorch Occlum instance starts, get required keys from one remote attestation based KMS. The keys could be for data decryption, result encryption or Pytorch nodes TLS certs.
+3. PyTorch nodes set up rendezvous.
+4. Loading dataset, training started.
+5. Encrypt and save result if necessary.
+
+For step 2, users could use the Occlum [init-ra AECS](https://occlum.readthedocs.io/en/latest/remote_attestation.html#init-ra-solution) solution which has no invasion to the PyTorch application.
+
+More details please refer to [pytorch demo](https://github.com/occlum/occlum/tree/master/demos/pytorch/distributed).
+
+Note, the demo has not involved KMS and data encryption or decryption. Users could add them accordingly.