Add GraphLearn-for-PyTorch(GLT) distributed examples

examples/graphlearn_for_pytorch/README.md

@@ -1,8 +1,7 @@
 # Using GraphLearn-for-PyTorch(GLT) for Distributed Acceleration for PyG
 
 **[GraphLearn-for-PyTorch(GLT)](https://github.com/alibaba/graphlearn-for-pytorch)** is a graph learning library for PyTorch that makes distributed GNN training and inference easy and efficient. 
-It leverages the
-power of GPUs to accelerate graph sampling and utilizes UVA to reduce the conversion and copying of features of vertices and edges.
+It leverages the power of GPUs to accelerate graph sampling and utilizes UVA to reduce the conversion and copying of features of vertices and edges.
 For large-scale graphs, it supports distributed training on multiple GPUs or multiple machines through fast distributed sampling and feature lookup.
 Additionally, it provides flexible deployment for distributed training to meet different requirements. 
 

examples/graphlearn_for_pytorch/distributed/README.md

@@ -33,11 +33,11 @@ CUDA_VISIBLE_DEVICES=0,1 python dist_train_sage_supervised.py \
 CUDA_VISIBLE_DEVICES=2,3 python dist_train_sage_supervised.py \
   --num_nodes=2 --node_rank=1 --master_addr=localhost \
   --dataset=ogbn-products --dataset_root_dir=../../../data/ogbn-products \
-  --in_channel=100 --out_channel=47 
+  --in_channel=100 --out_channel=47
 ```
 
 **Note:**
-1. You should change master_addr to the ip of node#0 
+1. You should change master_addr to the ip of node#0
 2. Since there is randomness in the step of partitioning, please ensure all nodes are using the same data when running `dist_train_sage_supervised.py`.
 
 
@@ -122,4 +122,4 @@ Optional parameters which you can append after the command above includes:
 --batch_size: batch size for sampling, (default=2048)
 ```
 
-Note: you should change master_addr to the ip of server-node#0
+Note: you should change master_addr to the ip of server-node#0