partition_arxiv_products.py partitions the ogbn-arxiv and ogbn-products graphs from the Open Graph Benchmarks using DGL's metis-based partitioning. The general technique there can be used to partition arbitrary homogeneous graphs. Note that all node-related information must be included in the graph's ndata dictionary so that they are correctly partitioned with the graph. Similarly, edge-related information must be included in the graph's edata dictionary
partition_mag.py partitions the ogbn-mag heterogeneous graph. Again, all node-related information are included in the graph's ndata for the relevant node types
The script train_homogeneous_graph_basic.py demonstrates the basic functionality of SAR. It runs distriobuted training using a 3-layer GraphSage network on a partiotioned graph. If you want to train using N workers, then you need to launch the script N times, preferably on separate machines. For example, for N=2, and assuming the two workers are on the same network file system, you can launch the 2 workers using the following two commands:
python3 train_homogeneous_graph_basic.py --partitioning-json-file /path/to/partitioning/graph_name.json --ip-file /path/to/ip_file --rank 0 --world-size 2
python3 train_homogeneous_graph_basic.py --partitioning-json-file /path/to/partitioning/graph_name.json --ip-file /path/to/ip_file --rank 1 --world-size 2
The worker with rank=0 (the master) will write its address to the file specified by the --ip-file option and the other worker(s) will read this file and connect to the master.
The train_homogeneous_graph_advanced.py script demonstrates more advanced features of SAR such as distributed construction of Message Flow Graphs (MFGs), and the multiple training modes supported by SAR.
The train_heterogeneous_graph.py script demonstrates training on a heterogeneous graph (ogbn-mag). The script trains a 3-layer R-GCN.