dmlc--dgl
Hongzhi (Steve), Chen
5008af2210
* pytorch_example * fix --------- Co-authored-by: Ubuntu <ubuntu@ip-172-31-28-63.ap-northeast-1.compute.internal>
97 行
2.7 KiB
Python
97 行
2.7 KiB
Python
import argparse
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import os
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import sys
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import time
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import dgl
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import numpy as np
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import torch as th
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sys.path.append(os.path.join(os.path.dirname(__file__), ".."))
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from load_graph import load_ogb, load_reddit
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if __name__ == "__main__":
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argparser = argparse.ArgumentParser("Partition builtin graphs")
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argparser.add_argument(
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"--dataset",
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type=str,
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default="reddit",
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help="datasets: reddit, ogb-product, ogb-paper100M",
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)
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argparser.add_argument(
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"--num_parts", type=int, default=4, help="number of partitions"
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)
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argparser.add_argument(
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"--part_method", type=str, default="metis", help="the partition method"
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)
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argparser.add_argument(
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"--balance_train",
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action="store_true",
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help="balance the training size in each partition.",
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)
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argparser.add_argument(
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"--undirected",
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action="store_true",
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help="turn the graph into an undirected graph.",
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)
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argparser.add_argument(
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"--balance_edges",
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action="store_true",
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help="balance the number of edges in each partition.",
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)
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argparser.add_argument(
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"--num_trainers_per_machine",
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type=int,
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default=1,
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help="the number of trainers per machine. The trainer ids are stored\
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in the node feature 'trainer_id'",
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)
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argparser.add_argument(
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"--output",
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type=str,
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default="data",
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help="Output path of partitioned graph.",
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)
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args = argparser.parse_args()
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start = time.time()
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if args.dataset == "reddit":
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g, _ = load_reddit()
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elif args.dataset == "ogb-product":
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g, _ = load_ogb("ogbn-products")
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elif args.dataset == "ogb-paper100M":
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g, _ = load_ogb("ogbn-papers100M")
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print(
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"load {} takes {:.3f} seconds".format(args.dataset, time.time() - start)
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)
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print("|V|={}, |E|={}".format(g.num_nodes(), g.num_edges()))
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print(
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"train: {}, valid: {}, test: {}".format(
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th.sum(g.ndata["train_mask"]),
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th.sum(g.ndata["val_mask"]),
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th.sum(g.ndata["test_mask"]),
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)
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)
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if args.balance_train:
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balance_ntypes = g.ndata["train_mask"]
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else:
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balance_ntypes = None
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if args.undirected:
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sym_g = dgl.to_bidirected(g, readonly=True)
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for key in g.ndata:
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sym_g.ndata[key] = g.ndata[key]
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g = sym_g
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dgl.distributed.partition_graph(
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g,
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args.dataset,
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args.num_parts,
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args.output,
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part_method=args.part_method,
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balance_ntypes=balance_ntypes,
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balance_edges=args.balance_edges,
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num_trainers_per_machine=args.num_trainers_per_machine,
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)
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