def main(args):
if not args.standalone:
th.distributed.init_process_group(backend="gloo")
g = dgl.distributed.DistGraph(args.ip_config,
args.graph_name,
conf_file=args.conf_path)
print("rank:", g.rank())
train_nid = dgl.distributed.node_split(g.ndata["train_mask"],
g.get_partition_book(),
force_even=True)
val_nid = dgl.distributed.node_split(g.ndata["val_mask"],
g.get_partition_book(),
force_even=True)
test_nid = dgl.distributed.node_split(g.ndata["test_mask"],
g.get_partition_book(),
force_even=True)
print("part {}, train: {}, val: {}, test: {}".format(
g.rank(), len(train_nid), len(val_nid), len(test_nid)))
device = th.device("cpu")
n_classes = len(
th.unique(g.ndata["labels"][np.arange(g.number_of_nodes())]))
# Pack data
in_feats = g.ndata["features"].shape[1]
data = train_nid, val_nid, in_feats, n_classes, g
run(args, device, data)
print("parent ends")
def main(args):
th.distributed.init_process_group(backend='gloo')
g = dgl.distributed.DistGraph(args.ip_config, args.graph_name)
print('rank:', g.rank())
train_nid = dgl.distributed.node_split(g.ndata['train_mask'],
g.get_partition_book(),
force_even=True)
val_nid = dgl.distributed.node_split(g.ndata['val_mask'],
g.get_partition_book(),
force_even=True)
test_nid = dgl.distributed.node_split(g.ndata['test_mask'],
g.get_partition_book(),
force_even=True)
print('part {}, train: {}, val: {}, test: {}'.format(
g.rank(), len(train_nid), len(val_nid), len(test_nid)))
device = th.device('cpu')
n_classes = len(
th.unique(g.ndata['labels'][np.arange(g.number_of_nodes())]))
# Pack data
in_feats = g.ndata['features'].shape[1]
data = train_nid, val_nid, in_feats, n_classes, g
run(args, device, data)
print("parent ends")
def main(args):
if not args.standalone:
th.distributed.init_process_group(backend='gloo')
g = dgl.distributed.DistGraph(args.ip_config, args.graph_name, conf_file=args.conf_path)
print('rank:', g.rank())
pb = g.get_partition_book()
train_nid = dgl.distributed.node_split(g.ndata['train_mask'], pb, force_even=True)
val_nid = dgl.distributed.node_split(g.ndata['val_mask'], pb, force_even=True)
test_nid = dgl.distributed.node_split(g.ndata['test_mask'], pb, force_even=True)
local_nid = pb.partid2nids(pb.partid).detach().numpy()
print('part {}, train: {} (local: {}), val: {} (local: {}), test: {} (local: {})'.format(
g.rank(), len(train_nid), len(np.intersect1d(train_nid.numpy(), local_nid)),
len(val_nid), len(np.intersect1d(val_nid.numpy(), local_nid)),
len(test_nid), len(np.intersect1d(test_nid.numpy(), local_nid))))
device = th.device('cpu')
labels = g.ndata['labels'][np.arange(g.number_of_nodes())]
n_classes = len(th.unique(labels[th.logical_not(th.isnan(labels))]))
print('#labels:', n_classes)
# Pack data
in_feats = g.ndata['features'].shape[1]
data = train_nid, val_nid, in_feats, n_classes, g
run(args, device, data)
print("parent ends")