def main(args, devices):
# load reddit data
data = RedditDataset(self_loop=True)
n_classes = data.num_classes
g = data[0]
features = g.ndata['feat']
in_feats = features.shape[1]
labels = g.ndata['label']
train_mask = g.ndata['train_mask']
val_mask = g.ndata['val_mask']
test_mask = g.ndata['test_mask']
g.ndata['features'] = features
g.create_formats_()
# Pack data
data = train_mask, val_mask, test_mask, in_feats, labels, n_classes, g
n_gpus = len(devices)
if devices[0] == -1:
run(0, 0, args, ['cpu'], data)
elif n_gpus == 1:
run(0, n_gpus, args, devices, data)
else:
procs = []
for proc_id in range(n_gpus):
p = mp.Process(target=thread_wrapped_func(run),
args=(proc_id, n_gpus, args, devices, data))
p.start()
procs.append(p)
for p in procs:
p.join()
def main(args, devices):
# load reddit data
data = RedditDataset(self_loop=True)
train_mask = data.train_mask
val_mask = data.val_mask
test_mask = data.test_mask
features = th.Tensor(data.features)
in_feats = features.shape[1]
labels = th.LongTensor(data.labels)
n_classes = data.num_labels
# Construct graph
g = dgl.graph(data.graph.all_edges())
g.ndata['features'] = features
# Pack data
data = train_mask, val_mask, test_mask, in_feats, labels, n_classes, g
n_gpus = len(devices)
if devices[0] == -1:
run(0, 0, args, ['cpu'], data)
if n_gpus == 1:
run(0, n_gpus, args, devices, data)
else:
procs = []
for proc_id in range(n_gpus):
p = mp.Process(target=thread_wrapped_func(run),
args=(proc_id, n_gpus, args, devices, data))
p.start()
procs.append(p)
for p in procs:
p.join()
run(args, device, data)
def main(args, devices):
# load reddit data
data = RedditDataset(self_loop=False)
n_classes = data.num_classes
g = data[0]
train_mask = g.ndata['train_mask']
val_mask = g.ndata['val_mask']
test_mask = g.ndata['test_mask']
# Create csr/coo/csc formats before launching training processes with multi-gpu.
# This avoids creating certain formats in each sub-process, which saves momory and CPU.
g.create_formats_()
# Pack data
data = train_mask, val_mask, test_mask, n_classes, g
n_gpus = len(devices)
if devices[0] == -1:
run(0, 0, args, ['cpu'], data)
elif n_gpus == 1:
run(0, n_gpus, args, devices, data)
else:
procs = []
for proc_id in range(n_gpus):
p = mp.Process(target=thread_wrapped_func(run),
args=(proc_id, n_gpus, args, devices, data))
p.start()
procs.append(p)
for p in procs:
p.join()
devices = list(map(int, args.gpu.split(',')))
n_gpus = len(devices)
g, n_classes = load_reddit()
# Construct graph
g = dgl.as_heterograph(g)
in_feats = g.ndata['features'].shape[1]
if args.inductive:
train_g, val_g, test_g = inductive_split(g)
else:
train_g = val_g = test_g = g
prepare_mp(train_g)
prepare_mp(val_g)
prepare_mp(test_g)
# Pack data
data = in_feats, n_classes, train_g, val_g, test_g
if n_gpus == 1:
run(0, n_gpus, args, devices, data)
else:
procs = []
for proc_id in range(n_gpus):
p = mp.Process(target=thread_wrapped_func(run),
args=(proc_id, n_gpus, args, devices, data))
p.start()
procs.append(p)
for p in procs:
p.join()
g, n_classes = load_reddit()
# Construct graph
g = dgl.as_heterograph(g)
in_feats = g.ndata["features"].shape[1]
if args.inductive:
train_g, val_g, test_g = inductive_split(g)
else:
train_g = val_g = test_g = g
prepare_mp(train_g)
prepare_mp(val_g)
prepare_mp(test_g)
# Pack data
data = in_feats, n_classes, train_g, val_g, test_g
if n_gpus == 1:
run(0, n_gpus, args, devices, data)
else:
procs = []
for proc_id in range(n_gpus):
p = mp.Process(
target=thread_wrapped_func(run),
args=(proc_id, n_gpus, args, devices, data),
)
p.start()
procs.append(p)
for p in procs:
p.join()
print("make sure the number of gpus greater than 1!")
sys.exit()
dataset = MAG240MDataset(root=args.rootdir)
print('Loading graph')
(g, ), _ = dgl.load_graphs(args.graph_path)
g = g.formats(['csc'])
print('Loading features')
paper_offset = dataset.num_authors + dataset.num_institutions
num_nodes = paper_offset + dataset.num_papers
num_features = dataset.num_paper_features
feats = np.memmap(args.full_feature_path,
mode='r',
dtype='float16',
shape=(num_nodes, num_features))
procs = []
for proc_id in range(n_gpus):
p = mp.Process(target=thread_wrapped_func(train),
args=(proc_id, n_gpus, args, dataset, g, feats,
paper_offset))
p.start()
procs.append(p)
for p in procs:
p.join()
test(args, dataset, g, feats, paper_offset)
