🎉 Official STGraph Documentation started

.gitignore

@@ -13,7 +13,10 @@ benchmarking/results/
 *.egg-info/
 stgraph/build/
 stgraph/dist/
+dev-stgraph/
 docs/_build
+docs_old/
+docs_v2/
 __pycache__/
 .DS_Store
 build

benchmarking/gat/seastar/train.py

@@ -25,8 +25,8 @@ def train(args):
     cora = CoraDataLoader(verbose=True)
 
     # To account for the initial CUDA Context object for pynvml
-    tmp = StaticGraph([(0,0)], [1], 1)
-    
+    tmp = StaticGraph([(0, 0)], [1], 1)
+
     features = torch.FloatTensor(cora.get_all_features())
     labels = torch.LongTensor(cora.get_all_targets())
     train_mask = cora.get_train_mask()
@@ -49,15 +49,15 @@ def train(args):
 
     assert train_mask.shape[0] == num_nodes
 
-    print('dataset {}'.format("Cora"))
-    print('# of edges : {}'.format(num_edges))
-    print('# of nodes : {}'.format(num_nodes))
-    print('# of features : {}'.format(num_feats))
+    print("dataset {}".format("Cora"))
+    print("# of edges : {}".format(num_edges))
+    print("# of nodes : {}".format(num_nodes))
+    print("# of features : {}".format(num_feats))
 
     features = torch.FloatTensor(features)
     labels = torch.LongTensor(labels)
 
-    if hasattr(torch, 'BoolTensor'):
+    if hasattr(torch, "BoolTensor"):
         train_mask = torch.BoolTensor(train_mask)
 
     else:
@@ -74,17 +74,19 @@ def train(args):
 
     # create model
     heads = ([args.num_heads] * args.num_layers) + [args.num_out_heads]
-    model = GAT(g,
-                args.num_layers,
-                num_feats,
-                args.num_hidden,
-                n_classes,
-                heads,
-                F.elu,
-                args.in_drop,
-                args.attn_drop,
-                args.negative_slope,
-                args.residual)
+    model = GAT(
+        g,
+        args.num_layers,
+        num_feats,
+        args.num_hidden,
+        n_classes,
+        heads,
+        F.elu,
+        args.in_drop,
+        args.attn_drop,
+        args.negative_slope,
+        args.residual,
+    )
     print(model)
     if args.early_stop:
         stopper = EarlyStopping(patience=100)
@@ -94,7 +96,8 @@ def train(args):
 
     # use optimizer
     optimizer = torch.optim.Adam(
-        model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
+        model.parameters(), lr=args.lr, weight_decay=args.weight_decay
+    )
 
     # initialize graph
     dur = []
@@ -103,8 +106,8 @@ def train(args):
     Used_memory = 0
 
     for epoch in range(args.num_epochs):
-        #print('epoch = ', epoch) 
-        #print('mem0 = {}'.format(mem0))
+        # print('epoch = ', epoch)
+        # print('mem0 = {}'.format(mem0))
         torch.cuda.synchronize()
         tf = time.time()
         model.train()
@@ -120,7 +123,7 @@ def train(args):
         torch.cuda.synchronize()
         loss.backward()
         optimizer.step()
-        t2 =time.time()
+        t2 = time.time()
         run_time_this_epoch = t2 - tf
 
         if epoch >= 3:
@@ -131,56 +134,77 @@ def train(args):
 
         train_acc = accuracy(logits[train_mask], labels[train_mask])
 
-        #log for each step
-        print('Epoch {:05d} | Time(s) {:.4f} | train_acc {:.6f} | Used_Memory {:.6f} mb'.format(
-            epoch, run_time_this_epoch, train_acc, (now_mem * 1.0 / (1024**2))
-        ))
+        # log for each step
+        print(
+            "Epoch {:05d} | Time(s) {:.4f} | train_acc {:.6f} | Used_Memory {:.6f} mb".format(
+                epoch, run_time_this_epoch, train_acc, (now_mem * 1.0 / (1024**2))
+            )
+        )
 
     if args.early_stop:
-        model.load_state_dict(torch.load('es_checkpoint.pt'))
+        model.load_state_dict(torch.load("es_checkpoint.pt"))
 
-    #OUTPUT we need
-    avg_run_time = avg_run_time *1. / record_time
-    Used_memory /= (1024**3)
-    print('^^^{:6f}^^^{:6f}'.format(Used_memory, avg_run_time))
+    # OUTPUT we need
+    avg_run_time = avg_run_time * 1.0 / record_time
+    Used_memory /= 1024**3
+    print("^^^{:6f}^^^{:6f}".format(Used_memory, avg_run_time))
 
-if __name__ == '__main__':
 
-    parser = argparse.ArgumentParser(description='GAT')
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="GAT")
 
     # COMMENT IF SNOOP IS TO BE ENABLED
     snoop.install(enabled=False)
 
-    parser.add_argument("--gpu", type=int, default=0,
-                        help="which GPU to use. Set -1 to use CPU.")
-    parser.add_argument("--num_epochs", type=int, default=200,
-                        help="number of training epochs")
-    parser.add_argument("--num_heads", type=int, default=8,
-                        help="number of hidden attention heads")
-    parser.add_argument("--num_out_heads", type=int, default=1,
-                        help="number of output attention heads")
-    parser.add_argument("--num_layers", type=int, default=1,
-                        help="number of hidden layers")
-    parser.add_argument("--num_hidden", type=int, default=32,
-                        help="number of hidden units")
-    parser.add_argument("--residual", action="store_true", default=False,
-                        help="use residual connection")
-    parser.add_argument("--in_drop", type=float, default=.6,
-                        help="input feature dropout")
-    parser.add_argument("--attn_drop", type=float, default=.6,
-                        help="attention dropout")
-    parser.add_argument("--lr", type=float, default=0.005,
-                        help="learning rate")
-    parser.add_argument('--weight_decay', type=float, default=5e-4,
-                        help="weight decay")
-    parser.add_argument('--negative_slope', type=float, default=0.2,
-                        help="the negative slope of leaky relu")
-    parser.add_argument('--early_stop', action='store_true', default=False,
-                        help="indicates whether to use early stop or not")
-    parser.add_argument('--fastmode', action="store_true", default=False,
-                        help="skip re-evaluate the validation set")
+    parser.add_argument(
+        "--gpu", type=int, default=0, help="which GPU to use. Set -1 to use CPU."
+    )
+    parser.add_argument(
+        "--num_epochs", type=int, default=200, help="number of training epochs"
+    )
+    parser.add_argument(
+        "--num_heads", type=int, default=8, help="number of hidden attention heads"
+    )
+    parser.add_argument(
+        "--num_out_heads", type=int, default=1, help="number of output attention heads"
+    )
+    parser.add_argument(
+        "--num_layers", type=int, default=1, help="number of hidden layers"
+    )
+    parser.add_argument(
+        "--num_hidden", type=int, default=32, help="number of hidden units"
+    )
+    parser.add_argument(
+        "--residual", action="store_true", default=False, help="use residual connection"
+    )
+    parser.add_argument(
+        "--in_drop", type=float, default=0.6, help="input feature dropout"
+    )
+    parser.add_argument(
+        "--attn_drop", type=float, default=0.6, help="attention dropout"
+    )
+    parser.add_argument("--lr", type=float, default=0.005, help="learning rate")
+    parser.add_argument("--weight_decay", type=float, default=5e-4, help="weight decay")
+    parser.add_argument(
+        "--negative_slope",
+        type=float,
+        default=0.2,
+        help="the negative slope of leaky relu",
+    )
+    parser.add_argument(
+        "--early_stop",
+        action="store_true",
+        default=False,
+        help="indicates whether to use early stop or not",
+    )
+    parser.add_argument(
+        "--fastmode",
+        action="store_true",
+        default=False,
+        help="skip re-evaluate the validation set",
+    )
     args = parser.parse_args()
 
     print(args)
-        
+
     train(args)

docs/Makefile

@@ -5,8 +5,8 @@
 # from the environment for the first two.
 SPHINXOPTS    ?=
 SPHINXBUILD   ?= sphinx-build
-SOURCEDIR     = .
-BUILDDIR      = _build
+SOURCEDIR     = source
+BUILDDIR      = build
 
 # Put it first so that "make" without argument is like "make help".
 help: