skip snmg example

python/cugraph-pyg/cugraph_pyg/examples/rgcn_link_class_snmg.py

@@ -15,6 +15,7 @@
 
 import os
 import argparse
+import warnings
 
 from typing import Tuple, Any
 
@@ -275,42 +276,45 @@ def get_eval_loader(stage: str):
 
 
 if __name__ == "__main__":
-    args = parse_args()
+    if "CI_RUN" in os.environ and os.environ["CI_RUN"] == "1":
+        warnings.warn("Skipping SMNG example in CI due to memory limit")
+    else:
+        args = parse_args()
 
-    # change the allocator before any allocations are made
-    from rmm.allocators.torch import rmm_torch_allocator
+        # change the allocator before any allocations are made
+        from rmm.allocators.torch import rmm_torch_allocator
 
-    torch.cuda.memory.change_current_allocator(rmm_torch_allocator)
+        torch.cuda.memory.change_current_allocator(rmm_torch_allocator)
 
-    # import ogb here to stop it from creating a context and breaking pytorch/rmm
-    from ogb.linkproppred import PygLinkPropPredDataset
+        # import ogb here to stop it from creating a context and breaking pytorch/rmm
+        from ogb.linkproppred import PygLinkPropPredDataset
 
-    data = PygLinkPropPredDataset(args.dataset, root=args.dataset_root)
-    dataset = data[0]
+        data = PygLinkPropPredDataset(args.dataset, root=args.dataset_root)
+        dataset = data[0]
 
-    splits = data.get_edge_split()
+        splits = data.get_edge_split()
 
-    meta = {}
-    meta["num_nodes"] = dataset.num_nodes
-    meta["num_rels"] = dataset.edge_reltype.max() + 1
+        meta = {}
+        meta["num_nodes"] = dataset.num_nodes
+        meta["num_rels"] = dataset.edge_reltype.max() + 1
 
-    model = RGCNEncoder(
-        meta["num_nodes"],
-        hidden_channels=args.hidden_channels,
-        num_relations=meta["num_rels"],
-    )
+        model = RGCNEncoder(
+            meta["num_nodes"],
+            hidden_channels=args.hidden_channels,
+            num_relations=meta["num_rels"],
+        )
 
-    print("Data =", data)
-    if args.n_devices == -1:
-        world_size = torch.cuda.device_count()
-    else:
-        world_size = args.n_devices
-    print("Using", world_size, "GPUs...")
-
-    uid = cugraph_comms_create_unique_id()
-    torch.multiprocessing.spawn(
-        run_train,
-        (world_size, uid, model, data, meta, splits, args),
-        nprocs=world_size,
-        join=True,
-    )
+        print("Data =", data)
+        if args.n_devices == -1:
+            world_size = torch.cuda.device_count()
+        else:
+            world_size = args.n_devices
+        print("Using", world_size, "GPUs...")
+
+        uid = cugraph_comms_create_unique_id()
+        torch.multiprocessing.spawn(
+            run_train,
+            (world_size, uid, model, data, meta, splits, args),
+            nprocs=world_size,
+            join=True,
+        )