Add Additional Check For SSSP Source Vertex & Fix SSSP Benchmark

benchmarks/cugraph/pytest-based/bench_algos.py

@@ -330,8 +330,17 @@ def bench_bfs(gpubenchmark, graph):
 
 
 def bench_sssp(gpubenchmark, graph):
+    if not graph.is_weighted():
+        pytest.skip("Skipping: Unweighted Graphs are not supported by SSSP")
+
     sssp = dask_cugraph.sssp if is_graph_distributed(graph) else cugraph.sssp
-    start = graph.edgelist.edgelist_df["src"][0]
+
+    start_col = graph.select_random_vertices(num_vertices=1)
+    if is_graph_distributed(graph):
+        start_col = start_col.compute()
+
+    start = start_col.to_arrow().to_pylist()[0]
+
     gpubenchmark(sssp, graph, start)
 
 

benchmarks/pytest.ini

@@ -1,67 +1,67 @@
 [pytest]
 pythonpath =
-             shared/python
+    shared/python
 
 testpaths =
-            cugraph/pytest_based
-	    cugraph-service/pytest_based
+    cugraph/pytest_based
+    cugraph-service/pytest_based
 
 addopts =
-          --benchmark-columns="min, max, mean, stddev, outliers"
+    --benchmark-columns="min, max, mean, stddev, outliers"
 
 markers =
-          managedmem_on: RMM managed memory enabled
-          managedmem_off: RMM managed memory disabled
-          poolallocator_on: RMM pool allocator enabled
-          poolallocator_off: RMM pool allocator disabled
-          tiny: tiny datasets
-          small: small datasets
-	  medium: medium datasets
-          large: large datasets
-          directed: directed datasets
-          undirected: undirected datasets
-	  matrix_types: inputs are matrices
-	  nx_types: inputs are NetowrkX Graph objects
-	  cugraph_types: inputs are cuGraph Graph objects
-	  sg: single-GPU
-	  mg: multi-GPU
-	  snmg: single-node multi-GPU
-	  mnmg: multi-node multi-GPU
-	  local: local cugraph
-	  remote: cugraph-service
-	  batch_size_100: batch size of 100 for sampling algos
-	  batch_size_500: batch size of 500 for sampling algos
-	  batch_size_1000: batch size of 1000 for sampling algos
-	  batch_size_2500: batch size of 2500 for sampling algos
-	  batch_size_5000: batch size of 5000 for sampling algos
-	  batch_size_10000: batch size of 10000 for sampling algos
-	  batch_size_20000: batch size of 20000 for sampling algos
-	  batch_size_30000: batch size of 30000 for sampling algos
-	  batch_size_40000: batch size of 40000 for sampling algos
-	  batch_size_50000: batch size of 50000 for sampling algos
-	  batch_size_60000: batch size of 60000 for sampling algos
-	  batch_size_70000: batch size of 70000 for sampling algos
-	  batch_size_80000: batch size of 80000 for sampling algos
-	  batch_size_90000: batch size of 90000 for sampling algos
-	  batch_size_100000: batch size of 100000 for sampling algos
-	  num_clients_2: start 2 cugraph-service clients
-	  num_clients_4: start 4 cugraph-service clients
-	  num_clients_8: start 8 cugraph-service clients
-	  num_clients_16: start 16 cugraph-service clients
-	  num_clients_32: start 32 cugraph-service clients
-	  fanout_10_25: fanout [10, 25] for sampling algos
-	  fanout_5_10_15: fanout [5, 10, 15] for sampling algos
-	  rmat_data: RMAT-generated synthetic datasets
-	  file_data: datasets from $RAPIDS_DATASET_ROOT_DIR
+    managedmem_on: RMM managed memory enabled
+    managedmem_off: RMM managed memory disabled
+    poolallocator_on: RMM pool allocator enabled
+    poolallocator_off: RMM pool allocator disabled
+    tiny: tiny datasets
+    small: small datasets
+    medium: medium datasets
+    large: large datasets
+    directed: directed datasets
+    undirected: undirected datasets
+    matrix_types: inputs are matrices
+    nx_types: inputs are NetowrkX Graph objects
+    cugraph_types: inputs are cuGraph Graph objects
+    sg: single-GPU
+    mg: multi-GPU
+    snmg: single-node multi-GPU
+    mnmg: multi-node multi-GPU
+    local: local cugraph
+    remote: cugraph-service
+    batch_size_100: batch size of 100 for sampling algos
+    batch_size_500: batch size of 500 for sampling algos
+    batch_size_1000: batch size of 1000 for sampling algos
+    batch_size_2500: batch size of 2500 for sampling algos
+    batch_size_5000: batch size of 5000 for sampling algos
+    batch_size_10000: batch size of 10000 for sampling algos
+    batch_size_20000: batch size of 20000 for sampling algos
+    batch_size_30000: batch size of 30000 for sampling algos
+    batch_size_40000: batch size of 40000 for sampling algos
+    batch_size_50000: batch size of 50000 for sampling algos
+    batch_size_60000: batch size of 60000 for sampling algos
+    batch_size_70000: batch size of 70000 for sampling algos
+    batch_size_80000: batch size of 80000 for sampling algos
+    batch_size_90000: batch size of 90000 for sampling algos
+    batch_size_100000: batch size of 100000 for sampling algos
+    num_clients_2: start 2 cugraph-service clients
+    num_clients_4: start 4 cugraph-service clients
+    num_clients_8: start 8 cugraph-service clients
+    num_clients_16: start 16 cugraph-service clients
+    num_clients_32: start 32 cugraph-service clients
+    fanout_10_25: fanout [10, 25] for sampling algos
+    fanout_5_10_15: fanout [5, 10, 15] for sampling algos
+    rmat_data: RMAT-generated synthetic datasets
+    file_data: datasets from $RAPIDS_DATASET_ROOT_DIR
 
 python_classes =
-                 Bench*
-                 Test*
+    Bench*
+    Test*
 
 python_files =
-                 bench_*
-                 test_*
+    bench_*
+    test_*
 
 python_functions =
-                   bench_*
-                   test_*
+    bench_*
+    test_*