diff --git a/Jenkinsfile b/Jenkinsfile
index 305f3b9e0d86..b615f8e3fca4 100644
--- a/Jenkinsfile
+++ b/Jenkinsfile
@@ -45,7 +45,7 @@
 //
 
 ci_lint = "tvmai/ci-lint:v0.51"
-ci_gpu = "tvmai/ci-gpu:v0.54"
+ci_gpu = "tvmai/ci-gpu:v0.55"
 ci_cpu = "tvmai/ci-cpu:v0.54"
 ci_i386 = "tvmai/ci-i386:v0.52"
 
diff --git a/tutorials/frontend/build_gcn.py b/tutorials/frontend/build_gcn.py
index e97c83c6c44e..077b066f7f62 100644
--- a/tutorials/frontend/build_gcn.py
+++ b/tutorials/frontend/build_gcn.py
@@ -46,6 +46,7 @@
 import torch.nn as nn
 import torch.nn.functional as F
 import dgl
+import networkx as nx
 from dgl.nn.pytorch import GraphConv
 
 class GCN(nn.Module):
@@ -88,7 +89,7 @@ def load_dataset(dataset="cora"):
 
     # Remove self-loops to avoid duplicate passing of a node's feature to itself
     g = data.graph
-    g.remove_edges_from(g.selfloop_edges())
+    g.remove_edges_from(nx.selfloop_edges(g))
     g.add_edges_from(zip(g.nodes, g.nodes))
 
     return g, data
@@ -110,7 +111,7 @@ def evaluate(data, logits):
 Parameters
 ----------
 dataset: str
-    Name of dataset. You can choose from ['cora', 'citeseer', 'pubmed']. 
+    Name of dataset. You can choose from ['cora', 'citeseer', 'pubmed'].
 
 num_layer: int
     number of hidden layers
@@ -251,7 +252,7 @@ def GraphConv(layer_name,
 ######################################################################
 # Prepare the parameters needed in the GraphConv layers
 # ------------------
-# 
+#
 import numpy as np
 import networkx as nx