diff --git a/nnvm/tests/python/frontend/onnx/test_forward.py b/nnvm/tests/python/frontend/onnx/test_forward.py
index 187e6c175cd410474734eb25f5fe800a7f9b38f4..7ca520a88b1248291e8ca34845e157bcaebb63b6 100644
--- a/nnvm/tests/python/frontend/onnx/test_forward.py
+++ b/nnvm/tests/python/frontend/onnx/test_forward.py
@@ -66,7 +66,7 @@ def get_caffe2_output(model, x, dtype='float32'):
 def verify_onnx_forward_impl(graph_file, data_shape, out_shape):
     dtype = 'float32'
     x = np.random.uniform(size=data_shape)
-    model = onnx.load(graph_file)
+    model = onnx.load_model(graph_file)
     c2_out = get_caffe2_output(model, x, dtype)
     for target, ctx in ctx_list():
         tvm_out = get_tvm_output(model, x, target, ctx, out_shape, dtype)
diff --git a/nnvm/tests/python/frontend/onnx/test_graph.py b/nnvm/tests/python/frontend/onnx/test_graph.py
index 0aad9d22f1bec0d6142f0875f4e0a79c6ccef36c..b3961c1a38fdd7d2008fea3e610d2891527cffd5 100755
--- a/nnvm/tests/python/frontend/onnx/test_graph.py
+++ b/nnvm/tests/python/frontend/onnx/test_graph.py
@@ -6,7 +6,7 @@ from model_zoo import super_resolution, super_resolution_sym
 from model_zoo import squeezenet as squeezenet
 
 def compare_graph(onnx_file, nnvm_sym, ishape):
-    onnx_model = onnx.load(onnx_file)
+    onnx_model = onnx.load_model(onnx_file)
     onnx_sym, params = nnvm.frontend.from_onnx(onnx_model)
     g1 = nnvm.graph.create(onnx_sym)
     g2 = nnvm.graph.create(nnvm_sym)
diff --git a/tutorials/nnvm/from_onnx.py b/tutorials/nnvm/from_onnx.py
index df8dee8272ce48ebfc31bb9b0dc9b2e6ab4fbe87..0fdef8afa98cfd0fadd479dcc23fc5df7cbed691 100644
--- a/tutorials/nnvm/from_onnx.py
+++ b/tutorials/nnvm/from_onnx.py
@@ -46,7 +46,7 @@ model_url = ''.join(['https://gist.github.com/zhreshold/',
                      'super_resolution_0.2.onnx'])
 download(model_url, 'super_resolution.onnx', True)
 # now you have super_resolution.onnx on disk
-onnx_model = onnx.load('super_resolution.onnx')
+onnx_model = onnx.load_model('super_resolution.onnx')
 # we can load the graph as NNVM compatible model
 sym, params = nnvm.frontend.from_onnx(onnx_model)