Add application file

app.py

@@ -0,0 +1,40 @@
+# ----------- Triton Model Code Starts Below -------------
+
+import json
+import triton_python_backend_utils as pb_utils
+import numpy as np
+
+class TritonPythonModel:
+    def initialize(self, args):
+        self.model_config = model_config = json.loads(args["model_config"])
+
+        output0_config = pb_utils.get_output_config_by_name(model_config, "OUTPUT0")
+        output1_config = pb_utils.get_output_config_by_name(model_config, "OUTPUT1")
+
+        self.output0_dtype = pb_utils.triton_string_to_numpy(output0_config["data_type"])
+        self.output1_dtype = pb_utils.triton_string_to_numpy(output1_config["data_type"])
+
+    def execute(self, requests):
+        output0_dtype = self.output0_dtype
+        output1_dtype = self.output1_dtype
+        responses = []
+
+        for request in requests:
+            in_0 = pb_utils.get_input_tensor_by_name(request, "INPUT0")
+            in_1 = pb_utils.get_input_tensor_by_name(request, "INPUT1")
+
+            out_0 = in_0.as_numpy() + in_1.as_numpy()
+            out_1 = in_0.as_numpy() - in_1.as_numpy()
+
+            out_tensor_0 = pb_utils.Tensor("OUTPUT0", out_0.astype(output0_dtype))
+            out_tensor_1 = pb_utils.Tensor("OUTPUT1", out_1.astype(output1_dtype))
+
+            inference_response = pb_utils.InferenceResponse(
+                output_tensors=[out_tensor_0, out_tensor_1]
+            )
+            responses.append(inference_response)
+
+        return responses
+
+    def finalize(self):
+        print("Cleaning up...")