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1/model.py

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+import triton_python_backend_utils as pb_utils
+import numpy as np
+import json
+import random
+import string
+
+class TritonPythonModel:
+    def initialize(self, args):
+        self.logger = pb_utils.Logger
+        self.logger.log_info(f"'{args["model_name"]}' 모델 초기화 완료")
+
+    def execute(self, requests):
+        responses = []
+
+        for request in requests:
+            input_tensor = pb_utils.get_input_tensor_by_name(request, "INPUT")
+            
+            input_data = input_tensor.as_numpy()[0].decode("utf-8")
+            self.logger.log_info(f"INPUT: {input_data}")
+
+            random_string = ''.join(
+                random.choice(string.ascii_letters + string.digits) for _ in range(10)
+            )
+            self.logger.log_info(f"OUTPUT: {random_string}")
+
+            output_tensor = pb_utils.Tensor(
+                "OUTPUT",
+                np.array([random_string.encode("utf-8")], dtype=np.object_)
+            )
+
+            responses.append(pb_utils.InferenceResponse(
+                output_tensors=[output_tensor]
+            ))
+
+        return responses
+
+    def finalize(self):
+        pass

1/test.txt

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+test

README.md

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-lll

config.pbtxt

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+# Triton Backend for Python.
+backend: "python"
+max_batch_size: 0
+
+input [
+  {
+    name: "INPUT"
+    data_type: TYPE_STRING
+    dims: [ -1 ]
+  }
+]
+
+output [
+  {
+    name: "OUTPUT"
+    data_type: TYPE_STRING
+    dims: [ -1 ]
+  }
+]
+
+
+parameters: [
+  {
+    key: "model_path",
+    value: {string_value: "/cheetah/input/model/groupuser/DBInc-Sample"}
+  }
+]
+
+
+instance_group [
+  {
+    kind: KIND_AUTO
+    count: 1
+  }
+]