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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
-  }
-]