.gitattributes

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1/model.py

@@ -0,0 +1,38 @@
+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

config.pbtxt

@@ -7,7 +7,7 @@ input [
   {
     name: "INPUT"
     data_type: TYPE_STRING
-    dims: [ 1 ]
+    dims: [ -1 ]
   }
 ]
 
@@ -15,7 +15,7 @@ output [
   {
     name: "OUTPUT"
     data_type: TYPE_STRING
-    dims: [ 1 ]
+    dims: [ -1 ]
   }
 ]
 
@@ -33,4 +33,4 @@ instance_group [
     kind: KIND_AUTO
     count: 1
   }
-]
+]

my-model.py

@@ -1,58 +0,0 @@
-import triton_python_backend_utils as pb_utils
-import numpy as np
-import random
-import string
-import json
-
-class TritonPythonModel:
-    """
-    Triton Python Model 클래스.
-    """
-
-def initialize(self, args):
-    print("TritonPythonModel: initialize() called.")
-    self.model_config = json.loads(args['model_config'])
-    
-    # 출력 설정에서 데이터 타입 정보를 가져옴
-    output_config = pb_utils.get_output_config_by_name(
-        self.model_config, "OUTPUT")
-    
-    # Triton 데이터 타입 문자열을 NumPy 데이터 타입으로 직접 변환
-    # 'BYTES'는 np.object_ 타입에 해당함
-    if output_config['data_type'] == 'TYPE_STRING':
-        self.output_dtype = np.object_
-    else:
-        # 다른 데이터 타입에 대한 처리 로직 추가 가능
-        self.output_dtype = pb_utils.triton_string_to_np_dtype(output_config['data_type'])
-
-    def execute(self, requests):
-        """
-        클라이언트의 요청이 들어올 때마다 호출됩니다.
-        """
-        responses = []
-
-        for request in requests:
-            # 입력 데이터 가져오기 (이 예제에서는 입력 데이터를 사용하지 않음)
-            in_0 = pb_utils.get_input_tensor_by_name(request, "INPUT")
-            
-            # 무작위 문자열 생성
-            random_string = ''.join(random.choice(string.ascii_letters + string.digits) for _ in range(16))
-            
-            # numpy 배열로 변환
-            output_array = np.array([[random_string.encode('utf-8')]], dtype=self.output_dtype)
-            
-            # 출력 텐서 생성
-            out_tensor = pb_utils.Tensor("OUTPUT", output_array)
-            
-            # 추론 응답 생성
-            inference_response = pb_utils.InferenceResponse(
-                output_tensors=[out_tensor])
-            responses.append(inference_response)
-
-        return responses
-
-    def finalize(self):
-        """
-        모델이 언로드될 때 호출됩니다.
-        """
-        print("TritonPythonModel: finalize() called.")