adding the triton docker build minimal example (#242)

examples/usage/triton/models/character_generation/1/model.py

@@ -0,0 +1,45 @@
+import triton_python_backend_utils as pb_utils
+import numpy
+import sglang as sgl
+from sglang import function, set_default_backend
+from sglang.srt.constrained import build_regex_from_object
+
+from pydantic import BaseModel
+
+sgl.set_default_backend(sgl.RuntimeEndpoint("http://localhost:30000"))
+
+class Character(BaseModel):
+    name: str
+    eye_color: str
+    house: str
+
+@function
+def character_gen(s, name):
+    s += (
+        name
+        + " is a character in Harry Potter. Please fill in the following information about this character.\n"
+    )
+    s += sgl.gen("json_output", max_tokens=256, regex=build_regex_from_object(Character))
+
+
+class TritonPythonModel:
+    def initialize(self, args):
+        print("Initialized.")
+    def execute(self, requests):
+        responses = []
+        for request in requests:
+            tensor_in = pb_utils.get_input_tensor_by_name(request, "INPUT_TEXT")
+            if tensor_in is None:
+                return pb_utils.InferenceResponse(output_tensors=[])
+            
+            input_list_names = [i.decode('utf-8') if isinstance(i, bytes) else i for i in tensor_in.as_numpy().tolist()]
+
+            input_list_dicts = [{"name":i} for i in input_list_names]
+
+            states = character_gen.run_batch(input_list_dicts)
+            character_strs = [state.text() for state in states]
+
+            tensor_out = pb_utils.Tensor("OUTPUT_TEXT", numpy.array(character_strs, dtype=object))
+
+            responses.append(pb_utils.InferenceResponse(output_tensors = [tensor_out]))
+        return responses