create by cheetah

01jph5579a5rafy48p3yasaw1f.Dockerfile

@@ -0,0 +1,3 @@
+FROM registry.aifrica.co.kr/cheetah/cheetah-embedding:base
+#FROM alpine:latest
+COPY . /app/models/model

1/model.py

@@ -1,22 +0,0 @@
-import triton_python_backend_utils as pb_utils
-from transformers import AutoTokenizer, AutoModel
-import torch
-
-class TritonPythonModel:
-    def initialize(self, args):
-        self.tokenizer = AutoTokenizer.from_pretrained("../../kicon_e5large_15_v1")
-        self.model = AutoModel.from_pretrained("../../kicon_e5large_15_v1")
-        self.model.eval()
-
-    def execute(self, requests):
-        responses = []
-        for request in requests:
-            input_text = pb_utils.get_input_tensor_by_name(request, "text").as_numpy()[0].decode("utf-8")
-            inputs = self.tokenizer(input_text, return_tensors="pt", truncation=True, padding=True)
-            with torch.no_grad():
-                outputs = self.model(**inputs)
-            embedding = outputs.last_hidden_state.mean(dim=1).numpy()
-
-            out_tensor = pb_utils.Tensor("embedding", embedding)
-            responses.append(pb_utils.InferenceResponse(output_tensors=[out_tensor]))
-        return responses

config.pbtxt

@@ -1,25 +0,0 @@
-name: "kicon_e5large_15_v1"
-backend: "python"
-
-input [
-  {
-    name: "text"
-    data_type: TYPE_STRING
-    dims: [1]
-  }
-]
-
-output [
-  {
-    name: "embedding"
-    data_type: TYPE_FP32
-    dims: [1024]  # 임베딩 크기에 맞게 조정
-  }
-]
-
-max_batch_size: 0
-instance_group [
-  {
-    kind: KIND_GPU
-  }
-]

config.pbtxthttps:/cheetah.dev2.aifrica.co.kr/models/37/blob/main

@@ -1,20 +0,0 @@
-name: "kicon_e5large_15_v1"
-backend: "python"
-
-input [
-  {
-    name: "text"
-    data_type: TYPE_STRING
-    dims: [1]
-  }
-]
-
-output [
-  {
-    name: "embedding"
-    data_type: TYPE_FP32
-    dims: [1, 1024]
-  }
-]
-
-max_batch_size: 0