string_value: "/opt/tritonserver/python_backend/models/chatglm2-6b" } } 创建model.py 自定义Python代码实现的模型推理逻辑 vi models/chatglm2-6b/1/model.py 模型的输入,输出和参数可以在这里使用python脚本进行加工处理 import triton_python_backend_utils as pb_utils class TritonPythonModel: @staticmeth...
importjsonimporttriton_python_backend_utilsaspb_utilsclassTritonPythonModel:definitialize(self,args):self.model_config=model_config=json.loads(args["model_config"])# Get OUTPUT configurationoutput0_config=pb_utils.get_output_config_by_name(model_config,"OUTPUT0")output1_config=pb_utils.get_output_...
用Model Ensemble 组装 Python Backend 和 ONNX 组成完整的推理服务 ✨ 注意:运行以下代码依赖 utils.py 文件和 mlp.py 文件。 一、CLIP 模型 import logging import torch import clip import utils from PIL import Image from transformers import CLIPProcessor, CLIPModel MODEL_PATH = 'workspace' DATA_PATH...
import triton_python_backend_utils as pb_utils from vllm.engine.arg_utils import AsyncEngineArgs from vllm.engine.async_llm_engine import AsyncLLMEngine from vllm.lora.request import LoRARequest from vllm.sampling_params import SamplingParams from vllm.utils import random_uuid _VLLM_ENGINE_ARGS...
importjsonimporttriton_python_backend_utilsaspb_utilsclassTritonPythonModel:definitialize(self,args):self.model_config=model_config=json.loads(args['model_config'])output0_config=pb_utils.get_output_config_by_name(model_config,"OUTPUT0")output1_config=pb_utils.get_output_config_by_name(model_conf...
git clonehttps:///triton-inference-server/python_backend-b r22.12 容器内操作:如果中途退出容器,使用命令 docker exec -it 容器名 /bin/bash 进入容器 如下载不下来可以拷贝到容器内:docker cp python\_backend busy\_galileo:/opt Step 4: 创建模型目录 ...
创建model.py 自定义Python代码实现的模型推理逻辑 vi models/chatglm2-6b/1/model.py 模型的输入,输出和参数可以在这里使用python脚本进行加工处理 importtriton_python_backend_utilsaspb_utilsclassTritonPythonModel:@staticmethoddefauto_complete_config(auto_complete_model_config):"""`auto_complete_config` is ...
responses = []# Every Python backend must iterate over everyone of the requests# and create a pb_utils.InferenceResponse for each of them.forrequestinrequests:# 获取请求数据in_0 = pb_utils.get_input_tensor_by_name(request,"input__0")# 第一个输出结果自己随便造一个假的,就假装是有逻辑了...
pip3 install numpy On Ubuntu or Debian you can use the command below to installrapidjson,libarchive, andzlib: sudo apt-get install rapidjson-dev libarchive-dev zlib1g-dev Build Python backend. Replace <GIT_BRANCH_NAME> with the GitHub branch that you want to compile. For release branches it...
git clone https://github.com/triton-inference-server/python_backend -b r22.12 容器内操作:如果中途退出容器,使用命令 docker exec -it 容器名 /bin/bash 进入容器 如下载不下来可以拷贝到容器内:docker cp python_backend busy_galileo:/opt Step 4: 创建模型目录 ...