Python 后端的目标是让您能够用 Python为 Triton Inference Server 编写模型服务,而无需编写任何 C++ 代码。 用法 为了使用 Python Backend,您需要创建一个具有类似于以下结构的 Python 文件: importtriton_python_backend_utilsaspb_utilsclassTritonPythonModel:@staticmethoddefauto_complete_config(auto_complete_model_c...
介绍Triton 的 Python Backend,其通常用于模型预处理和后处理 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 CL...
vllm_engine_config["model"] = os.path.join(pb_utils.get_model_dir(), vllm_engine_config["model"]) vllm_engine_config["tokenizer"] = os.path.join(pb_utils.get_model_dir(), vllm_engine_config["tokenizer"]) # Create an AsyncLLMEngine from the config from JSON # TODO 读取模型和分...
python3 examples/add_sub/client.py UsageIn order to use the Python backend, you need to create a Python file that has a structure similar to below:import triton_python_backend_utils as pb_utils class TritonPythonModel: """Your Python model must use the same class name. Every Python model...
import triton_python_backend_utils as pb_utils class TritonPythonModel: """Your Python model must use the same class name. Every Python model that is created must have "TritonPythonModel" as the class name. """ # def initialize(self, args): # """`initialize` is called only once ...
response_sender.send(flags=pb_utils.TRITONSERVER_RESPONSE_COMPLETE_FINAL) raise ValueError("wait_secs cannot be negative") And this is config pbtxt name: "centerface" backend: "python" max_batch_size: 4 input [ { name: "INPUT0" data_type: TYPE_FP32 ...
typing as npt import torch import triton_python_backend_utils as pb_utils from torch.nn.functional import pad class TritonPythonModel: def initialize(self, args) -> None: self.logger = pb_utils.Logger self.cuda = torch.cuda.is_available() self.logger.log_info(f": initialize: CUDA ...
.path.abspath(__file__))+"/work/"# os.environ["CUDA_VISIBLE_DEVICES"] = '0,1,2'importgcimportjsonimportbase64importtorchimportnumpyasnpfrommarker.convertimportconvert_single_pdffrommarker.loggerimportconfigure_loggingfrommarker.modelsimportload_all_modelsimporttriton_python_backend_utilsaspb_utils...
本文要介绍的是以Triton作为推理服务器,以TensorRT作为推理后端的部署方案,其中Triton中的后端程序由Python实现,模型格式为TensorRT,使用Python后端下的TensorRT包实现对模型推理。 TensorRT+Triton环境搭建 笔者的环境为NVIDIA显卡驱动driver版本为535.154.05,cuda版本为12.2。下载Triton的Docker镜像,到NVIDIA查看符合cuda版本的...
模型文件model.py中必须定义TritonPythonModel类并实现其execute函数。该Python模型从每个request中读入两个输入INPUT0和INPUT1,获取两个输出OUTPUT0=INPUT0+INPUT1,OUTPUT1=INPUT0-INPUT1,将其封装成response返回。 importjsonimporttriton_python_backend_utilsaspb_utilsclassTritonPythonModel:definitialize(self,args):se...