Below is an example of how to serve a TensorRT-LLM model with the Triton TensorRT-LLM Backend on a 4-GPU environment. The example uses the GPT model from the TensorRT-LLM repository with the NGC Triton TensorRT-LLM container. Make sure you are cloning the same version of TensorRT-LLM ...
These two tests are ran in theL0_backend_trtllmtest. Below are the instructions to run the tests manually. Generate the model repository# Follow the instructions in theCreate the model repositorysection to prepare the model repository.
编译好engine后,会生成/work/trtModel/llama/1-gpu,后续会用到。 然后克隆https://github.com/triton-inference-server/tensorrtllm_backend: 执行以下命令: cd tensorrtllm_backend mkdir triton_model_repo # 拷贝出来模板模型文件夹 cp -r all_models/inflight_batcher_llm/* triton_model_repo/ # 将刚才生成...
The TRT-LLM backend supports requests with batch size greater than one. When sending a request with a batch size greater than one, the TRT-LLM backend will return multiple batch size 1 responses, where each response will be associated with a given batch index. An output tensor namedbatch_ind...
调研和复现TensorRT-LLM&tensorrtllm_backend整个端到端的过程大概一个多月了,踩坑的时候遇到过很多问题,难度不是很大但是作为入行不到半年的同学来说也是能力范围之内的。 尝试过适配其他模型到这个框架,这个难度感觉就有些超出能力范围了。需要修改的包括build.py(用于构建引擎,跟随后续函数修改,改动不多),weight....
模型推理功能的实现,在Triton里是通过一个backend的抽象来实现的。TensorRT-LLM就是其中一种backend,可以对接到Triton Inference Server里,提供最终的模型推理功能。所以,Triton不仅仅是只能和TensorRT-LLM集成使用,还可以和其他推理引擎集成,例如vLLM。 在对Triton Inference Server有了简单了解后,下面我们介绍如何实现部署...
TensorRT-LLM Backend The Triton backend forTensorRT-LLM. You can learn more about Triton backends in thebackend repo. The goal of TensorRT-LLM Backend is to let you serveTensorRT-LLMmodels with Triton Inference Server. Theinflight_batcher_llmdirectory contains the C++ implementation of the backend...
python3 tensorrtllm_backend/tools/fill_template.py -i${TRITON_REPO}/tensorrt_llm/config.pbtxt${OPTIONS} # 建立 /data/model 的软链(TIONE在线服务中,模型默认挂载到此处) mkdir-p /data ln-s${TRITON_REPO}/data/model # 本地启动 Triton 推理服务调试 ...
● 支持多种开源框架的部署,包括TensorFlow/PyTorch/ONNX Runtime/TensorRT 等,同时也支持用户提供自定义backend扩展解码引擎; ● 支持多个模型同时运行在 GPU 上,以提高 GPU 设备的利用率; ● 支持 HTTP/gRPC 协议,提供二进制格式扩展来压缩发送请求大小; ...
首先,创建一个模型库,以便Triton可以读取模型和任何相关元数据。tensorrtllm_backend存储库包含all_models/inflight_batcher_llm/下适当模型存储库的骨架。该目录中有以下子文件夹,其中包含模型执行过程不同部分的构件: /preprocessing和/postprocessing:包含适用于 Python 的 Triton 后端,用于在字符串和模型运行...