You can install the vLLM backend directly into the NGC Triton container. In this case, please install vLLM first. You can do so by runningpipinstallvllm==<vLLM_version>. Then, set up the vLLM backend in the container with the following commands: mkdir-p/opt/tritonserver/backends/vllmg...
Triton不会做任何的调度处理,而是将请求全部打给vLLM,让vLLM根据PagedAttention和异步API自行处理请求,vLLM的调度策略更适配大语言模型decode场景的KV-Cache,提高GPU的利用率,因此在Triton+vLLM的组合中,由vLLM来负责调度,而Triton负责
wget -P model_repository/vllm_model/1 https://raw.githubusercontent.com/triton-inference-server/vllm_backend/r<xx.yy>/samples/model_repository/vllm_model/1/model.json wget -P model_repository/vllm_model/ https://raw.githubusercontent.com/triton-inference-server/vllm_backend/r<xx.yy>/sam...
构建完成的镜像:tritonserver:23.12-vllm-python-py3 启动 创建文件夹 mkdir -p /home/model_repository/vllm_model 将qwen模型放置到该目录下,同时创建config.pbtxt文件 vi /home/model_repository/vllm_model/config.pbtxt 内容如下 backend: "vllm" # The usage of device is deferred to thevLLMengine i...
vLLM: The vLLM backend is designed to runsupported modelson avLLM engine. This backend depends onpython_backendto load and serve models. Thevllm_backendrepo contains the documentation and source for the backend. Important Note!Not all the above backends are supported on every platform supported...
triton-inference-server/vllm_backendPublic NotificationsYou must be signed in to change notification settings Fork19 Star179
docker run --rm -it --net host --shm-size=2g \ --ulimit memlock=-1 --ulimit stack=67108864 --gpus all \ -v </path/to/tensorrtllm_backend>:/tensorrtllm_backend \ -v </path/to/engines>:/engines \ nvcr.io/nvidia/tritonserver:24.07-trtllm-python-py3 Pre...
下面我们先使用TensorRT-LLM来进行模型的推理,然后介绍TensorRT-LLM在提升推理性能上做的部分优化。 3.1. 设置TensorRT-LLM环境 下面我们参考TensorRT-LLM的官网[1]进行设置。 # 安装docker sudo apt-get install docker # 部署nvidia ubuntu容器 docker run --runtime=nvidia --gpus all -v /home/ubuntu/data:/...
在tensorrtllm_backend项目中tensor_llm目录中拉取TensorRT-LLM项目代码 代码语言:javascript 代码运行次数:0 运行 AI代码解释 git clone https://github.com/NVIDIA/TensorRT-LLM.git 注意分支版本的一致,我是拉取的-b v0.5.0分支。(拉取分支主要注意TensorRT-LLM中的/docker/common/install_tensorrt.sh中cuda版本...
一般来说,我们都是从最主要的server开始编,编译的时候会链接core、common、backend中的代码,其他自定义backend(比如tensorrt_backend)在编译的时候也需要带上common、core、backend这三个仓库,这些关系我们可以从相应的CMakeList中找到。 自行编译 如果想要研究源码,修改源码实现客制化,那么自行编译是必须的。