backend: "vllm"# The usage of device is deferred to the vLLM engine instance_group[{ count:1kind: KIND_MODEL } ] 2、启动docker:在model_repository同级目录下执行(会引用${PWD}变量): docker run --gpus all -it --rm -p 8000:8000 -p 8001:8001 -p 8002:8002 --shm-size=1G --ulimit ...
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...
cp -r models/vllm_opt models/vllm_load_test mkdir -p models/add_sub/1/ wget -P models/add_sub/1/ https://raw.githubusercontent.com/triton-inference-server/python_backend/main/examples/add_sub/model.py @@ -96,7 +103,7 @@ wait $SERVER_PID SERVER_ARGS="--model-repository=...
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
1、伺服 05-用 Triton 部署 vLLM 模型 - Deploying a vLLM model in Triton The following tutorial demonstrates how to deploy a simple facebook/opt-125m model on Triton Inference Server using the Triton's Python-based vLLM backend. 下面的教程展示了使用 Triton 的基于派森 vLLM 后端上,如何部署一...
In this example, we will use Triton 24.07 with TensorRT-LLM v0.11.0. Launch Triton TensorRT-LLM container Launch Triton docker containernvcr.io/nvidia/tritonserver:<xx.yy>-trtllm-python-py3with TensorRT-LLM backend. Make anenginesfolder outside docker to reuse engines for future runs. Make ...
TensorRT-LLM:github.com/NVIDIA/Tenso TensorRT-LLM Backend:github.com/triton-infer 创建并进入容器。 docker run -dt --name triton-server-6 \ --restart=always \ --gpus '"device=6"' \ --network=host \ --shm-size=32g \ -v /data/hpc/home/guodong.li/workspace:/workspace \ -w /workspace...
另外对于语⾔⼤模型的推理官⽅也推出了⼀个集成了vllm的triton server镜像,⼤家有兴趣可以尝试⽐较。 到这⾥完成了使⽤ triton server 以及 tensorRT-LLM 作为推理后端的服务部署和客户端利⽤ LlaMA2⼤语⾔模型的推理应⽤,这类推理应⽤可以扩展到其他领域的模型⽐如⽬标检测、图像识别等。
triton import -m gpt2 --backend vllm ``` 4. Run server: ```bash # Run server: triton start ``` ### Running GenAI-Perf ### Run GenAI-Perf 1. Run Triton Inference Server SDK container: Run GenAI-Perf from Triton Inference Server SDK container: ```bash export RELEASE="yy.mm" ...
框架后端支持情况:目前已经支持TensorRT-LLM、vLLM、PythonBackend、Pytorch Backend、onnxruntime、TensorFlow、TensorRT、FIL、DALI等后端 Response Cache:根据模型名字、模型版本、输入prompt缓存对应的输出,可以选择缓存到内存、redis数据库(也支持用户自己拓展新的缓存类型)。如果遇到同样的prompt,可以直接返回给用户,缓解...