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...
5、vLLM Model 配置示例 这些仓库包含了什么? 这些仓库包含以下资源: 1、概念指南:这份指南侧重于构建推理基础设施时 所面临的一般性挑战,以及如何通过 Triton Inference Server 来最好地解决这些挑战。 2、快速部署:这是一套关于将您偏好的框架中的模型部署到 Triton 推理服务器的指南。这些指南假设您对 Triton In...
curl-XPOSTlocalhost:8000/v2/models/vllm_model/generate-d'{"text_input": "What is Triton Inference Server?", "parameters": {"stream": false, "temperature": 0}}' {"model_name":"vllm_model","model_version":"1","text_output":"What is Triton Inference Server?Triton Inference...
vllm:time_to_first_token_seconds_bucket{model="vllm_model",version="1",le="+Inf"}1# HELP vllm:time_per_output_token_seconds Histogram of time per output token in seconds.# TYPE vllm:time_per_output_token_seconds histogramvllm:time_per_output_token_seconds_count{model="vllm_model",v...
ArgumentParser( prog="GEMM tutorial example", allow_abbrev=False, ) parser.add_argument("-v", action='store_true', default=False, help="Print out the best tuning config") args = parser.parse_args() return args def main(): # assign to a global verbose var to indicate whether print #...
您可以使用 Kubernetes 将经过优化的大语言模型(LLMs)的部署从单个 GPU 扩展到多个 GPU,以低延迟、高准确度处理数千个实时推理请求,并在推理请求数量减少时缩减 GPU 数量。这对于在线购物和呼叫中心等企业特别有用,因为它们可以在高峰和非高峰时段灵活处理不同数量的推理请求,同时受益于总成本的降低,而不是购买数量...
docker run--gpus=1--rm--net=host-v ${PWD}/model_repository:/models nvcr.io/nvidia/tritonserver:22.09-py3 tritonserver--model-repository=/models # 第三步,发送 # In a separate console,launch the image_client example from theNGCTritonSDKcontainer ...
openai_trtllm support custom history templates to convert message history to prompt for chat models. The template engine used here is liquid. Follow the syntax to create your own template. For examples of history templates, see the templates folder. Here's an example of llama3: {% for item...
搭载TensorRT-LLM 后端的 NVIDIA Triton 本教程使用 StarCoder,这是一个 155 亿个参数 LLM,使用 The Stack (v1。2)中的 80 多种编程语言进行训练。StarCoder 的基础模型使用来自 80 多种编程语言、GitHub 问题、Git Commits 和 Jupyter Notebooks 的 1 万亿个令牌进行训练。StarCoder 在其之上使用另外...
VLLM An intermediate example expanding further on the concepts introduced in the Hello World example. In this example, we demonstrate Disaggregated Serving as an application of the components defined in Triton Distributed. Disclaimers Note This project is currently in the alpha / experimental / rapid...