$ ./bin/segmentation_tutorial [01/07/2022-20:20:34] [I] [TRT] [MemUsageChange] Init CUDA: CPU +322, GPU +0, now: CPU 463, GPU 707 (MiB) [01/07/2022-20:20:34] [I] [TRT] Loaded engine size: 132 MiB [01/07/2022-20:20:35] [I] [TRT] [MemUsageChange] Init cuBLAS/cu...
也可以准备NVIDIA Docker拉取对应版本的nvidia/cuda镜像,再ADDTensorRT即可。 # 解压进 $HOME (以免 sudo 编译样例,为当前用户)tar -xzvf TensorRT-*.tar.gz -C$HOME/# 软链到 /usr/local/TensorRT (以固定一个路径)sudoln-s$HOME/TensorRT-8.2.2.1 /usr/local/TensorRT 之后,编译运行样例,保证 TensorRT 安...
打开tutorial-runtime.ipynb 笔记本,并按照其步骤操作。 TensorRT Python运行时API直接映射到在C ++中运行引擎中描述的C ++ API 。 8.其他资源 参考官方文档 8.1。词汇表 Builder TensorRT的模型优化器。构建器将网络定义作为输入,执行与设备无关和针对特定设备的优化,并创建引擎。有关构建器的更多信息,请参见Builde...
答：此处提示SM相关错误，所以可以检查makefile或CMakeLists.txt中对nvcc编译器option的设定是否存在问题。
Check out the Multi-Node Generative AI w/ Triton Server and TensorRT-LLM tutorial for Triton Server and TensorRT-LLM multi-node deployment. Model Parallelism Tensor Parallelism, Pipeline Parallelism and Expert Parallelism Tensor Parallelism, Pipeline Parallelism and Expert parallel...
近10年CUDA开发经验,近5年TensorRT 开发经验,Github TensorRT_Tutorial作者。 康博 高级研究员,主要方向为自然语言处理、智能语音及其在端侧的部署。博士毕业于清华大学,在各类国际AI会议和刊物中发表论文10篇以上,多次获得NIST主办的国际比赛top2成绩。近年来主要研究方向为AI在场景中的落地应用。 深度学习算法商业化...
fix user_guide and tutorial docs by @yoosful in #2854 chore: Make from and to methods use the same TRT API by @narendasan in #2858 add aten.topk implementation by @lanluo-nvidia in #2841 feat: support aten.atan2.out converter by @chohk88 in #2829 chore: update docker, refactor CI...
See the MIG tutorial for more details on how to run TRT-LLM models and Triton with MIG.SchedulingThe scheduler policy helps the batch manager adjust how requests are scheduled for execution. There are two scheduler policies supported in TensorRT-LLM, MAX_UTILIZATION and GUARANTEED_NO_EVICT. See...
tar -xzvf TensorRT-*.tar.gz -C $HOME/ # 软链到 /usr/local/TensorRT (以固定一个路径) sudo ln -s $HOME/TensorRT-8.2.2.1 /usr/local/TensorRT 1. 2. 3. 4. 之后,编译运行样例,保证 TensorRT 安装正确。 编译样例 样例在 TensorRT/samples,说明见 Sample Support Guide...
这类方法简单有效,适合不精通C++但需要加速的人群,可以参考如下工程: yoloX:https://github.com/Megvii-BaseDetection/YOLOXOcean:https://github.com/researchmm/TracKit/blob/master/lib/tutorial/Ocean/ocean.md