https://arleyzhang.github.io/articles/fda11be6/ https://docs.nvidia.com/deeplearning/sdk/tensorrt-api/c_api/index.html https://docs.nvidia.com/deeplearning/sdk/tensorrt-api/python_api/index.html https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html 6. 同期文章 深度学...
# Create the model repository that will be used by the Triton servercd /tensorrtllm_backend # 假设tensorrtllm_backend源码下载到这个位置mkdir triton_model_repo# Copy the example models to the model repositorycp -r all_models/inflight_batcher_llm/* triton_model_repo/# Copy the TRT engine to ...
head=create_head(nf,3,None,ps=0.5,bn_final=None)model=nn.Sequential(body,head)state=torch.load('new-mobilenetv2-128_S.pth',map_location=device)model.load_state_dict(state['model'],strict=True)example=torch.rand(1,3,128,128).cuda()model.to(device)# 导出onnx模型 torch_out=torch.onnx...
Here we take TensorRT C++ runtime APIs as an example to run the exported RVM TRT model. rvm_infer.cc complete code. 1. Load the model: create runtime , deserialize the data of the TRT model file static Logger logger{Logger::Severity::kINFO}; auto runtime = std::unique_ptr<nvinfer...
The code in this repository was tested on both Jetson Nano and Jetson TX2 Devkits. In order to run the demos below, first make sure you have the proper version of image (JetPack) installed on the target Jetson system. For example, this is my blog post about setting up a Jetson Nano:...
git clone -b master https://github.com/nvidia/TensorRT TensorRT cd TensorRT git submodule update --init --recursive $Env:TRT_SOURCE = $(Get-Location) Download TensorRT GA To build TensorRT OSS, obtain the corresponding TensorRT GA build from NVIDIA Developer Zone. Example: Ubuntu 18.04 on ...
https://github.com/NVIDIA/TensorRT/issues/1556 https://github.com/NVIDIA/TensorRT/issues/1519 目前来看还是无法解决的: thanks for update, we will check, and the c%4 will not work for ConvTranspose , it is for depthConv. 部分量化结果会错误解析 tactic : ampere_scudnn_128x64_relu_interior...
使用Tensorrt部署,C++ API yolov7_pose模型 虽然标题叫部署yolov7_pose模型,但是接下来的教程可以使用Tensorrt部署任何pytorch模型。 仓库地址:https://github.com/WongKinYiu/yolov7/tree/pose 系统版本:ub
按照上一节的讲解,我们知道TensorRT的例程主要是分为Build和Deployment(infer)这两个步骤,接下来我们就按照参数初始化,Build,Deployment这个顺序来看看代码。 2.1 主函数 sampleMNIST例程的主函数代码实现如下: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 ...
sampleMNIST的github 代码参考link:https:///NVIDIA/TensorRT/blob/release/6.0/samples/opensource/sampleMNIST/sampleMNIST.cpp 程序的主要流程分为 main与程序输入参数初始化 -> 网络构建 -> 网络推理 -> 释放资源结束 这几个阶段,下面逐个阶段分析代码