Windows11(CUDA11.7)下安装TensorRT

系列文章目录


前言

TensorRT有多厉害就不多说了,因为确实很好用。

作为在英伟达自家GPU上的推理库,这些年来一直被大力推广,更新也非常频繁,issue反馈也挺及时,社区的负责人员也很积极,简直不要太NICE。

那么我们应该如何入门呢 我们应该先安装好TensorRT 在博主研究了两天观摩了很多大佬的博客不断碰壁之后也做出了自己的总结 来进行Win11的 TensorRT的安装教程


一、本人环境 以及配置

CUDA11.7
cuDNN8.7
TensorRT8.5.2.2
可以看到博主N卡驱动版本是528.33 适配的CUDA版本号是11.7
在这里插入图片描述

二、安装步骤

首先CUDA和cuDNN

我这边找到两个链接没有安装的各位请根据这位博主的教程一步步安装
CUDA和cuDNN的安装教程链接:https://blog.youkuaiyun.com/jhsignal/article/details/111401628

TensorRT安装

通过上面博主的教程大家应该安装好了CUDA和cuDNN
下面根据自己的CUDA版本号来找对应的TensorRT的安装包
TensorRT下载链接:https://developer.nvidia.com/nvidia-tensorrt-8x-download
例如博主的cuda版本是11.7那么我下载的则是win系统下的8.5 GA Update 如图:
在这里插入图片描述
将该TensorRT文件解压后,如下图所示:
在这里插入图片描述

注:请根据自己的版本 来找对应的路径 例如博主这里是将 CUDA安装到了C盘 所以将 TensorRT中的文件放到了对应位置

核心重点:
将 TensorRT-8.5.2.2\include中头文件 copy 到C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\include
将TensorRT-8.5.2.2\lib 中所有lib文件 copy 到C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\lib\x64
将TensorRT-8.5.2.2\lib 中所有dll文件copy 到C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\bin

Python安装TensorRT库

我们在Anaconda Prompt中进行安装:
在这里插入图片描述
CD 进入TensorRT-8.5.2.2/python
在这里插入图片描述
这个cp38是根据自己的python版本来设置的 例如博主的python3.7那就是cp37
输入代码段

pip install tensorrt-8.2.3.0-cp38-none-win_amd64.whl

三、进行测试

建立一个python工程输入代码进行测试

import tensorrt as trt
if __name__ == "__main__":
    print(trt.__version__)
    print("hello trt!!")

结果
使用的Pycharm进行执行的终端效果

自编译tensorflow: 1.python3.5,tensorflow1.12; 2.支持cuda10.0,cudnn7.3.1,TensorRT-5.0.2.6-cuda10.0-cudnn7.3; 3.支持mkl,无MPI; 软硬件硬件环境:Ubuntu16.04,GeForce GTX 1080 配置信息: hp@dla:~/work/ts_compile/tensorflow$ ./configure WARNING: --batch mode is deprecated. Please instead explicitly shut down your Bazel server using the command "bazel shutdown". You have bazel 0.19.1 installed. Please specify the location of python. [Default is /usr/bin/python]: /usr/bin/python3 Found possible Python library paths: /usr/local/lib/python3.5/dist-packages /usr/lib/python3/dist-packages Please input the desired Python library path to use. Default is [/usr/local/lib/python3.5/dist-packages] Do you wish to build TensorFlow with XLA JIT support? [Y/n]: XLA JIT support will be enabled for TensorFlow. Do you wish to build TensorFlow with OpenCL SYCL support? [y/N]: No OpenCL SYCL support will be enabled for TensorFlow. Do you wish to build TensorFlow with ROCm support? [y/N]: No ROCm support will be enabled for TensorFlow. Do you wish to build TensorFlow with CUDA support? [y/N]: y CUDA support will be enabled for TensorFlow. Please specify the CUDA SDK version you want to use. [Leave empty to default to CUDA 10.0]: Please specify the location where CUDA 10.0 toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]: /usr/local/cuda-10.0 Please specify the cuDNN version you want to use. [Leave empty to default to cuDNN 7]: 7.3.1 Please specify the location where cuDNN 7 library is installed. Refer to README.md for more details. [Default is /usr/local/cuda-10.0]: Do you wish to build TensorFlow with TensorRT support? [y/N]: y TensorRT support will be enabled for TensorFlow. Please specify the location where TensorRT is installed. [Default is /usr/lib/x86_64-linux-gnu]:/home/hp/bin/TensorRT-5.0.2.6-cuda10.0-cudnn7.3/targets/x86_64-linux-gnu Please specify the locally installed NCCL version you want to use. [Default is to use https://github.com/nvidia/nccl]: Please specify a list of comma-separated Cuda compute capabilities you want to build with. You can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus. Please note that each additional compute capability significantly increases your build time and binary size. [Default is: 6.1,6.1,6.1]: Do you want to use clang as CUDA compiler? [y/N]: nvcc will be used as CUDA compiler. Please specify which gcc should be used by nvcc as the host compiler. [Default is /usr/bin/gcc]: Do you wish to build TensorFlow with MPI support? [y/N]: No MPI support will be enabled for TensorFlow. Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native -Wno-sign-compare]: Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]: Not configuring the WORKSPACE for Android builds. Preconfigured Bazel build configs. You can use any of the below by adding "--config=" to your build command. See .bazelrc for more details. --config=mkl # Build with MKL support. --config=monolithic # Config for mostly static monolithic build. --config=gdr # Build with GDR support. --config=verbs # Build with libverbs support. --config=ngraph # Build with Intel nGraph support. --config=dynamic_kernels # (Experimental) Build kernels into separate shared objects. Preconfigured Bazel build configs to DISABLE default on features: --config=noaws # Disable AWS S3 filesystem support. --config=nogcp # Disable GCP support. --config=nohdfs # Disable HDFS support. --config=noignite # Disable Apacha Ignite support. --config=nokafka # Disable Apache Kafka support. --config=nonccl # Disable NVIDIA NCCL support. Configuration finished 编译: hp@dla:~/work/ts_compile/tensorflow$ bazel build --config=opt --config=mkl --verbose_failures //tensorflow/tools/pip_package:build_pip_package 卸载已有tensorflow: hp@dla:~/temp$ sudo pip3 uninstall tensorflow 安装自己编译的成果: hp@dla:~/temp$ sudo pip3 install tensorflow-1.12.0-cp35-cp35m-linux_x86_64.whl
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