Ubuntu22.04安装cuda12.1+cudnn8.9.2+TensorRT8.6.1+pytorch2.3.0+opencv_cuda4.9+onnxruntime-gpu1.18

说来话长，我想配一个一劳永逸的环境，方便以后复用。省的以后反复查教程重新装了

1. 安装miniconda+py3.10

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cd /root
wget -q https://repo.anaconda.com/miniconda/Miniconda3-py310_24.4.0-0-Linux-x86_64.sh
bash ./Miniconda3-py310_24.4.0-0-Linux-x86_64.sh -b -f -p /root/miniconda3
rm -f ./Miniconda3-py310_24.4.0-0-Linux-x86_64.sh
echo "PATH=/root/miniconda3/bin:/usr/local/bin:$PATH" >> /etc/profile
echo "source /etc/profile" >> /root/.bashrc
# 初始化miniconda
conda init

2. 本机安装cudatoolkit 12.1

这块内容来自：https://docs.infini-ai.com/posts/install-cuda-on-devmachine.html

首先更新系统包列表

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sudo apt update

以 Runfile 的方式安装系统级 CUDA 12.1.1

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# 下载 CUDA Toolkit 安装包
wget https://developer.download.nvidia.com/compute/cuda/12.1.1/local_installers/cuda_12.1.1_530.30.02_linux.run
# 安装 CUDA Toolkit
sudo sh cuda_12.1.1_530.30.02_linux.run

稍等片刻，会提示接受 EULA 协议。输入 accept 接受协议。

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┌──────────────────────────────────────────────────────────────────────────────┐
│  End User License Agreement                                                  │
│  --------------------------                                                  │
│                                                                              │
│  NVIDIA Software License Agreement and CUDA Supplement to                    │
│  Software License Agreement. Last updated: October 8, 2021                   │
│                                                                              │
│  The CUDA Toolkit End User License Agreement applies to the                  │
│  NVIDIA CUDA Toolkit, the NVIDIA CUDA Samples, the NVIDIA                    │
│  Display Driver, NVIDIA Nsight tools (Visual Studio Edition),                │
│  and the associated documentation on CUDA APIs, programming                  │
│  model and development tools. If you do not agree with the                   │
│  terms and conditions of the license agreement, then do not                  │
│  download or use the software.                                               │
│                                                                              │
│  Last updated: October 8, 2021.                                              │
│                                                                              │
│                                                                              │
│  Preface                                                                     │
│  -------                                                                     │
│                                                                              │
│──────────────────────────────────────────────────────────────────────────────│
│ Do you accept the above EULA? (accept/decline/quit):                         │
│                                                                              │
└──────────────────────────────────────────────────────────────────────────────┘

输入accept,同意协议后，按照提示进行安装，选择自定义安装，只选择 CUDA Toolkit 和相关库。

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┌──────────────────────────────────────────────────────────────────────────────┐
│ CUDA Installer                                                               │
│ - [ ] Driver                                                                 │
│      [ ] 520.61.05                                                           │
│ + [X] CUDA Toolkit 12.1                                                      │
│   [X] CUDA Demo Suite 12.1                                                   │
│   [X] CUDA Documentation 12.1                                                │
│ - [ ] Kernel Objects                                                         │
│      [ ] nvidia-fs                                                           │
│   Options                                                                    │
│   Install                                                                    │
│                                                                              │
│                                                                              │
│                                                                              │
│                                                                              │
│                                                                              │
│                                                                              │
│                                                                              │
│                                                                              │
│                                                                              │
│                                                                              │
│                                                                              │
│                                                                              │
│ Up/Down: Move | Left/Right: Expand | 'Enter': Select | 'A': Advanced options │
└──────────────────────────────────────────────────────────────────────────────┘

安装完成后，输出如下：

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===========
= Summary =
===========

Driver:   Not Selected
Toolkit:  Installed in /usr/local/cuda-12.1/

Please make sure that
 -   PATH includes /usr/local/cuda-12.1/bin
 -   LD_LIBRARY_PATH includes /usr/local/cuda-12.1/lib64, or, add /usr/local/cuda-12.1/lib64 to /etc/ld.so.conf and run ldconfig as root

To uninstall the CUDA Toolkit, run cuda-uninstaller in /usr/local/cuda-12.1/bin
***WARNING: Incomplete installation! This installation did not install the CUDA Driver. A driver of version at least 520.00 is required for CUDA 12.1 functionality to work.
To install the driver using this installer, run the following command, replacing <CudaInstaller> with the name of this run file:
    sudo <CudaInstaller>.run --silent --driver

Logfile is /var/log/cuda-installer.log

接下来配置 CUDA 环境变量

设置 PATH、LD_LIBRARY_PATH 和 CUDA_HOME（通用路径和 CUDA 12.1 特定路径）：

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echo 'export PATH=/usr/local/cuda/bin:/usr/local/cuda-12.1/bin${PATH:+:${PATH}}' >> ~/.bashrc
echo 'export LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda-12.1/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}' >> ~/.bashrc
echo 'export CUDA_HOME=/usr/local/cuda' >> ~/.bashrc

将 CUDA 库路径添加到 /etc/ld.so.conf：

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echo '/usr/local/cuda/lib64' | sudo tee -a /etc/ld.so.conf
echo '/usr/local/cuda-12.1/lib64' | sudo tee -a /etc/ld.so.conf

运行 ldconfig：

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sudo ldconfig

应用更改到当前会话：

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source ~/.bashrc

验证路径设置：

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echo $PATH | grep -E "cuda|cuda-12.1"
echo $LD_LIBRARY_PATH | grep -E "cuda|cuda-12.1"
echo $CUDA_HOME
ldconfig -p | grep "libcudart"

验证nvcc可用否：

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nvcc --version

输出：

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(base) root@autodl-container-39eb4a843f-12e69afc:~/autodl-tmp/code# nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2023 NVIDIA Corporation
Built on Mon_Apr__3_17:16:06_PDT_2023
Cuda compilation tools, release 12.1, V12.1.105
Build cuda_12.1.r12.1/compiler.32688072_0
(base) root@autodl-container-39eb4a843f-12e69afc:~/autodl-tmp/code#

3. 安装系统级cudnn 8.9.2.26

我们这里选择tarball方式安装，首先wget下载cudnn安装包

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wget -O cudnn-linux-x86_64-8.9.2.26_cuda12-archive.tar.xz https://developer.download.nvidia.com/compute/cudnn/redist/cudnn/linux-x86_64/cudnn-linux-x86_64-8.9.2.26_cuda12-archive.tar.xz

随后解压并安装

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# 解压安装包
tar -xvf cudnn-linux-x86_64-8.9.2.26_cuda12-archive.tar.xz
cd cudnn-linux-x86_64-8.9.2.26_cuda12-archive
# 将解压后的文件复制到cuda安装路径
sudo cp -r include/* /usr/local/cuda/include
sudo cp -r lib/* /usr/local/cuda/lib64
# 也可以将其复制到 /usr/include/ 和 /usr/lib/x86_64-linux-gnu/
# 修改文件权限
sudo chmod a+r /usr/local/cuda/include/cudnn*.h
sudo chmod a+r /usr/local/cuda/lib64/libcudnn*
# 添加环境变量
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH
# 刷新ldconfig
sudo ldconfig

其他安装方式，例如包管理器安装方式可参考：https://docs.nvidia.com/deeplearning/cudnn/latest/installation/linux.html#tarball-installation

4. 安装pytorch2.3.0+cu121 torchvision0.18.0等

使用pip命令安装pytorch和torchvision，注意，该方法安装的pytorch会自带cudatoolkit12.1和cudnn8.9.0,但仅限于pytorch自己使用，与系统中之前本机安装的系统级，所有环境都能使用的cudatoolkit和cudnn不是同一套，参考:

https://docs.infini-ai.com/posts/where-is-cuda.html

首先在base环境上新建一个环境，命名为pytorch

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conda create -n pytorch
conda activate pytorch

然后在pytorch环境中安装torch2.3.0,安装命令如下：

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pip install torch==2.3.0 torchvision==0.18.0 torchaudio==2.3.0 --index-url https://download.pytorch.org/whl/cu121

验证安装是否成功，新建并执行脚本：python test_torch.py，如下内容：

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import torch
print('PyTorch version: ' + str(torch.__version__))
print('CUDA available: ' + str(torch.cuda.is_available()))
print('cuDNN version: ' + str(torch.backends.cudnn.version()))
a = torch.tensor([0., 0.], dtype=torch.float32, device='cuda')
print('Tensor a =', a)
b = torch.randn(2, device='cuda')
print('Tensor b =', b)
c = a + b
print('Tensor c =', c)

import torchvision
print(torchvision.__version__)

输出如下：

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PyTorch version: 2.3.0+cu121
CUDA available: True
cuDNN version: 8902
Tensor a = tensor([0., 0.], device='cuda:0')
Tensor b = tensor([-0.4807, -0.8651], device='cuda:0')
Tensor c = tensor([-0.4807, -0.8651], device='cuda:0')
0.18.0+cu121

5. 安装onnxruntime-gpu

首先pypi上官方的onnxruntime-gpu安装包只有1.19.0以上版本才支持cuda12.x,但1.19.0以上版本又不支持cudnn8.x,所以需要自己编译安装支持cudnn8.x且能支持cuda12.x的onnxruntime-gpu，不过已经有人编译过了，可以直接拿来用。

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# 安装支持cuda12+cudnn8的onnxruntime-gpu1.18.0:
pip install -U onnxruntime-gpu==1.18.0 --extra-index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/onnxruntime-cuda-12/pypi/simple/

验证安装是否成功，新建并执行脚本：python test_onnxruntime.py，如下内容：

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import cv2
import numpy as np
import onnxruntime as ort
import torch

use_gpu = True

# 检查可用的执行提供者
available_providers = ort.get_available_providers()
print("Available providers:", available_providers)
# 设置执行提供者
providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] if use_gpu and 'CUDAExecutionProvider' in available_providers else ['CPUExecutionProvider']
print("Using providers:", providers)
# 加载模型
session = ort.InferenceSession('./yolov8s.onnx', providers=providers)
# 获取输入和输出的名称
input_name = session.get_inputs()[0].name
output_names = [output.name for output in session.get_outputs()]

print(f"input_name: {input_name}")
print(f"output_names: {output_names}")

providers = [("CUDAExecutionProvider", {"device_id": torch.cuda.current_device(),
                                        "user_compute_stream": str(torch.cuda.current_stream().cuda_stream)})]
sess_options = ort.SessionOptions()
sess = ort.InferenceSession("./yolov8s.onnx", sess_options=sess_options, providers=providers)

输出如下：

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Available providers: ['TensorrtExecutionProvider', 'CUDAExecutionProvider', 'CPUExecutionProvider']
Using providers: ['CUDAExecutionProvider', 'CPUExecutionProvider']
2024-12-15 22:59:47.961114142 [W:onnxruntime:, constant_folding.cc:269 ApplyImpl] Could not find a CPU kernel and hence can't constant fold ReduceMean node '/model.2/m.0/cv2/ReduceMean'
2024-12-15 22:59:47.962762629 [W:onnxruntime:, constant_folding.cc:269 ApplyImpl] Could not find a CPU kernel and hence can't constant fold ReduceMean node '/model.4/m.0/cv2/ReduceMean'
2024-12-15 22:59:47.963180939 [W:onnxruntime:, constant_folding.cc:269 ApplyImpl] Could not find a CPU kernel and hence can't constant fold ReduceMean node '/model.6/m.0/cv2/ReduceMean'
2024-12-15 22:59:47.966209107 [W:onnxruntime:, constant_folding.cc:269 ApplyImpl] Could not find a CPU kernel and hence can't constant fold ReduceMean node '/model.2/m.0/cv2/ReduceMean'
2024-12-15 22:59:47.966248419 [W:onnxruntime:, constant_folding.cc:269 ApplyImpl] Could not find a CPU kernel and hence can't constant fold ReduceMean node '/model.4/m.0/cv2/ReduceMean'
2024-12-15 22:59:47.966477201 [W:onnxruntime:, constant_folding.cc:269 ApplyImpl] Could not find a CPU kernel and hence can't constant fold ReduceMean node '/model.6/m.0/cv2/ReduceMean'
input_name: images
output_names: ['output0']
2024-12-15 22:59:48.085628154 [W:onnxruntime:, constant_folding.cc:269 ApplyImpl] Could not find a CPU kernel and hence can't constant fold ReduceMean node '/model.2/m.0/cv2/ReduceMean'
2024-12-15 22:59:48.085909892 [W:onnxruntime:, constant_folding.cc:269 ApplyImpl] Could not find a CPU kernel and hence can't constant fold ReduceMean node '/model.4/m.0/cv2/ReduceMean'
2024-12-15 22:59:48.086174739 [W:onnxruntime:, constant_folding.cc:269 ApplyImpl] Could not find a CPU kernel and hence can't constant fold ReduceMean node '/model.6/m.0/cv2/ReduceMean'
2024-12-15 22:59:48.091461531 [W:onnxruntime:, constant_folding.cc:269 ApplyImpl] Could not find a CPU kernel and hence can't constant fold ReduceMean node '/model.2/m.0/cv2/ReduceMean'
2024-12-15 22:59:48.091661505 [W:onnxruntime:, constant_folding.cc:269 ApplyImpl] Could not find a CPU kernel and hence can't constant fold ReduceMean node '/model.4/m.0/cv2/ReduceMean'
2024-12-15 22:59:48.091925856 [W:onnxruntime:, constant_folding.cc:269 ApplyImpl] Could not find a CPU kernel and hence can't constant fold ReduceMean node '/model.6/m.0/cv2/ReduceMean'

6. 安装TensorRT8.6.1

下载对应cuda版本的TensorRT版本和Liunx_x86_64版本的【Tar】文件

官网的tensorrt9.*版本的安装包不知咋的全都没了，我这里下载了支持cuda12.1的tensorrt8.6.1.8

下载TensorRT 8.6 GA for Linux x86_64 and CUDA 12.0 and 12.1 TAR Package然后上传到容器中

参考官方指导进行安装：https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html#installing-tar

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# 解压安装包
tar -xvf TensorRT-8.6.1.8.Linux.x86_64-gnu.cuda-12.1.cudnn8.9.tar.gz
# 将 TensorRT lib 目录的绝对路径添加到环境变量中 LD_LIBRARY_PATH ：
export LD_LIBRARY_PATH=/path/to/TensorRT-8.6.1.6/lib:$LD_LIBRARY_PATH
# 安装 Python TensorRT wheel 文件，3.x换成你的python版本
cd TensorRT-8.6.1.8/python
python3 -m pip install tensorrt-*-cp310-none-linux_x86_64.whl
# （可选）安装 TensorRT 精益和调度运行时轮 文件：
python3 -m pip install tensorrt_lean-*-cp310-none-linux_x86_64.whl
python3 -m pip install tensorrt_dispatch-*-cp310-none-linux_x86_64.whl

验证安装是否成功，新建并执行脚本：python test_tensorrt.py，如下内容：

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import onnx
from onnx import shape_inference
import tensorrt as trt
import os

print(f"TensorRT version: {trt.__version__}")

# 加载ONNX模型
onnx_model_path = "./yolov8s.onnx"
onnx_model = onnx.load(onnx_model_path)

# 进行形状推断
inferred_model = shape_inference.infer_shapes(onnx_model)
onnx.save(inferred_model, "inferred_" + os.path.basename(onnx_model_path))

# 创建TensorRT builder和网络定义
TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
builder = trt.Builder(TRT_LOGGER)
network = builder.create_network(1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))

# 解析ONNX模型
parser = trt.OnnxParser(network, TRT_LOGGER)
with open("inferred_" + os.path.basename(onnx_model_path), 'rb') as model:
    if not parser.parse(model.read()):
        print('Failed to parse the ONNX file.')
        for error in range(parser.num_errors):
            print(parser.get_error(error))
        exit()

# 构建TensorRT引擎
config = builder.create_builder_config()
config.max_workspace_size = 1 << 30  # 1GB
serialized_engine = builder.build_serialized_network(network, config)

# 保存TensorRT引擎文件
engine_file_path = "yolov8s.trt"
with open(engine_file_path, "wb") as f:
    f.write(serialized_engine)

# 加载TensorRT引擎并进行推理（省略具体实现）

如果能正常运行，将onnx模型转为tensorrt模型，不报错，说明安装成功。

7. 编译安装带有cuda支持的opencv4.9.0

首先安装编译工具,也是opencv正常运行的前提。

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sudo apt install cmake
sudo apt install python3-numpy
sudo apt install libavcodec-dev libavformat-dev libswscale-dev
sudo apt install libgstreamer-plugins-base1.0-dev libgstreamer1.0-dev
sudo apt install libgtk-3-dev
sudo apt install libpng-dev libjpeg-dev libopenexr-dev libtiff-dev libwebp-dev

然后下载4.9.0版本的opencv源码包

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sudo apt install git
cd ~/Downloads
git clone --branch=4.9.0 --single-branch https://github.com/opencv/opencv.git
git clone --branch=4.9.0 --single-branch https://github.com/opencv/opencv_contrib.git

编译安装到当前conda环境中：

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cd opencv
mkdir build
cd build
cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local -D WITH_CUDA=ON -D WITH_CUDNN=ON -D WITH_CUBLAS=ON -D WITH_TBB=ON -D OPENCV_DNN_CUDA=ON -D OPENCV_ENABLE_NONFREE=ON -D CUDA_ARCH_BIN=8.9 -D OPENCV_EXTRA_MODULES_PATH=$HOME/Downloads/opencv_contrib/modules -D BUILD_EXAMPLES=OFF -D HAVE_opencv_python3=ON -D ENABLE_FAST_MATH=1 -D cuda_toolkit_root_dir=/usr/local/cuda-12.1 -D CUDNN_INCLUDE_DIR=/usr/include/ -D CUDNN_LIBRARY=/usr/lib/x86_64-linux-gnu/libcudnn.so.8 -D PYTHON_DEFAULT_EXECUTABLE=$(python3 -c "import sys; print(sys.executable)") -D PYTHON3_EXECUTABLE=$(python3 -c "import sys; print(sys.executable)") -D PYTHON3_NUMPY_INCLUDE_DIRS=$(python3 -c "import numpy; print (numpy.get_include())") -D PYTHON3_PACKAGES_PATH=$(python3 -c "from distutils.sysconfig import get_python_lib; print(get_python_lib())") ..

注意，请将CUDA_ARCH_BIN参数的值改为你的cuda算力，例如：CUDA_ARCH_BIN=8.9，请将CUDNN_INCLUDE_DIR和CUDNN_LIBRARY参数的值改为你的cudnn安装路径，例如：CUDNN_INCLUDE_DIR=/usr/include/，CUDNN_LIBRARY=/usr/lib/x86_64-linux-gnu/libcudnn.so.8，请将OPENCV_EXTRA_MODULES_PATH参数的值改为你的opencv_contrib源码包路径，例如：OPENCV_EXTRA_MODULES_PATH=$HOME/Downloads/opencv_contrib/modules，最后执行：

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make -j$(nproc)

等待编译完成，编译完成后，执行：

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sudo make install
sudo ldconfig

验证安装，新建并执行脚本：python test_cv.py，如下内容：

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import cv2
print(cv2.__version__)
print(cv2.cuda.getCudaEnabledDeviceCount())
print(cv2.getBuildInformation())

如果没有报错，则说明安装成功。

注意，由于我pytorch环境中使用的也是base环境中的python3.10.14,所以pytorch环境中可直接使用该cuda版opencv4.9.0，这个支持cuda的opencv4.9.0针对python的包位于/usr/local/lib/python3.10/dist-packages/cv2，如更换其他版本，请自行创建链接到自己版本python的库中：sudo ln -s /usr/local/lib/python3.10/dist-packages/cv2 /root/miniconda3/lib/python3.xx/site-packages/cv2
安装完成后，之前apt安装的包也不要卸载，否则opencv会导入报错，无法运行。