Neo Sun


What's past is prologue

计算机环境调试经验积累

  • ~16.40K words
  1. 1. C++
    1. 1.1. 相关工具使用
      1. 1.1.1. CMake——自动构建项目的工具
      2. 1.1.2. 配置CGAL库
        1. 1.1.2.1. 一、安装 Boost
          1. 1.1.2.1.1. 1. 下载 Boost
          2. 1.1.2.1.2. 2. 安装 Boost
          3. 1.1.2.1.3. 3. 配置环境变量
        2. 1.1.2.2. 二、下载并安装 CGAL
          1. 1.1.2.2.1. 1. 下载 CGAL
          2. 1.1.2.2.2. 2. 安装 CGAL
          3. 1.1.2.2.3. 3. 配置环境变量
        3. 1.1.2.3. 三、配置VS2019属性页
          1. 1.1.2.3.1. 1. 连接器 - 输入 - 附加依赖项
          2. 1.1.2.3.2. 2. C/C++ - 常规 - 附加包含目录
          3. 1.1.2.3.3. 3. 测试代码
      3. 1.1.3. 配置PCL库
        1. 1.1.3.0.1. 配置属性-调试-环境-添加:
        2. 1.1.3.0.2. C/C++-常规-SDL检查:否
        3. 1.1.3.0.3. C/C++-语言-符合模式:否
        4. 1.1.3.0.4. VC++目录一包含目录，添加7个include路径
        5. 1.1.3.0.5. VC++目录一库目录，添加6个ib路径
        6. 1.1.3.0.6. C/C++-预处理器-预处理器定义-添加:
        7. 1.1.3.0.7. 链接器一输入一附加依赖项—添加PCL和VTK的相关lib文件
        8. 1.1.3.0.8. 测试代码
    2. 1.1.4. OpenCV配置
      1. 1.1.4.1. 配置系统变量
    3. 1.1.5. vcglib配置
    4. 1.1.6. C++常识
  • 2. 一些常见问题
    1. 2.0.1. 解决压缩包问题后的DLL缺失
  • 3. Python
    1. 3.1. GrowSP项目
    2. 3.2. Conda有关问题

    • C++

    相关工具使用

    CMake——自动构建项目的工具

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    cmake_minimum_required(VERSION 3.10)
    #设置此项目所需的最低CMake版本为3.10
    project(Rasterizer)
    #定义项目名称为 "Rasterizer"
    find_package(OpenCV REQUIRED)
    #查找并加载OpenCV库
    set(CMAKE_CXX_STANDARD 17)
    #将C++标准设置为C++17
    include_directories(/usr/local/include)
    #添加包含目录
    add_executable(Rasterizer main.cpp rasterizer.hpp rasterizer.cpp Triangle.hpp Triangle.cpp)
    #定义一个名为 "Rasterizer" 的可执行文件，并指定其源文件和头文件。CMake将会编译 main.cpp、rasterizer.cpp 和 Triangle.cpp，并将它们链接在一起生成可执行文件。
    target_link_libraries(Rasterizer ${OpenCV_LIBRARIES})
    #将OpenCV库链接到 "Rasterizer" 可执行文件。${OpenCV_LIBRARIES} 是之前 find_package(OpenCV REQUIRED) 中找到的OpenCV库文件列表。

    配置CGAL库

    一、安装 Boost

    1. 下载 Boost

    从 Boost C++ Libraries - SourceForge 下载合适版本。根据 Visual Studio 版本选择相应的 Boost 版本：

    • VC2015 对应 14.0
    • VC2017 对应 14.1
    • VC2019 对应 14.2

    例如，下载 boost_1_76_0-msvc-14.2-64.exe。

    2. 安装 Boost

    运行下载的 boost_1_76_0-msvc-14.2-64.exe 文件，将 Boost 安装到指定目录，例如：D:\dev\boost_1_76_0。

    3. 配置环境变量
    1. 设置 Boost 的库目录和包含目录：
      • 打开系统环境变量设置：我的电脑 -> 属性 -> 高级系统设置 -> 环境变量
      • 新建或编辑以下环境变量：
        • BOOST_LIBRARYDIR = D:\dev\boost_1_76_0\lib64-msvc-14.2
        • BOOST_INCLUDEDIR = D:\dev\boost_1_76_0
    2. 将 Boost 的库路径添加到系统环境变量 PATH 中：
      • 在系统环境变量 PATH 中，添加 D:\dev\boost_1_76_0\lib64-msvc-14.2

    二、下载并安装 CGAL

    1. 下载 CGAL

    从 CGAL GitHub Releases 页面下载 CGAL 安装程序，例如 CGAL-5.0.2-Setup.exe。

    2. 安装 CGAL

    运行下载的 CGAL-5.0.2-Setup.exe 文件，按照安装向导完成安装。

    3. 配置环境变量

    安装完成后，将 CGAL 的 GMP 库路径添加到系统环境变量 PATH 中：

    1. 打开系统环境变量设置：我的电脑 -> 属性 -> 高级系统设置 -> 环境变量
    2. 在系统环境变量 PATH 中，添加 D:\dev\CGAL-5.0.2\auxiliary\gmp\lib

    三、配置VS2019属性页

    1. 连接器 - 输入 - 附加依赖项

    在 Visual Studio 中，配置项目属性页，添加以下附加依赖项：

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    D:\dev\CGAL-5.0.2\auxiliary\gmp\lib\libmpfr-4.lib
    D:\dev\CGAL-5.0.2\auxiliary\gmp\lib\libgmp-10.lib
    2. C/C++ - 常规 - 附加包含目录

    在 Visual Studio 中，配置项目属性页，添加以下附加包含目录：

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    D:\dev\CGAL-5.0.2\auxiliary\gmp\include
    D:\boost_1_76_0
    D:\dev\CGAL-5.0.2\include
    3. 测试代码

    编写并运行以下测试代码，以确保配置正确并验证依赖项和包含目录的有效性。

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    #include <iostream>
    #include <CGAL/Simple_cartesian.h>
    typedef CGAL::Simple_cartesian<double> Kernel;
    typedef Kernel::Point_2 Point_2;
    typedef Kernel::Segment_2 Segment_2;
    int main()
    {
        Point_2 p(1, 1), q(10, 10);
        std::cout << "p = " << p << std::endl;
        std::cout << "q = " << q.x() << " " << q.y() << std::endl;
        std::cout << "sqdist(p,q) = "
            << CGAL::squared_distance(p, q) << std::endl;
        Segment_2 s(p, q);
        Point_2 m(5, 9);
        std::cout << "m = " << m << std::endl;
        std::cout << "sqdist(Segment_2(p,q), m) = "
            << CGAL::squared_distance(s, m) << std::endl;
        std::cout << "p, q, and m ";
        switch (CGAL::orientation(p, q, m)) {
        case CGAL::COLLINEAR:
            std::cout << "are collinear\n";
            break;
        case CGAL::LEFT_TURN:
            std::cout << "make a left turn\n";
            break;
        case CGAL::RIGHT_TURN:
            std::cout << "make a right turn\n";
            break;
        }
        std::cout << " midpoint(p,q) = " << CGAL::midpoint(p, q) << std::endl;
        return 0;
    }

    配置PCL库

    配置属性-调试-环境-添加:
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    PATH=D:\PCL 1.11.1\\bin;D:\PCL 1.11.1\\3rdParty\FLANN\bin;D:\PCL 1.11.1\\3rdParty\VTK\bin;D:\PCL 1.11.1\\3rdParty\OpenNI2\Tools
    C/C++-常规-SDL检查:否
    C/C++-语言-符合模式:否
    VC++目录一包含目录，添加7个include路径
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    D:\PCL 1.11.1\include\pcl-1.11
    D:\PCL 1.11.1\3rdParty\Boost\include\boost-1_74
    D:\PCL 1.11.1\3rdParty\Eigen\eigen3
    D:\PCL 1.11.1\3rdParty\FLANN\include
    D:\PCL 1.11.1\3rdParty\OpenNI2\Include
    D:\PCL 1.11.1\3rdParty\Qhull\include
    D:\PCL 1.11.1\3rdParty\VTK\include\vtk-8.2
    VC++目录一库目录，添加6个ib路径
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    D:\PCL 1.11.1\lib
    D:\PCL 1.11.1\3rdParty\Boost\lib
    D:\PCL 1.11.1\3rdParty\FLANN\lib
    D:\PCL 1.11.1\3rdParty\OpenNI2\Lib
    D:\PCL 1.11.1\3rdParty\Qhull\lib
    D:\PCL 1.11.1\3rdParty\VTK\lib
    C/C++-预处理器-预处理器定义-添加:
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    BOOST_USE_WINDOWS_H
    NOMINMAX
    _CRT_SECURE_NO_DEPRECATE
    链接器一输入一附加依赖项—添加PCL和VTK的相关lib文件
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    pcl_commond.lib
    pcl_featuresd.lib
    pcl_filtersd.lib
    pcl_iod.lib
    pcl_io_plyd.lib
    pcl_kdtreed.lib
    pcl_keypointsd.lib
    pcl_mld.lib
    pcl_octreed.lib
    pcl_outofcored.lib
    pcl_peopled.lib
    pcl_recognitiond.lib
    pcl_registrationd.lib
    pcl_sample_consensusd.lib
    pcl_searchd.lib
    pcl_segmentationd.lib
    pcl_stereod.lib
    pcl_surfaced.lib
    pcl_trackingd.lib
    pcl_visualizationd.lib
    vtkChartsCore-8.2-gd.lib
    vtkCommonColor-8.2-gd.lib
    vtkCommonComputationalGeometry-8.2-gd.lib
    vtkCommonCore-8.2-gd.lib
    vtkCommonDataModel-8.2-gd.lib
    vtkCommonExecutionModel-8.2-gd.lib
    vtkCommonMath-8.2-gd.lib
    vtkCommonMisc-8.2-gd.lib
    vtkCommonSystem-8.2-gd.lib
    vtkCommonTransforms-8.2-gd.lib
    vtkDICOMParser-8.2-gd.lib
    vtkDomainsChemistry-8.2-gd.lib
    vtkDomainsChemistryOpenGL2-8.2-gd.lib
    vtkdoubleconversion-8.2-gd.lib
    vtkexodusII-8.2-gd.lib
    vtkexpat-8.2-gd.lib
    vtkFiltersAMR-8.2-gd.lib
    vtkFiltersCore-8.2-gd.lib
    vtkFiltersExtraction-8.2-gd.lib
    vtkFiltersFlowPaths-8.2-gd.lib
    vtkFiltersGeneral-8.2-gd.lib
    vtkFiltersGeneric-8.2-gd.lib
    vtkFiltersGeometry-8.2-gd.lib
    vtkFiltersHybrid-8.2-gd.lib
    vtkFiltersHyperTree-8.2-gd.lib
    vtkFiltersImaging-8.2-gd.lib
    vtkFiltersModeling-8.2-gd.lib
    vtkFiltersParallel-8.2-gd.lib
    vtkFiltersParallelImaging-8.2-gd.lib
    vtkFiltersPoints-8.2-gd.lib
    vtkFiltersProgrammable-8.2-gd.lib
    vtkFiltersSelection-8.2-gd.lib
    vtkFiltersSMP-8.2-gd.lib
    vtkFiltersSources-8.2-gd.lib
    vtkFiltersStatistics-8.2-gd.lib
    vtkFiltersTexture-8.2-gd.lib
    vtkFiltersTopology-8.2-gd.lib
    vtkFiltersVerdict-8.2-gd.lib
    vtkfreetype-8.2-gd.lib
    vtkGeovisCore-8.2-gd.lib
    vtkgl2ps-8.2-gd.lib
    vtkglew-8.2-gd.lib
    vtkGUISupportMFC-8.2-gd.lib
    vtkhdf5-8.2-gd.lib
    vtkhdf5_hl-8.2-gd.lib
    vtkImagingColor-8.2-gd.lib
    vtkImagingCore-8.2-gd.lib
    vtkImagingFourier-8.2-gd.lib
    vtkImagingGeneral-8.2-gd.lib
    vtkImagingHybrid-8.2-gd.lib
    vtkImagingMath-8.2-gd.lib
    vtkImagingMorphological-8.2-gd.lib
    vtkImagingSources-8.2-gd.lib
    vtkImagingStatistics-8.2-gd.lib
    vtkImagingStencil-8.2-gd.lib
    vtkInfovisCore-8.2-gd.lib
    vtkInfovisLayout-8.2-gd.lib
    vtkInteractionImage-8.2-gd.lib
    vtkInteractionStyle-8.2-gd.lib
    vtkInteractionWidgets-8.2-gd.lib
    vtkIOAMR-8.2-gd.lib
    vtkIOAsynchronous-8.2-gd.lib
    vtkIOCityGML-8.2-gd.lib
    vtkIOCore-8.2-gd.lib
    vtkIOEnSight-8.2-gd.lib
    vtkIOExodus-8.2-gd.lib
    vtkIOExport-8.2-gd.lib
    vtkIOExportOpenGL2-8.2-gd.lib
    vtkIOExportPDF-8.2-gd.lib
    vtkIOGeometry-8.2-gd.lib
    vtkIOImage-8.2-gd.lib
    vtkIOImport-8.2-gd.lib
    vtkIOInfovis-8.2-gd.lib
    vtkIOLegacy-8.2-gd.lib
    vtkIOLSDyna-8.2-gd.lib
    vtkIOMINC-8.2-gd.lib
    vtkIOMovie-8.2-gd.lib
    vtkIONetCDF-8.2-gd.lib
    vtkIOParallel-8.2-gd.lib
    vtkIOParallelXML-8.2-gd.lib
    vtkIOPLY-8.2-gd.lib
    vtkIOSegY-8.2-gd.lib
    vtkIOSQL-8.2-gd.lib
    vtkIOTecplotTable-8.2-gd.lib
    vtkIOVeraOut-8.2-gd.lib
    vtkIOVideo-8.2-gd.lib
    vtkIOXML-8.2-gd.lib
    vtkIOXMLParser-8.2-gd.lib
    vtkjpeg-8.2-gd.lib
    vtkjsoncpp-8.2-gd.lib
    vtklibharu-8.2-gd.lib
    vtklibxml2-8.2-gd.lib
    vtklz4-8.2-gd.lib
    vtklzma-8.2-gd.lib
    vtkmetaio-8.2-gd.lib
    vtkNetCDF-8.2-gd.lib
    vtkogg-8.2-gd.lib
    vtkParallelCore-8.2-gd.lib
    vtkpng-8.2-gd.lib
    vtkproj-8.2-gd.lib
    vtkpugixml-8.2-gd.lib
    vtkRenderingAnnotation-8.2-gd.lib
    vtkRenderingContext2D-8.2-gd.lib
    vtkRenderingContextOpenGL2-8.2-gd.lib
    vtkRenderingCore-8.2-gd.lib
    vtkRenderingExternal-8.2-gd.lib
    vtkRenderingFreeType-8.2-gd.lib
    vtkRenderingGL2PSOpenGL2-8.2-gd.lib
    vtkRenderingImage-8.2-gd.lib
    vtkRenderingLabel-8.2-gd.lib
    vtkRenderingLOD-8.2-gd.lib
    vtkRenderingOpenGL2-8.2-gd.lib
    vtkRenderingVolume-8.2-gd.lib
    vtkRenderingVolumeOpenGL2-8.2-gd.lib
    vtksqlite-8.2-gd.lib
    vtksys-8.2-gd.lib
    vtktheora-8.2-gd.lib
    vtktiff-8.2-gd.lib
    vtkverdict-8.2-gd.lib
    vtkViewsContext2D-8.2-gd.lib
    vtkViewsCore-8.2-gd.lib
    vtkViewsInfovis-8.2-gd.lib
    vtkzlib-8.2-gd.lib
    测试代码
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    #include <iostream>
    #include <vector>
    #include <ctime>
    #include <pcl/point_cloud.h>
    #include <pcl/octree/octree.h>
    #include <boost/thread/thread.hpp>
    #include <pcl/visualization/pcl_visualizer.h>
    using namespace std;
    int
    main(int argc, char** argv)
    {
    	srand((unsigned int)time(NULL));
    	pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
    	// 创建点云数据
    	cloud->width = 1000;
    	cloud->height = 1;
    	cloud->points.resize(cloud->width * cloud->height);
    	for (size_t i = 0; i < cloud->points.size(); ++i)
    	{
    		cloud->points[i].x = 1024.0f * rand() / (RAND_MAX + 1.0f);
    		cloud->points[i].y = 1024.0f * rand() / (RAND_MAX + 1.0f);
    		cloud->points[i].z = 1024.0f * rand() / (RAND_MAX + 1.0f);
    	}

    	pcl::octree::OctreePointCloudSearch<pcl::PointXYZ> octree(0.1);
    	octree.setInputCloud(cloud);
    	octree.addPointsFromInputCloud();
    	pcl::PointXYZ searchPoint;
    	searchPoint.x = 1024.0f * rand() / (RAND_MAX + 1.0f);
    	searchPoint.y = 1024.0f * rand() / (RAND_MAX + 1.0f);
    	searchPoint.z = 1024.0f * rand() / (RAND_MAX + 1.0f);

    	//半径内近邻搜索
    	vector<int>pointIdxRadiusSearch;
    	vector<float>pointRadiusSquaredDistance;
    	float radius = 256.0f * rand() / (RAND_MAX + 1.0f);
    	cout << "Neighbors within radius search at (" << searchPoint.x
    		<< " " << searchPoint.y
    		<< " " << searchPoint.z
    		<< ") with radius=" << radius << endl;
    	if (octree.radiusSearch(searchPoint, radius, pointIdxRadiusSearch, pointRadiusSquaredDistance) > 0)
    	{
    		for (size_t i = 0; i < pointIdxRadiusSearch.size(); ++i)
    			cout << "    " << cloud->points[pointIdxRadiusSearch[i]].x
    			<< " " << cloud->points[pointIdxRadiusSearch[i]].y
    			<< " " << cloud->points[pointIdxRadiusSearch[i]].z
    			<< " (squared distance: " << pointRadiusSquaredDistance[i] << ")" << endl;
    	}
    	// 初始化点云可视化对象
    	boost::shared_ptr<pcl::visualization::PCLVisualizer>viewer(new pcl::visualization::PCLVisualizer("显示点云"));
    	viewer->setBackgroundColor(0, 0, 0);  //设置背景颜色为黑色
    	// 对点云着色可视化 (red).
    	pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ>target_color(cloud, 255, 0, 0);
    	viewer->addPointCloud<pcl::PointXYZ>(cloud, target_color, "target cloud");
    	viewer->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 1, "target cloud");

    	// 等待直到可视化窗口关闭
    	while (!viewer->wasStopped())
    	{
    		viewer->spinOnce(100);
    		boost::this_thread::sleep(boost::posix_time::microseconds(1000));
    	}

    	return (0);
    }

    OpenCV配置

    https://blog.csdn.net/Creama_/article/details/107238475

    https://blog.csdn.net/Smalldemons/article/details/129723228

    配置系统变量

    找到OpenCV的安装(解压)路径，将路径opencv\build\x64\vc15\bin添加到Path中。

    VC++目录，点击包含目录，然后将OpenCV的三个包含目录添加进去（图中还没加）：
    D:\opencv\build\include
    D:\opencv\build\include\opencv
    D:\opencv\build\include\opencv2
    添加完后，继续点击库目录，将OpenCV的库目录添加进去：
    D:\opencv\build\x64\vc12\lib

    添加完后，点击连接器，点击输入，点击附加依赖项，添加lib文件，lib文件可以在D:\Program Files\opencv\build\x64\vc15\lib中查看，数字后面带d的表示debug，选这个就行了（添加依赖项的时候可不用路径，只输入文件名，如：opencv_world411d.lib）。

    vcglib配置

    在打开的项目属性页，配置属性一栏，找到VC++目录–>包含目录，将其设置为

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    path/to/your/include/vcg

    点击C/C++一栏，找到预处理–>预处理器定义，输入

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    _CRT_SECURE_NO_WARNINGS
    参数说明：
    这个宏用于禁用微软C运行库（CRT）中关于安全函数的警告。
    在Visual Studio中，使用不安全的函数（如strcpy、sprintf等）会触发编译器警告，建议改用更安全的替代函数（如strcpy_s、sprintf_s等）。
    定义CRT_SECURE_NO_WARNINGS可以抑制这些警告，从而编译通过，但要注意，这样做可能会增加代码存在安全漏洞的风险。

    C++常识

    一些常见问题

    解决压缩包问题后的DLL缺失

    压缩包问题解决后，运行程序时又提示缺少“luad.dll”。这种问题在网上很难找到明确的解决方法。尝试了各种途径，最终通过安装lua for Windows修复了这个问题。安装完成后，系统能够正确识别并加载“luad.dll”，程序也终于得以顺利运行。

    Python

    GrowSP项目

    1.官网下载conda包（4090无法连接网络404）

    https://anaconda.org/conda-forge/qhull/files?version=2015.2

    2.AttributeError: ‘numpy.ndarray’ object has no attribute ‘numpy‘：

    删掉.numpy()

    3.78行：

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    coords = coords.astype(np.float32)

    4.MinkowskiEngine安装总结

    MinkowskiEngine安装避坑：https://blog.csdn.net/qq_52297947/article/details/126706762

    建议直接github下

    5.ValueError: Unknown CUDA arch (8.9) or GPU not supported 报错处理

    在RTX4090上运行深度学习代码报错：ValueError: Unknown CUDA arch (8.9) or GPU not suppor。

    原因是cuda（我这里是cuda11.0，最高支持8.6）的版本不支持当前算力（8.9）

    解决办法（我这是 Ubuntu 系统），改算力：

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    sudo vim ~/.bashrc

    # 在配置文件中添加如下一行

    export TORCH_CUDA_ARCH_LIST=8.6  # 因为是CUDA11.0，对应的算力为8.6
    source ~/.bashrc

    nvidia给的显卡算力查询：CUDA GPU | NVIDIA 开发者https://developer.nvidia.cn/cuda-gpus#compute

    6./usr/local/cuda/bin/nvcc: No such file or directory 错误

    先确定 cuda 是否安装成功

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    nvcc -V

    安装成功的话直接在命令行里输入

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    export CUDA_HOME=/usr/local/cuda

    7.pclpy 安装和使用

    建议直接找github https://github.com/davidcaron/pclpy 或 https://blog.csdn.net/m0_73126623/article/details/136180532

    conda install qhull==20xx.x -c conda-forge（如果报错“ImportError: libqhull_p.so.7: cannot open shared object file”的话）

    8.【anaconda】conda创建、查看、删除虚拟环境（anaconda命令集）

    https://blog.csdn.net/miracleoa/article/details/106115730

    Conda有关问题

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    add_anaconda_token: True
    add_pip_as_python_dependency: True
    aggressive_update_packages:
      - ca-certificates
      - certifi
      - openssl
    allow_conda_downgrades: False
    allow_cycles: True
    allow_non_channel_urls: False
    allow_softlinks: False
    allowlist_channels: []
    always_copy: False
    always_softlink: False
    always_yes: None
    anaconda_anon_usage: True
    anaconda_upload: None
    auto_activate_base: True
    auto_stack: 0
    auto_update_conda: True
    bld_path: 
    changeps1: True
    channel_alias: https://conda.anaconda.org
    channel_priority: flexible
    channel_settings: []
    channels:
      - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/pro/
      - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/
      - https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge/
      - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/
      - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
      - defaults
    client_ssl_cert: None
    client_ssl_cert_key: None
    clobber: False
    conda_build: {}
    create_default_packages: []
    croot: /opt/conda/conda-bld
    custom_channels:
      pkgs/main: https://repo.anaconda.com
      pkgs/r: https://repo.anaconda.com
      pkgs/pro: https://repo.anaconda.com
    custom_multichannels:
      defaults: 
        - https://repo.anaconda.com/pkgs/main
        - https://repo.anaconda.com/pkgs/r
      local: 
    debug: False
    default_channels:
      - https://repo.anaconda.com/pkgs/main
      - https://repo.anaconda.com/pkgs/r
    default_python: 3.8
    default_threads: None
    deps_modifier: not_set
    dev: False
    disallowed_packages: []
    download_only: False
    dry_run: False
    enable_private_envs: False
    env_prompt: ({default_env}) 
    envs_dirs:
      - /opt/conda/envs
      - /root/.conda/envs
    error_upload_url: https://conda.io/conda-post/unexpected-error
    execute_threads: 1
    experimental: []
    extra_safety_checks: False
    fetch_threads: 5
    force: False
    force_32bit: False
    force_reinstall: False
    force_remove: False
    ignore_pinned: False
    json: False
    local_repodata_ttl: 1
    migrated_channel_aliases: []
    migrated_custom_channels: {}
    no_lock: False
    no_plugins: False
    non_admin_enabled: True
    notify_outdated_conda: True
    number_channel_notices: 5
    offline: False
    override_channels_enabled: True
    path_conflict: clobber
    pinned_packages: []
    pip_interop_enabled: False
    pkgs_dirs:
      - /opt/conda/pkgs
      - /root/.conda/pkgs
    proxy_servers: {}
    quiet: False
    register_envs: True
    remote_backoff_factor: 1
    remote_connect_timeout_secs: 9.15
    remote_max_retries: 3
    remote_read_timeout_secs: 60.0
    repodata_fns:
      - current_repodata.json
      - repodata.json
    repodata_threads: None
    repodata_use_zst: True
    report_errors: None
    restore_free_channel: False
    rollback_enabled: True
    root_prefix: /opt/conda
    safety_checks: warn
    sat_solver: pycosat
    separate_format_cache: False
    shortcuts: True
    shortcuts_only: []
    show_channel_urls: True
    signing_metadata_url_base: None
    solver: libmamba
    solver_ignore_timestamps: False
    ssl_verify: True
    subdir: linux-64
    subdirs:
      - linux-64
      - noarch
    target_prefix_override: 
    trace: False
    track_features: []
    unsatisfiable_hints: True
    unsatisfiable_hints_check_depth: 2
    update_modifier: update_specs
    use_index_cache: False
    use_local: False
    use_only_tar_bz2: False
    verbosity: 0
    verify_threads: 1

    conda环境拷贝：https://blog.csdn.net/qazplm12_3/article/details/108924561

    修改anaconda 创建新虚拟环境的路径(Linux)

    查看虚拟环境位置

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    conda info --envs
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    vim .condarc
    envs_dirs:
      - /root/hy-data/envs
    pkgs_dirs:
      - /root/hy-data/pkgs
    This work is licensed under Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)