我正在尝试使用 C++ 编译器设置 VScode 按照本教程 在此处输入链接描述
我遇到的问题是(通过使用代码运行器运行代码)
“g++”不被识别为内部或外部命令、可操作程序或批处理文件。 在此输入图像描述
如果我使用调试选项运行我会遇到这个问题
preLaunchTask“构建和运行文件”以退出代码 1 终止。在此处输入图像描述
这是我的“c_cpp_properties.json”
{
"configurations": [
{
"name": "Win32",
"intelliSenseMode": "clang-x64",
"defines": [
"_DEBUG",
"UNICODE",
"__GNUC__=7",
"__stdcall=attribute((stdcall))",
"__cdecl=__attribute__((__cdecl__))",
"__cplusplus=201703L"
],
"includePath": [
"${workspaceFolder}/include",
"C:/MinGW64/bin/../lib/gcc/x86_64-w64-mingw32/4.9.2/include/c++",
"C:/MinGW64/bin/../lib/gcc/x86_64-w64-mingw32/4.9.2/include/c++/x86_64-w64-mingw32",
"C:/MinGW64/bin/../lib/gcc/x86_64-w64-mingw32/4.9.2/include/c++/backward",
"C:/MinGW64/bin/../lib/gcc/x86_64-w64-mingw32/4.9.2/include",
"C:/MinGW64/bin/../lib/gcc/x86_64-w64-mingw32/4.9.2/../../../../include",
"C:/MinGW64/bin/../lib/gcc/x86_64-w64-mingw32/4.9.2/include-fixed",
"C:/MinGW64/bin/../lib/gcc/x86_64-w64-mingw32/4.9.2/../../../../x86_64-w64-mingw32/include"
],
"browse": {
"path": [
"${workspaceFolder}/include",
"C:/MinGW64/bin/../lib/gcc/x86_64-w64-mingw32/4.9.2/include/c++",
"C:/MinGW64/bin/../lib/gcc/x86_64-w64-mingw32/4.9.2/include/c++/x86_64-w64-mingw32",
"C:/MinGW64/bin/../lib/gcc/x86_64-w64-mingw32/4.9.2/include/c++/backward",
"C:/MinGW64/bin/../lib/gcc/x86_64-w64-mingw32/4.9.2/include",
"C:/MinGW64/bin/../lib/gcc/x86_64-w64-mingw32/4.9.2/../../../../include",
"C:/MinGW64/bin/../lib/gcc/x86_64-w64-mingw32/4.9.2/include-fixed",
"C:/MinGW64/bin/../lib/gcc/x86_64-w64-mingw32/4.9.2/../../../../x86_64-w64-mingw32/include"
],
"limitSymbolsToIncludedHeaders": false,
"databaseFilename": ""
},
"cStandard": "c11",
"cppStandard": "c++17"
}
],
"version": 4
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}
这是我的“launch.json”
{
"version": "0.2.0",
"configurations": [
{
"name": "Run C/C++", …Run Code Online (Sandbox Code Playgroud) 我正在尝试使用 jupyter 笔记本运行 pytorch 代码,但出现此错误
RuntimeError: CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 2.00 GiB total capacity; 1.23 GiB already allocated; 18.83 MiB free; 1.25 GiB reserved in total by PyTorch)
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我已经找到答案了。最重要的是,只是减少批量大小。我尝试将批量大小从 20 减少到 10 到 2 和 1。现在仍然无法运行代码
我的 GPU 是 Quadro p620