PyTorch building in-depth dismantling: from environment preparation to cross-end compilation

If you have tried building ExecuTorch from source, you should be familiar with the feeling of "getting stuck in the environment configuration and spending half an hour". Every step may lead to pitfalls: The conda environment cannot be activated, the cmake version is wrong, and the error message cannot tell the reason. The submodule is not fully synchronized, and the CMakeLists.txt that the third party depends on cannot be found. One cross-compilation parameter is missing, and all symbols are lost during linking. No matter how detailed the official documentation is, there will always be some path problems, permission errors, and version conflicts that will stop you during actual operations. ExecuTorch itself is a good thing. As PyTorch’s official edge AI inference runtime, it lets models