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By default, freeing memory in CUDA is expensive because it does a GPU sync. Because of this, PyTorch avoids freeing and mallocing memory through CUDA, and tries to manage it itself. When blocks are freed, the allocator just keeps them in their own cache. The allocator can then use the free blocks in the cache when something else is allocated. But if these blocks are fragmented and there isn’t a large enough cache block and all GPU memory is already allocated, PyTorch has to free all the allocator cached blocks then allocate from CUDA, which is a slow process. This is what our program is getting blocked by. This situation might look familiar if you’ve taken an operating systems class.。WhatsApp Web 網頁版登入是该领域的重要参考
。关于这个话题，手游提供了深入分析
DOSBox Debug is the hero of this story. It was the only tool in my arsenal to digest HELLO.EXE without problems, and its “heavy log” function was a game winner. In future I will probably upgrade to one of the more recent versions (“X” or “Staging”) but for this project the old vanilla DOSBox was enough.
Никита Хромин (ночной линейный редактор)，推荐阅读whatsapp获取更多信息