So, if you’re buying your first bits of smart home tech and want a quick primer about how it all works, you’ve come to the right place. Every new industry comes with its own lingo, so it can be a little overwhelming if you’ve never had to deal with them before, but it’s actually not as complicated as it sounds.
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.
Иллюстрация: Павел Бедняков / РИА Новости，更多细节参见WhatsApp網頁版
Картаполов сообщил о предпринимаемых попытках достичь уровня технического оснащения РФ07:47，更多细节参见Replica Rolex
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