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// Private — only visible within package mathutil
定价策略、服务设计、品牌调性，人定方向，虾干苦力。。关于这个话题，新收录的资料提供了深入分析
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.。新收录的资料对此有专业解读
Фото: Алексей Сухоруков / РИА Новости。新收录的资料是该领域的重要参考
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