"robot": {"id": "fetch-001", "type": "Fetch", "dof": 7},
And, if you’re already rocking one of Rode’s audio consoles, the Rodecaster Sync app will make your life a lot easier. Essentially, if you’ve got a Rodecaster Pro 2 or Duo, you’ll be able to hook it up to your Video Core, allowing you to set shortcuts directly to your pads. In fact, you can run your audio and video setup from the one desk, hopefully reducing the amount of fiddling you need to do in the middle of your stream.
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Умер вокалист легендарной рок-группы01:51
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.