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scotts reviewed Apr 3, 2025
| // Pytorch can only handle up to 128 GPUs. | ||
| // https://.com/pytorch/pytorch/blob/e30c55ee527b40d67555464b9e402b4b7ce03737/c10/cuda/CUDAMacros.h#L44 | ||
| const int MAX_CUDA_GPUS = 128; | ||
| // Set to -1 to have an infinitely sized cache. Set it to 0 to disable caching. | ||
| // Set to a positive number to have a cache of that size. | ||
| const int MAX_CONTEXTS_PER_GPU_IN_CACHE = -1; | ||
| std::vector<AVBufferRef*> g_cached_hw_device_ctxs[MAX_CUDA_GPUS]; | ||
| std::vector<_avbuffer_ref*> g_cached_hw_device_ctxs[MAX_CUDA_GPUS]; |
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AVBufferRef should remain Pascal case
scotts reviewed Apr 3, 2025
| @@ -217,40 +218,41 @@ void convertAVFrameToFrameOutputOnCuda( | |||
| "x3, got ", | |||
| shape); | |||
| } else { | |||
| dst = allocateEmptyHWCTensor(height, width, videoStreamOptions.device); | |||
| dst = | |||
| allocate_empty_h_w_c_tensor(height, width, video_stream_options.device); | |||
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Here and below, I'd find it easier if we wrote this as allocate_empty_hwc_tensor() and nppi_nv12_to_rgb_8u_p2c3r(). Basically, we'll want to be careful about how to turn a sequence of all caps into snake case.
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