Low performance of transferring tensor to CUDA

Hello Team, i using Libtorch with CUDA support. I get lowest performance on transferring memory to CUDA operation. It cost about 2.60s.
tensor_image = tensor_image.to(torch::kCUDA);
How can i improve this operation, check it or profile?

  clock_t tROIset = clock();
  cv::Rect myROI(30, 10, 400, 400);
  printf("ROI set.\n Time taken: %.2fs\n", (double)(clock() - tROIset)/CLOCKS_PER_SEC);

  clock_t tLoadOpenCV = clock();
  cv::Mat img = cv::imread("test_data/image.png");     // 600x900  
  printf("loader of Tensor.\n Time taken: %.2fs\n", (double)(clock() - tLoadOpenCV)/CLOCKS_PER_SEC);
  clock_t tTester = clock();
  tensor_image = tensor_image.to(torch::kCUDA);
  printf("CPU-GPU transfer.\n Time taken: %.2fs\n", (double)(clock() - tTester)/CLOCKS_PER_SEC);

Result of execution:

ROI set in Tensor . Time taken: 0.00s
loader of Tensor. Time taken: 0.01s
CPU-GPU Tensor transfer. Time taken: 2.60s

PyTorch version: 1.10.0
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2019 NVIDIA Corporation
Built on Wed_Oct_23_19:24:38_PDT_2019
Cuda compilation tools, release 10.2, V10.2.89

How can i use non blocking memory transfering here ?
non_blocking=True, or something like this…

CUDA operations are executed asynchronously, so you would need to synchronize the code before starting and stopping the timers. Based on your code snippet you might profile the CUDA context initialization etc. which would increase the measured time.

Yes, non_blocking=True would allow the transfer to be executed in the background. Profiling it could still show the same time as the advantage would be a potential overlap with other workloads.

How can i set this parametrs ( non_blocking=True) in C/C++ code?
Are there examples?

The to() operations as well as e.g.copy_ accept the non_blocking argument and an example was posted in your other question.

Thanks Patrick, for the example, it seems things have moved)