Although CUDA kernel launches are asynchronous, all GPU-related tasks placed in one stream (which is the default behavior) are executed sequentially. So, for example, So in your example, there is no need for cudaDeviceSynchronize. However, it might be useful for debugging to detect which of your kernel has caused an error (if there is any). cudaDeviceSynchronize may … Read more
Nope, you can’t use CUDA for that. CUDA is limited to NVIDIA hardware. OpenCL would be the best alternative. Khronos itself has a list of resources. As does the StreamComputing.eu website. For your AMD specific resources, you might want to have a look at AMD’s APP SDK page. Note that at this time there are several initiatives to translate/cross-compile CUDA … Read more
i am facing issue with my inception model during the performance testing with Apache JMeter. Error: OOM when allocating tensor with shape[800,1280,3] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc [[Node: Cast = CastDstT=DT_FLOAT, SrcT=DT_UINT8, _device=”/job:localhost/replica:0/task:0/device:GPU:0″]] Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for … Read more
Does Numpy/Python automatically detect the presence of GPU and utilize it to speed up matrix computation (e.g. numpy.multiply, numpy.linalg.inv, … etc)? No. Or do I have code in a specific way to exploit the GPU for fast computation? Yes. Search for Numba, CuPy, Theano, PyTorch or PyCUDA for different paradigms for accelerating Python with GPUs.
If you are using TensorFlow with GPU, you need to install CUDA and cuDNN. Please follow instructions on https://www.tensorflow.org/install/ If you have already install CUDA and cuDNN, but still get this error, then you probably forgot to export your libraries: for Linux, you may need to set LD_LIBRARY_PATH to include CUDA libraries.
No, I don’t think “open CUDA library” is enough to tell, because different nodes of the graph may be on different devices. When using tensorflow2: For tensorflow1, to find out which device is used, you can enable log device placement like this: Check your console for this type of output.
Surprise surprise, rebooting solved the issue (I thought I had already tried that). The solution Robert Crovella mentioned in the comments may also be useful to someone else, since it’s pretty similar to what I did to solve the issue the first time I had it.