【cuda】This graphics driver could not find compatible graphics hardware..,程序员大本营,技术文章内容聚合第一站。
OSError: (External) CUDA error(3), initialization error. [Hint: 'cudaErrorInitializationError'. The API call failed because the CUDA driver and runtime could not be initialized. ] (at /paddle/paddle/fluid/platform/gpu_info.cc:355) [pid: 508038|app: 0|req: 2/2] 124.160.31.180 () {48...
Finally, the cuda container appear to be working properly: ~$ sudo docker run --rm --gpus all nvidia/cuda:11.0.3-base-ubuntu20.04 nvidia-smi Unable to find image 'nvidia/cuda:11.0.3-base-ubuntu20.04' locally 11.0.3-base-ubuntu20.04: Pulling from nvidia/cuda d7...
I tried building Arrow C++ andpyarrowto start contributing, but I ran into an issue that I am not familiar with and that I could not debug myself. I followed the tutorial from the docs:https://arrow.apache.org/docs/developers/python.html#using-system-and-bundled-dependencies ...
But other modes (CUDAExecutionProviderandCPUExecutionProvider) work properly. But it seems that it could be run inTensorrtExecutionProviderproviders. Environment TensorRT Version: 8.2.0.6. GPU Type: 1080 Ti Nvidia Driver Version: 470.82.01
version of the library from mrcnn.config import Config from mrcnn import model as modellib, utils # Path to trained weights file COCO_MODEL_PATH = os.path.join(ROOT_DIR, "mask_rcnn_coco.h5") # Directory to save logs and model checkpoints, if not provided # through the command line...
Somewhere earlier this macro was redefined (or not defined at all if it was conditional on some other macro) and now your new operators do not have the parameters they need.It is just a guess, you will need to verify if it was the case or not....
TRT Warning: Could not find TensorRT WARNING: All log messages beforeabsl::InitializeLog() is called are written to STDERR I0000 00:00:1728650506.662098 1240 cuda_executor.cc:1001] could not open file toreadNUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node Your kernel may have been ...
Hi I did not find any solution, maybe you have another suggestion that can help me? nvidia-smi prompts: +---+ | NVIDIA-SMI 432.00 Driver Version: 432.00 CUDA Version: 10.1 | |---+---+---+ | GPU Name TCC/WDDM | Bus-Id Disp.A | Volatile Uncorr. ECC |...
However, the terminal is still very slow at this step. Applied providers: ['CUDAExecutionProvider', 'CPUExecutionProvider'], with options: {'CUDAExecutionProvider': {'cudnn_conv_algo_search': 'EXHAUSTIVE', 'device_id': '0', 'has_user_compute_stream': '0', 'cudnn_conv1d_pad_to_nc1...