🐛 Describe the bug Starting with CUDA 11.0, devices of compute capability 8.0 and above have the capability to influence persistence of data in the L2 cache, potentially providing higher bandwidth and lower latency accesses to global mem...
That’s a strange error. If you’re using a Pytorch build new enough to support autocast, AutocastTensorId should be present in the backend. If you’re not, I’d expect a different error, sooner (torch.cuda.amp.autocast should not be available in Python to begin with). What version ...
├── include │ └── add2.h # cuda算子的头文件├── kernel │ ├── add2_kernel.cu # cuda算子的具体实现│ └── add2.cpp # cuda算子的cpp torch封装├── CMakeLists.txt ├── LICENSE ├── README.md ├── setup.py ├── time.py # 比较cuda算子和torch实现的时间差异...
Cuda compilation tools, release 11.4, V11.4.48 Build cuda_11.4.r11.4/compiler.30033411_0 But when we verify if CUDA is working fine or not by testing the CUDA Samples 11.4 deviceQuery, the test fails: $./deviceQuery ./deviceQuery Starting… CUDA Device Query (Runtime API) versi...
I have a problem, torch.cuda.is_available() returns False. I followed everything in https://developer.nvidia.com/embedded/learn/get-started-jetson-nano-devkit . I also followed all the advice for installing torch and torchvision given in: https://forums.developer.nvidia.com/t/pytorch-for-...
cuda9.0 + cudnn7.0.5 1060-6G 正式开始 与之前实现的任务相同,我这里将libtorch和OpenCV一起编译,使用OpenCV的读取摄像头然后识别当前的手势,模型是我自己训练好的,对于大家来说可以自己随便挑一个模型来使用。 下图为在Visual Studio中使用libtorch和OpenCV来实现判断剪刀石头布手势,运行的平台是cpu端。当然GPU端也...
CUDA 版本的要求不一样，即使使用 Python 虚拟环境也是不可能把不同版本的 CUDA 做隔离，因为 CUDA 和...
CUDA 10 or higher (if you want GPU version) Python 3.6 or higher + headers (python-dev) PyTorch 1.5 or higher (1.4 and 1.3.1 should also be working but are not actively supported moving forward) MinkowskiEngine (optional) seeherefor installation instructions ...
inference:trt_engine:/path/to/engine/filedataset:data:samples_per_gpu:16test:data_prefix:/raid/ImageNet2012/ImageNet2012/valclasses:/raid/ImageNet2012/classnames.txt Use the following command to run classification (PyTorch) engine inference: ...
Running an engine that was generated with a different version of TensorRT and CUDA is not supported and will cause unknown behavior that affects inference speed, accuracy, and stability, or it may fail to run altogether. Option 1 is very straightforward. The .etlt file and calibration cache ...