using namespace torch; //这一句一般是不建议这么使用的 2. 判断CUDA是否可以使用: //测试CUDA是否可以使用 cout << "CUDA is available = " << torch::cuda::is_available() << endl; 3. 其他测试代码: //获取第一块设备信息 c10::Device device = torch::Device(t
#include <torch/torch.h> #include <torch/script.h> using namespace torch; int main() { torch::DeviceType device_type = at::kCPU; if (torch::cuda::is_available()) { cout << "cuda!" << endl; torch::DeviceType device_type = at::kCUDA; } else { cout << "cpu" << endl; ...
std::cout <<"检查CDUA是否可用:"<< torch::cuda::is_available() << std::endl; std::cout <<"检查cudnn是否可用:"<< torch::cuda::cudnn_is_available() << std::endl; std::clock_ts, e; s =clock(); torch::Tensor cuda_output;for(inti=0;i<1;i++) { cuda_output = torch::ra...
cache cleaning is disabled. This flag has no effect if model is on CPU. Setting this flag to true will negatively impact the performance due to additional CUDA cache cleaning operation after each model execution. Therefore, you should only use this flag if you serve ...
总结 解决’Failed to load image Python extension: libtorch_cuda_cu.so’错误通常涉及到检查CUDA版本、环境变量设置、依赖项安装以及库文件的存在性。通过仔细检查和遵循上述步骤,你应该能够解决这个问题并成功在Python环境中加载PyTorch的GPU扩展。相关文章推荐 ...
(1); //检查是否调用cuda torch::DeviceType device_cpu_type = torch::kCPU; torch::DeviceType device_CUDA_type = torch::kCUDA; torch::Device device(device_cpu_type); torch::Device device_cpu(device_cpu_type); if (torch::cuda::is_available()) { std::cout << "CUDA available!
安装结束后需要测试是否成功安装gpu版本的pytorch,这里依旧在torch115_gpu环境下输入python,进入python编程环境后输入import torch 回车后输入torch.cuda.is_available()。如果返回True则安装成功。 (4)打开pycharm并打开需要配置环境的项目,会在右上角提示配置环境,点开配置环境选择conda—>使用已有的环境就可以选择激活...
std::cout <<"cuda:cudnn_is_available()"<< torch::cuda::cudnn_is_available() << std::endl; std::cout <<"cuda::device_count()"<< torch::cuda::device_count() << std::endl; torch::Device device(torch::kCUDA); torch::Tensor tensor1 = torch::eye(3); ...
h> // 选择GPU或CPU void chooseDevice() { torch::manual_seed(1); torch::DeviceType device_type; if (torch::cuda::is_available()) { std::cout << "CUDA available! Training on GPU." << std::endl; device_type = torch::kCUDA; } else { std::cout << "Training on CPU." << ...
/INCLUDE:?warp_size@cuda@at@@YAHXZ 如果不配置这个, torch::cuda::is_available() 会返回0,就是不能使用CUDA。 五,测试 我们用一个最简单的测试测试自己有没有配置正确 #include<iostream> #include<memory> #include<torch/torch.h> intmain() ...