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(torch::kCUDA, 0); //打印设备...
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...
#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; ...
export LD_LIBRARY_PATH=/path/to/your/cuda/libs:$LD_LIBRARY_PATH 3. 检查PyTorch安装 确保PyTorch已正确安装并且与你的CUDA版本兼容。你可以使用以下命令检查PyTorch和CUDA的版本信息: import torch print(torch.__version__) print(torch.cuda.is_available()) print(torch.cuda.get_device_properties(0).name...
#include <iostream> #include <torch/torch.h> #include<torch/script.h> using namespace std; int main() { cout << "cuda是否可用:" << torch::cuda::is_available() << endl; cout << "cudnn是否可用:" << torch::cuda::cudnn_is_available() << endl; cout << torch::cuda::device_...
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); ...
#include <iostream> #include <torch/torch.h> int main() { std::cout << "Is CUDA available? " << torch::cuda::is_available() << std::endl; std::cout << "CUDA device count: " << torch::cuda::device_count() << std...
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." << ...
(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!