把数据从CPU迁移到GPU时,可以用.cuda()方法,也可以用.to(device)方法。示例如下。 .cuda()方法 import torch import time t = time.time() b = torch.rand([1024, 1024, 10]) b = b.cuda() p
首先,定义device device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") 对于变量,需要进行赋值操作才能真正转到GPU上: all_input_batch=all_input_batch.to(device) 对于模型,不需要进行赋值: model =TextRNN() model.to(device) 对模型进行to(device),还有一种方法,就是在定义模型的...
一、.to(device) 可以指定CPU 或者GPU device = torch.device("cuda:0"iftorch.cuda.is_available()else"cpu")#单GPU或者CPUmodel.to(device)#如果是多GPUiftorch.cuda.device_count() > 1: model= nn.DataParallel(model,device_ids=[0,1,2]) model.to(device) mytensor = my_tensor.to(device) 这...
print(torch.cuda.is_available()) #cuda是否可用; print(torch.cuda.device_count()) #返回gpu数量; print(torch.cuda.get_device_name(0)) #返回gpu名字,设备索引默认从0开始; print(torch.cuda.current_device()) #返回当前设备索引; device = torch.device('cuda') #将数据转移到GPU device = torch....
RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu! (when checking argument for argument tensors in method wrapper_CUDA_cat) Trace Shapes: Param Sites: Sample Sites: 1.
RuntimeError: CUDA error: CUDA-capable device(s) is/are busy or unavailable CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect. For debugging consider passing CUDA_LAUNCH_BL...
var train_device = torch.device("cuda:0"); var cr = DarkNet(train_device ); cr.to(train_device); 然后查看cr的数据类型: 如果将DarNet换成nn.Conv2d() 显然是没有问题的 然后笔者再仔细查看每一个成员的数据类型 咨询大佬后少了函数RegisterComponents();进行注册, ...
→ no CUDA-capable device is detected CUDA error at C:\dvs\p4\build\sw\rel\gpgpu\toolkit\r12.2\demo_suite\bandwidthTest\bandwidthTest.cu:255 code=100(cudaErrorNoDevice) “cudaSetDevice(currentDevice)” I have carefully checked the environment variable configuration and found no issues. ...
最后用下面的 python 代码来验证一下是否可以调用 GPU import torch torch.cuda.is_available() # 查看pytorch是否支持CUDA torch.cuda.device_count() # 查看可用的CUDA数量 torch.version.cuda # 查看对应CUDA的版本号 至此,我们的卸载,更新,安装就教完了。
CUDA out of memory，GPU显存申请超出界限了，从后面的信息也能看到：“GPU 0 has a total capacty ...