This article mainly introduces the difference between pytorch .to (device) and .cuda() function in Python.
1. .to (device) Function Can Be Used To Specify CPU or GPU.
# Single GPU or CPU device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") model.to(device) # If it is multi GPU if torch.cuda.device_count() > 1: model = nn.DataParallel(model，device_ids=[0,1,2]) model.to(device)
2. .cuda() Function Can Only Specify GPU.
# Specify a GPU os.environ['CUDA_VISIBLE_DEVICE']='1' model.cuda() # If it is multi GPU os.environment['CUDA_VISIBLE_DEVICES'] = '0,1,2,3' device_ids = [0,1,2,3] net = torch.nn.Dataparallel(net, device_ids =device_ids) # Use all device_ids by default net = torch.nn.Dataparallel(net) net = net.cuda()
3. The Concept Of device-agnostic.
- Device agnostic means that your code can run on any device.
- Code written by PyTorch to method can run on any different devices (CUDA / CPU).
- It is very difficult to write device-agnostic code in PyTorch of previous versions.
- Pytorch 0.4.0 makes code compatible.
- Pytorch 0.4.0 makes code compatibility very easy in two ways.
- Below is some example source code.
# Start the script and create a tensor device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") ... # However, no matter whether you get a new tensor or module, if they are already on the target device, the copy operation will not be performed input = data.to(device) model = MyModule(...).to(device)