y=X.to(device),y.to(device)# 1. 前向传播y_pred=model(X)# 2. 计算并累加损失loss=loss_fn...
4. 实例化模型,并移动到 device上。 代码语言:javascript 代码运行次数:0 运行 AI代码解释 import torch from torch import nn # Make device agnostic code device = "cuda" if torch.cuda.is_available() else "cpu" class CircleModelV0(nn.Module): def __init__(self): super().__init__() # ...
# Setup device agnostic code device = "cuda" if torch.cuda.is_available() else "cpu" print(f"Using device: {device}") Using device: cuda 如果有 GPU,上面的内容应该已经打印出来了: Using device: cuda 否则,将使用 CPU 进行下列计算。这对于小型数据集还好,但是对于大型数据集则需要更长的时间。
Setup the inference mode context manager with torch.no_grad(): # 3. Make sure the calculations are done with the model and data on the same device # in our case, we haven't setup device-agnostic code yet so our data and model are # on the CPU by default. # model_0.to(device) ...
(benefitting from GPU acceleration when available but falling back to CPU when not) is to pick and save the appropriatetorch.device, which can be used to determine where tensors should be stored. See theofficial docsfor more tips on device-agnostic code. The PyTorch way is to put device ...
在本章,我们将通过训练和使用线性回归模型来介绍标准 PyTorch 工作流程。 PyTorch 工作流程 我们将得到torch、torch.nn(nn代表神经网络,这个包包含在 PyTorch 中创建神经网络的构建块)和matplotlib。 代码语言:javascript 代码运行次数:0 运行 AI代码解释 importtorch ...
Tensors and Dynamic neural networks in Python with strong GPU acceleration - refactor torch.cuda.device code to be device-agnostic · pytorch/pytorch@00199ac
Importantly, the above piece of code is device agnostic, that is, you don’t have to separately change it for it to work on bothGPU and the CPU. cuda() function One way to transfer tensors to GPUs is by using thecuda(n)function, wherenspecifies the index of the GPU. If you call...
Stack from ghstack (oldest at bottom): -> refactor torch.cuda.device code to be device-agnostic #148880 Support torch.device as accelerator's device switch context #148864 cc @H-Huang @awgu @kwen...
我们在编写代码的时候并不知道代码运行在什么样的设备上,这被称为Device Agnostic,所以cuda和cpu这种硬编码的代码不太常用,而to这个方法可以通过传参数来决定使用什么设备。 torch.cuda.is_avaliable()根据返回结果给参数字符串赋值。 Lesson 36 PyTorch Dataset Normalization - Torchvision.Transforms.Normalize() Normali...