please use --device cpu
时间: 2023-03-28 19:00:43 浏览: 70
好的,我可以回答这个问题。使用 --device cpu 是指在使用深度学习框架时,将计算任务分配给 CPU 进行处理,而不是 GPU。这通常用于在没有 GPU 的情况下运行模型,或者在需要同时运行多个任务时,将一些任务分配给 CPU。
相关问题
please use english
`torch.arange` is a function in PyTorch used to create a one-dimensional tensor, where the elements increase by a fixed step size from a starting position up to a specified end position. The syntax is as follows:
```python
torch.arange(start=0, end, step=1, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor
```
Here, `start` represents the starting position (default is 0), `end` represents the end position (exclusive), and `step` represents the step size (default is 1). If `out` is not specified, a new tensor is created. `dtype` represents the data type of the output tensor (default is float32). `layout` represents the memory layout of the output tensor (default is `strided`). `device` represents the device of the output tensor (default is CPU). `requires_grad` represents whether or not gradients need to be computed (default is False).
For example, the following code creates a tensor that starts at 0, increments by 2, and stops before 10:
```python
import torch
a = torch.arange(0, 10, 2)
print(a)
```
The output is:
```
tensor([0, 2, 4, 6, 8])
```
Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. If you are running on a CPU-only machine, please use torch.load with map_loc
这个问题涉及到PyTorch的技术细节,我可以回答。这个问题的意思是,在使用PyTorch进行模型加载时,如果CUDA设备不可用,可以使用`torch.load`函数,并通过设置`map_location`参数来指定模型参数应该被加载到CPU上。如果您在CPU-only机器上运行,可以使用以下代码来加载模型:
```python
checkpoint = torch.load(PATH, map_location=torch.device('cpu'))
```
其中`PATH`是模型文件的路径,`torch.device('cpu')`指定将模型参数加载到CPU上。
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