*Memos:
- My post explains how to set and get dtype.
- My post explains how to set requires_grad and get grad.
-
My post explains
keepdim
argument. -
My post explains
out
argument. -
My post explains
bias
argument.
You can set and get device as shown below:
*Memos:
- I selected some popular
device
argument functions such as tensor(), arange(), rand(), rand_like(), zeros() and zeros_like(). - Basically,
device
(Optional-Default:None
-Type:int
,str
or device()). - Basically, if
device
isNone
, it's inferred from other tensor or get_default_device() is used. *My post explainsget_default_device()
and set_default_device(). -
cpu
,cuda
,ipu
,xpu
,mkldnn
,opengl
,opencl
,ideep
,hip
,ve
,fpga
,ort
,xla
,lazy
,vulkan
,mps
,meta
,hpu
,mtia
orprivateuseone
can be set todevice
. - Setting
0
todevice
usescuda
(GPU). *The number must be zero or positive. - Basically,
device=
must be needed. - My post explains device().
tensor()
. *My post explains tensor()
:
import torch
my_tensor = torch.tensor([0, 1, 2])
my_tensor = torch.tensor([0, 1, 2], device='cpu')
my_tensor = torch.tensor([0, 1, 2], device=torch.device(device='cpu'))
my_tensor = torch.tensor([0, 1, 2], device=torch.device(type='cpu'))
my_tensor, my_tensor.device
# (tensor([0, 1, 2]), device(type='cpu'))
my_tensor = torch.tensor([0, 1, 2], device='cuda:0')
my_tensor = torch.tensor([0, 1, 2], device='cuda')
my_tensor = torch.tensor([0, 1, 2], device=0)
my_tensor = torch.tensor([0, 1, 2], device=torch.device(device='cuda:0'))
my_tensor = torch.tensor([0, 1, 2], device=torch.device(device='cuda'))
my_tensor = torch.tensor([0, 1, 2], device=torch.device(device=0))
my_tensor = torch.tensor([0, 1, 2], device=torch.device(type='cuda', index=0))
my_tensor = torch.tensor([0, 1, 2], device=torch.device(type='cuda'))
my_tensor, my_tensor.device
# (tensor([0, 1, 2], device='cuda:0'), device(type='cuda', index=0))
tensor()
with is_available(). *My post explains is_available()
:
import torch
my_device = "cuda:0" if torch.cuda.is_available() else "cpu"
my_tensor = torch.tensor([0, 1, 2], device=my_device)
my_tensor, my_tensor.device
# (tensor([0, 1, 2], device='cuda:0'), device(type='cuda', index=0))
arange()
. *My post explains arange()
:
import torch
my_tensor = torch.arange(start=5, end=15, step=3, device='cpu')
my_tensor, my_tensor.device
# (tensor([5, 8, 11, 14]), device(type='cpu'))
my_tensor = torch.arange(start=5, end=15, step=3, device='cuda:0')
my_tensor, my_tensor.device
# (tensor([5, 8, 11, 14], device='cuda:0'), device(type='cuda', index=0))
rand()
. *My post explains rand()
:
import torch
my_tensor = torch.rand(size=(3,), device='cpu')
my_tensor, my_tensor.device
# (tensor([0.2985, 0.4517, 0.1018]), device(type='cpu'))
my_tensor = torch.rand(size=(3,), device='cuda:0')
my_tensor, my_tensor.device
# (tensor([0.6161, 0.8663, 0.8344], device='cuda:0'),
# device(type='cuda', index=0))
rand_like()
. *My post explains rand_like()
:
import torch
my_tensor = torch.rand_like(input=torch.tensor([7., 4., 5.]),
device='cpu')
my_tensor, my_tensor.device
# (tensor([0.8479, 0.3738, 0.7446]), device(type='cpu'))
my_tensor = torch.rand_like(input=torch.tensor([7., 4., 5.]),
device='cuda:0')
my_tensor, my_tensor.device
# (tensor([0.2788, 0.1682, 0.3529], device='cuda:0'),
# device(type='cuda', index=0))
zeros()
. *My post explains zeros()
:
import torch
my_tensor = torch.zeros(size=(3,), device='cpu')
my_tensor, my_tensor.device
# (tensor([0., 0., 0.]), device(type='cpu'))
my_tensor = torch.zeros(size=(3,), device='cuda:0')
my_tensor, my_tensor.device
# (tensor([0., 0., 0.], device='cuda:0'), device(type='cuda', index=0))
zeros_like()
. *My post explains zeros_like()
:
import torch
my_tensor = torch.zeros_like(input=torch.tensor([7., 4., 5.]),
device='cpu')
my_tensor, my_tensor.device
# (tensor([0., 0., 0.]), device(type='cpu'))
my_tensor = torch.zeros_like(input=torch.tensor([7., 4., 5.]),
device='cuda:0')
my_tensor, my_tensor.device
# (tensor([0., 0., 0.], device='cuda:0'), device(type='cuda', index=0))
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