Pytorch 0.4.0: There are three ways to create tensors on CUDA device. Is there some difference between them?
I failed in the third way. t3 is still on CPU. No idea why.
a = np.random.randn(1, 1, 2, 3)
t1 = torch.tensor(a)
t1 = t3.to(torch.device('cuda'))
t2 = torch.tensor(a)
t2 = t2.cuda()
t3 = torch.tensor(a, device=torch.device('cuda'))
pytorch tensor
edited Nov 16 '18 at 6:37
Umang Gupta
3,56711740
asked Nov 16 '18 at 4:01
laridzhanglaridzhang
686
add a comment |
I failed in the third way. t3 is still on CPU. No idea why.
a = np.random.randn(1, 1, 2, 3)
t1 = torch.tensor(a)
t1 = t3.to(torch.device('cuda'))
t2 = torch.tensor(a)
t2 = t2.cuda()
t3 = torch.tensor(a, device=torch.device('cuda'))
pytorch tensor
edited Nov 16 '18 at 6:37
Umang Gupta
3,56711740
asked Nov 16 '18 at 4:01
laridzhanglaridzhang
686
add a comment |
I failed in the third way. t3 is still on CPU. No idea why.
a = np.random.randn(1, 1, 2, 3)
t1 = torch.tensor(a)
t1 = t3.to(torch.device('cuda'))
t2 = torch.tensor(a)
t2 = t2.cuda()
t3 = torch.tensor(a, device=torch.device('cuda'))
pytorch tensor
edited Nov 16 '18 at 6:37
Umang Gupta
3,56711740
asked Nov 16 '18 at 4:01
laridzhanglaridzhang
686
I failed in the third way. t3 is still on CPU. No idea why.
a = np.random.randn(1, 1, 2, 3)
t1 = torch.tensor(a)
t1 = t3.to(torch.device('cuda'))
t2 = torch.tensor(a)
t2 = t2.cuda()
t3 = torch.tensor(a, device=torch.device('cuda'))
pytorch tensor
pytorch tensor
edited Nov 16 '18 at 6:37
Umang Gupta
3,56711740
asked Nov 16 '18 at 4:01
laridzhanglaridzhang
686
edited Nov 16 '18 at 6:37
Umang Gupta
3,56711740
asked Nov 16 '18 at 4:01
laridzhanglaridzhang
686
edited Nov 16 '18 at 6:37
Umang Gupta
3,56711740
edited Nov 16 '18 at 6:37
Umang Gupta
3,56711740
edited Nov 16 '18 at 6:37
Umang Gupta
3,56711740
3,56711740
asked Nov 16 '18 at 4:01
laridzhanglaridzhang
686
asked Nov 16 '18 at 4:01
laridzhanglaridzhang
686
asked Nov 16 '18 at 4:01
laridzhanglaridzhang
686
686
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
All three methods worked for me.
In 1 and 2, you create a tensor on CPU and then move it to GPU when you use .to(device) or .cuda(). They are same here.
However, when you use .to(device) method you can explicitly tell torch to move to specific GPU by setting device=torch.device("cuda:<id>"). with .cuda() you have to do .cuda(<id>) to move to some particular GPU.
Why do these two methods exist then?
.to(device) was introduced in 0.4 because it is easier to declare device variable at top of the code as
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
and use .to(device) everywhere. This makes it quite easy to switch from CPU to GPU and vice-versa
Before this, we had to use .cuda() and your code will have if check for cuda.is_available() everywhere which made it cumbersome to switch between GPU/CPU.
The third method doesn't create a tensor on CPU and directly copies data to GPU, which is more efficient.
answered Nov 16 '18 at 6:37
Umang GuptaUmang Gupta
3,56711740
add a comment |
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1 Answer
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1 Answer
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All three methods worked for me.
In 1 and 2, you create a tensor on CPU and then move it to GPU when you use .to(device) or .cuda(). They are same here.
However, when you use .to(device) method you can explicitly tell torch to move to specific GPU by setting device=torch.device("cuda:<id>"). with .cuda() you have to do .cuda(<id>) to move to some particular GPU.
Why do these two methods exist then?
.to(device) was introduced in 0.4 because it is easier to declare device variable at top of the code as
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
and use .to(device) everywhere. This makes it quite easy to switch from CPU to GPU and vice-versa
Before this, we had to use .cuda() and your code will have if check for cuda.is_available() everywhere which made it cumbersome to switch between GPU/CPU.
The third method doesn't create a tensor on CPU and directly copies data to GPU, which is more efficient.
answered Nov 16 '18 at 6:37
Umang GuptaUmang Gupta
3,56711740
add a comment |
All three methods worked for me.
In 1 and 2, you create a tensor on CPU and then move it to GPU when you use .to(device) or .cuda(). They are same here.
However, when you use .to(device) method you can explicitly tell torch to move to specific GPU by setting device=torch.device("cuda:<id>"). with .cuda() you have to do .cuda(<id>) to move to some particular GPU.
Why do these two methods exist then?
.to(device) was introduced in 0.4 because it is easier to declare device variable at top of the code as
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
and use .to(device) everywhere. This makes it quite easy to switch from CPU to GPU and vice-versa
Before this, we had to use .cuda() and your code will have if check for cuda.is_available() everywhere which made it cumbersome to switch between GPU/CPU.
The third method doesn't create a tensor on CPU and directly copies data to GPU, which is more efficient.
answered Nov 16 '18 at 6:37
Umang GuptaUmang Gupta
3,56711740
add a comment |
All three methods worked for me.
In 1 and 2, you create a tensor on CPU and then move it to GPU when you use .to(device) or .cuda(). They are same here.
However, when you use .to(device) method you can explicitly tell torch to move to specific GPU by setting device=torch.device("cuda:<id>"). with .cuda() you have to do .cuda(<id>) to move to some particular GPU.
Why do these two methods exist then?
.to(device) was introduced in 0.4 because it is easier to declare device variable at top of the code as
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
and use .to(device) everywhere. This makes it quite easy to switch from CPU to GPU and vice-versa
Before this, we had to use .cuda() and your code will have if check for cuda.is_available() everywhere which made it cumbersome to switch between GPU/CPU.
The third method doesn't create a tensor on CPU and directly copies data to GPU, which is more efficient.
answered Nov 16 '18 at 6:37
Umang GuptaUmang Gupta
3,56711740
All three methods worked for me.
In 1 and 2, you create a tensor on CPU and then move it to GPU when you use .to(device) or .cuda(). They are same here.
However, when you use .to(device) method you can explicitly tell torch to move to specific GPU by setting device=torch.device("cuda:<id>"). with .cuda() you have to do .cuda(<id>) to move to some particular GPU.
Why do these two methods exist then?
.to(device) was introduced in 0.4 because it is easier to declare device variable at top of the code as
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
and use .to(device) everywhere. This makes it quite easy to switch from CPU to GPU and vice-versa
Before this, we had to use .cuda() and your code will have if check for cuda.is_available() everywhere which made it cumbersome to switch between GPU/CPU.
The third method doesn't create a tensor on CPU and directly copies data to GPU, which is more efficient.
answered Nov 16 '18 at 6:37
Umang GuptaUmang Gupta
3,56711740
edited Nov 16 '18 at 6:45
answered Nov 16 '18 at 6:37
Umang GuptaUmang Gupta
3,56711740
answered Nov 16 '18 at 6:37
Umang GuptaUmang Gupta
3,56711740
answered Nov 16 '18 at 6:37
Umang GuptaUmang Gupta
3,56711740
3,56711740
add a comment |
add a comment |
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