问题
I would like to know if pytorch is using my GPU. It's possible to detect with nvidia-smi if there is any activity from the GPU during the process, but I want something written in a python script.
Is there a way to do so?
回答1:
This is going to work :
In [1]: import torch
In [2]: torch.cuda.current_device()
Out[2]: 0
In [3]: torch.cuda.device(0)
Out[3]: <torch.cuda.device at 0x7efce0b03be0>
In [4]: torch.cuda.device_count()
Out[4]: 1
In [5]: torch.cuda.get_device_name(0)
Out[5]: 'GeForce GTX 950M'
In [6]: torch.cuda.is_available()
Out[6]: True
This tells me the GPU GeForce GTX 950M is being used by PyTorch.
回答2:
After you start running the training loop, if you want to manually watch it from the terminal whether your program is utilizing the GPU resources and to what extent, then you can use:
$ watch -n 2 nvidia-smi
This will update the usage stats for every 2 seconds until you press ctrl+c
Also, you can check whether your installation of PyTorch detects your CUDA installation correctly by doing:
In [13]: import torch
In [14]: torch.cuda.is_available()
Out[14]: True
True status means that PyTorch is configured correctly and is using the GPU although you have to move/place the tensors with necessary statements in your code.
If you want to do this inside Python code, then look into this module:
https://github.com/jonsafari/nvidia-ml-py or in pypi here: https://pypi.python.org/pypi/nvidia-ml-py/
回答3:
As it hasn't been proposed here, I'm adding a method using torch.device, as this is quite handy, also when initializing tensors on the correct device.
# setting device on GPU if available, else CPU
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print('Using device:', device)
print()
#Additional Info when using cuda
if device.type == 'cuda':
print(torch.cuda.get_device_name(0))
print('Memory Usage:')
print('Allocated:', round(torch.cuda.memory_allocated(0)/1024**3,1), 'GB')
print('Cached: ', round(torch.cuda.memory_cached(0)/1024**3,1), 'GB')
Output:
Using device: cuda
Tesla K80
Memory Usage:
Allocated: 0.3 GB
Cached: 0.6 GB
As mentioned above, using device it is possible to:
To move tensors to the respective
device:torch.rand(10).to(device)To create a tensor directly on the
device:torch.rand(10, device=device)
Which makes switching between CPU and GPU comfortable without changing the actual code.
Edit:
As there has been some questions and confusion about the cached and allocated memory I'm adding some additional information about it:
torch.cuda.max_memory_cached(device=None)
Returns the maximum GPU memory managed by the caching allocator in bytes for a given device.torch.cuda.memory_allocated(device=None)
Returns the current GPU memory usage by tensors in bytes for a given device.
You can either directly hand over a device as specified further above in the post or you can leave it None and it will use the current_device().
回答4:
On the office site and the get start page, check GPU for PyTorch as below:
import torch
torch.cuda.is_available()
Reference: PyTorch|Get Start
回答5:
From practical standpoint just one minor digression:
import torch
dev = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
This dev now knows if cuda or cpu.
And there is a difference how you deal with model and with tensors when moving to cuda. It is a bit strange at first.
import torch
dev = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
t1 = torch.randn(1,2)
t2 = torch.randn(1,2).to(dev)
print(t1) # tensor([[-0.2678, 1.9252]])
print(t2) # tensor([[ 0.5117, -3.6247]], device='cuda:0')
t1.to(dev)
print(t1) # tensor([[-0.2678, 1.9252]])
print(t1.is_cuda) # False
t1=t1.to(dev)
print(t1) # tensor([[-0.2678, 1.9252]], device='cuda:0')
print(t1.is_cuda) # True
model = M() # not on cuda
model.to(dev) # is on cuda (all parameters)
print(next(model.parameters()).is_cuda) #True
This all is tricky and understanding it once, helps you to deal fast with less debugging.
回答6:
To check if there is a GPU available:
torch.cuda.is_available()
If the above function returns False, you either have no GPU, or the Nvidia drivers have not been installed so the OS does not see the GPU, or the GPU is being hidden by the environmental variable CUDA_VISIBLE_DEVICES. When the value of CUDA_VISIBLE_DEVICES is -1, then all your devices are being hidden. You can check that value in code with this line: `os.environ['CUDA_VISIBLE_DEVICES']
If the above function returns True that does not necessarily mean that you are using the GPU. In Pytorch you can allocate tensors to devices when you create them. By default, tensors get allocated to the cpu. To check where your tensor is allocated do:
# assuming that 'a' is a tensor created somewhere else
a.device # returns the device where the tensor is allocated
Note that you cannot operate on tensors allocated in different devices. To see how to allocate a tensor to the GPU, see here: https://pytorch.org/docs/stable/notes/cuda.html
回答7:
Almost all answers here reference torch.cuda.is_available(). However, that's only one part of the coin. It tells you whether the GPU (actually CUDA) is available, not whether it's actually being used. In a typical setup, you would set your device with something like this:
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
but in larger environments (e.g. research) it is also common to give the user more options, so based on input they can disable CUDA, specify CUDA IDs, and so on. In such case, whether or not the GPU is used is not only based on whether it is available or not. After the device has been set to a torch device, you can get its type property to verify whether it's CUDA or not.
if device.type == 'cuda':
# do something
回答8:
FWIW: If you are here because your pytorch always gives false for torch.cuda.is_available() that's probably because you installed your pytorch version without GPU support. (Eg: you coded up in laptop then testing on server). Solution is to uninstall and install pytorch again with the right command from pytorch downloads page. Also refer this pytorch issue.
回答9:
Simply from command prompt or Linux environment run the following command.
python -c 'import torch; print(torch.cuda.is_available())'
The above should print True
python -c 'import torch; print(torch.rand(2,3).cuda())'
This one should print the following:
tensor([[0.7997, 0.6170, 0.7042], [0.4174, 0.1494, 0.0516]], device='cuda:0')
回答10:
Create a tensor on the GPU as follows:
$ python
>>> import torch
>>> print(torch.rand(3,3).cuda())
Do not quit, open another terminal and check if the python process is using the GPU using:
$ nvidia-smi
来源：https://stackoverflow.com/questions/48152674/how-to-check-if-pytorch-is-using-the-gpu