How to use the device function (pytorch)



Python
library
pip

MeCab

Common
class

pickle

read/readline

numpy
asfarray

digitize

expit

linalg.solve

meshgrid

mgrid

ndmin

pad

poly1d

polyfit

prod

shape

matplotlib
figure

pcolormesh

scatter

pytorch
BCELoss, MSELoss

device

Embedding

TensorDataset, Dataloader

RNN, LSTM
scikit-learn
SVC

GaussianNB

scipy
interpolate
tkinter
postscript

image display

frame, grid

Crop Image

other
linear interpolation

Hysteresis switch

Square/Triangle wave

OpenAI gym
CartPole-v0

By purpose
1 of K Coding


Release date:2022/10/20         

In Japanese
<premise knowledge>
Python


■Description
Specify the GPU (cuda) to use with pytorch. cuda is a development environment for GPU provided by NVIDIA.

■Example
First, check if your PC has a GPU that can be used with pytorch.

import torch
print(torch.cuda.is_available())

  ⇒ False # False if there is no cuda environment


Next, store either "cuda" or "cpu" in a variable as a device name that can be used, as follows.

dev = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") #(1)
print(dev)

  ⇒ device(type='cpu')


By using ".to", you can perform operations using cuda or cpu as follows.

b = torch.zeros(4)
c = b.to(dev)
print(c)

  ⇒ tensor([0., 0., 0., 0.])


If you get an error message like the one below, the GPU is set to be used even though it cannot be used. Let's check if the formula in #(1) above is set correctly.

 "Torch not compiled with CUDA enabled"









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Python
library
pip

MeCab

Common
class

pickle

read/readline

numpy
asfarray

digitize

expit

linalg.solve

meshgrid

mgrid

ndmin

pad

poly1d

polyfit

prod

shape

matplotlib
figure

pcolormesh

scatter

pytorch
BCELoss, MSELoss

device

Embedding

TensorDataset, Dataloader

RNN, LSTM
scikit-learn
SVC

GaussianNB

scipy
interpolate
tkinter
postscript

image display

frame, grid

Crop Image

other
linear interpolation

Hysteresis switch

Square/Triangle wave

OpenAI gym
CartPole-v0

By purpose
1 of K Coding