Source: https://forums.developer.nvidia.com/t/pytorch-for-jetson-nano-version-1-4-0-now-available/72048
I followed this page to install.
My Jetpack version is 4.3
I am going to install PyTorch v1.3.0
wget https://nvidia.box.com/shared/static/phqe92v26cbhqjohwtvxorrwnmrnfx1o.whl -O torch-1.3.0-cp36-cp36m-linux_aarch64.whl
- Install this whl file
sudo -H python3 -m pip install numpy torch-1.3.0-cp36-cp36m-linux_aarch64.whl
- Install torchvision; so git clone it for my vision. They provided us a list.
PyTorch v1.0 - torchvision v0.2.2
PyTorch v1.1 - torchvision v0.3.0
PyTorch v1.2 - torchvision v0.4.0
PyTorch v1.3 - torchvision v0.4.2
PyTorch v1.4 - torchvision v0.5.0
So we can know that the version should be installed by v0.4.2
sudo apt-get install libjpeg-dev zlib1g-dev
git clone --branch v0.4.2 https://github.com/pytorch/vision torchvision
cd torchvision
- Install it.
sudo -H python3 setup.py install
- Install pillow
sudo -H python3 -m pip install 'pillow<7'
>>> import torch
>>> print(torch.__version__)
>>> print('CUDA available: ' + str(torch.cuda.is_available()))
>>> print('cuDNN version: ' + str(torch.backends.cudnn.version()))
>>> a = torch.cuda.FloatTensor(2).zero_()
>>> print('Tensor a = ' + str(a))
>>> b = torch.randn(2).cuda()
>>> print('Tensor b = ' + str(b))
>>> c = a + b
>>> print('Tensor c = ' + str(c))
>>> import torchvision
>>> print(torchvision.__version__)
We can directly follow the commands from Pytorch official website.
Environmnet setting:
OS: Ubuntu 18.04 CDUA: 10.0 cuDNN: 7.6. Version table:
| torch | torchvision |
|---|---|
| 1.5.0 | 0.6.0 |
| 1.4.0 | 0.5.0 |
| 1.3.1 | 0.4.2 |
Install torch and torchvision:
python3 -m pip install torch==1.4.0 torchvision==0.5.0 -f https://download.pytorch.org/whl/torch_stable.html
Note : you can also decide the specific cuda version and choose to install the cpu or gpu version. Please follow the instructions of official website.
For example, my CUDA version is 10.0.
Command:
pip3 install torch==1.4.0+cu100 torchvision==0.5.0+cu100 -f https://download.pytorch.org/whl/torch_stable.html
sudo -H python3 -m pip uninstall torch torchvision
Use python3 to check.
The code from here.
from __future__ import print_function
import torch
x = torch.rand(5, 3)
print(x)
Check the gpu whether it works or not.
import torch
torch.cuda.is_available()
Should be return True