TX2 搭建pytorch v0.3

TX2 环境：cuda8.0  cudnn6.0  Ubuntu16.04
参考：https://github.com/dusty-nv/jetson-reinforcement/
$ mkdir pytorch
$ git clone http://github.com/dusty-nv/jetson-reinforcement
$ cd jetson-reinforcement
$ git submodule update --init
$ mkdir build
$ cd build
$ cmake ../
$ make

依次运行以上命令，所需时间较长，要保证网络畅通，所需内存也不小，保证tx2空间够用

验证pytorch是否安装好：

$ python    然后依次输入以下命令，

>>> import torch
>>> print(torch.__version__)
>>> print('CUDA available: ' + str(torch.cuda.is_available()))
>>> 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))

会出现以下结果：

Python 2.7.12 (default, Nov 12 2018, 14:36:10) 
[GCC 5.4.0 20160609] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch
>>> print(torch.__version__)
0.3.0b0+af3964a
>>> print('CUDA available: ' + str(torch.cuda.is_available()))
CUDA available: True
>>> a = torch.cuda.FloatTensor(2).zero_()
>>> print('Tensor a = ' + str(a))
Tensor a = 
 0
 0
[torch.cuda.FloatTensor of size 2 (GPU 0)]

>>> b = torch.randn(2).cuda()
>>> print('Tensor b = ' + str(b))
Tensor b = 
-0.3896
-0.5981
[torch.cuda.FloatTensor of size 2 (GPU 0)]

>>> c = a + b
>>> print('Tensor c = ' + str(c))
Tensor c = 
-0.3896 
-0.5981
[torch.cuda.FloatTensor of size 2 (GPU 0)]