在Kubernetes上的JupyterHub，通过Notebook快速运行PyTorch例程，测试镜像是否可用。
1、快速安装
在我的镜像中，已经将PyTorch、TorchVision打包到JupyterHub for K8s的Notebook镜像中，可以直接使用。
或者，在notebook中进行安装，如下：
%%bash
pip install torch torchvision-
注意：%%bash为Notebook的魔法操作符，详细使用参见：
-
在Github的Databook项目获取本Notebook的源文件：
2、导入引用库
from __future__ import print_function, division
import os
import torch3、运行例程
x = torch.rand(5, 3)
print(x)
输出：
tensor([[0.4482, 0.9189, 0.2227],
[0.3906, 0.4695, 0.1300],
[0.5034, 0.7224, 0.0471],
[0.5570, 0.4676, 0.8005],
[0.0363, 0.2650, 0.1269]])4、查看torchvision库方法
查看torchvison的信息：
import torchvision
help(torchvision)输出信息：
Help on package torchvision:
NAME
torchvision
PACKAGE CONTENTS
_C
datasets (package)
models (package)
ops (package)
transforms (package)
utils
version
FUNCTIONS
get_image_backend()
Gets the name of the package used to load images
set_image_backend(backend)
Specifies the package used to load images.
Args:
backend (string): Name of the image backend. one of {'PIL', 'accimage'}.
The :mod:`accimage` package uses the Intel IPP library. It is
generally faster than PIL, but does not support as many operations.
VERSION
0.3.0
FILE
/opt/conda/lib/python3.6/site-packages/torchvision/__init__.py
5、查看torch库方法
help(torch)输出信息：
Help on package torch:
NAME
torch
DESCRIPTION
The torch package contains data structures for multi-dimensional
tensors and mathematical operations over these are defined.
Additionally, it provides many utilities for efficient serializing of
Tensors and arbitrary types, and other useful utilities.
It has a CUDA counterpart, that enables you to run your tensor computations
on an NVIDIA GPU with compute capability >= 3.0.
PACKAGE CONTENTS
_C
__config__
_dl
_jit_internal
_ops
_six
_storage_docs
_tensor_docs
_tensor_str
_thnn (package)
_torch_docs
_utils
_utils_internal
autograd (package)
backends (package)
contrib (package)
cuda (package)
distributed (package)
distributions (package)
for_onnx (package)
functional
hub
jit (package)
multiprocessing (package)
nn (package)
onnx (package)
optim (package)
quasirandom
random
serialization
sparse (package)
storage
tensor
testing (package)
utils (package)
version
SUBMODULES
cpp
ops
......
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