申请与创建Amazon实例
申请Amazon实例并创建的过程其实相对简单，参考Amazon完备的文档即可：
创建示例：http://docs.aws.amazon.com/AWSEC2/latest/WindowsGuide/EC2_GetStarted.html
我这里最终选择了Amazon的Linux免费实例
关于如何在AWS上配置云端服务器跑机器学习程序，以下列出各个问题的解决方案和资源，方便随时自取。
## 基础级别
[Mac终端指令大全](http://www.jianshu.com/p/3291de46f3ff)
[Vim指令大全](http://www.jianshu.com/p/117253829581)
[vi指令大全](http://www.cnblogs.com/88999660/articles/1581524.html)
## AWS的EC2配置
[AWS Signup & Getting Started](http://docs.aws.amazon.com/AWSEC2/latest/WindowsGuide/EC2_GetStarted.html)
[Getting Started with Amazon EC2 Linux Instances](http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/EC2_GetStarted.html)
[Connecting to Your Linux Instance Using SSH](http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/AccessingInstancesLinux.html)
## AWS的环境配置
[一文搞定所有配置--Jupyter, Plotly, Pandas, SciPy, NumPy and SciKit-Learn on AWS EC2](http://neuralfoundry.com/jupyter-plotly-pandas-scipy-numpy-and-scikit-learn-on-aws-ec2/)
[Optional: 上文的补充--Running a Jupyter Notebook](http://neuralfoundry.com/jupyter-plotly-pandas-scipy-numpy-and-scikit-learn-on-aws-ec2/)
## 如何让任务挂在AWS上持续运行
[Info: 需要用到Screen命令](https://stackoverflow.com/questions/26245942/how-do-i-leave-node-js-server-on-ec2-running-forever)
[如何在AWS上使用Screen命令: To update all packages on an Amazon Linux instance](http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/install-updates.html) ，这篇只看中间的一部分就行了。
常用的Screen命令包括：
Screen -ls
screen -r 17793
[ec2-user ~]$ exit
[screen is terminating]
## Trouble Shooting
[pip broken, reinstall doesn't work. EC2](https://stackoverflow.com/questions/34734436/pip-broken-reinstall-doesnt-work-ec2)
上文针对的报错信息为：
```pkg_resources.DistributionNotFound: The 'pip==6.1.1' distribution was not found and is required by the application
[ec2-user@ip-172-31-17-194 ~]$ which pip
/usr/local/bin/pip
```
其他的完整教程包括：
使用amazon ec2搭建gpu+keras的环境
https://hackernoon.com/keras-with-gpu-on-amazon-ec2-a-step-by-step-instruction-4f90364e49ac
最后，我使用了bitfusion的AMI镜像，因为它基本上预装了所有可能会用到的工具，非常强大，check out教学视频：
https://www.youtube.com/watch?v=zSZDm5-ymy4
To check 一切GPU驱动安装完好：
nvidia-smi
tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcublas.so.8.0 locally
