基于docker安装tensorflow的完整步骤

$ sudo apt-get remove docker   docker-engine   docker.io

$ sudo apt-get update
$ sudo apt-get install  linux-image-extra-$(uname -r)  linux-image-extra-virtual

$ sudo apt-get update
$ sudo apt-get install  apt-transport-https  ca-certificates  curl  software-properties-common

$ curl -fsSL https://mirrors.ustc.edu.cn/docker-ce/linux/ubuntu/gpg | sudo apt-key add -
# 官方源
# $ curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -

$ sudo add-apt-repository  "deb [arch=amd64] https://mirrors.ustc.edu.cn/docker-ce/linux/ubuntu  $(lsb_release -cs)  stable"
# 官方源
# $ sudo add-apt-repository # "deb [arch=amd64] https://download.docker.com/linux/ubuntu # $(lsb_release -cs) # stable"

$ sudo apt-get update
$ sudo apt-get install docker-ce

$ sudo groupadd docker

$ sudo usermod -aG docker $USER

$ sudo service docker start

docker run hello-world
Unable to find image 'hello-world:latest' locally
latest: Pulling from library/hello-world
ca4f61b1923c: Pull complete 
Digest: sha256:083de497cff944f969d8499ab94f07134c50bcf5e6b9559b27182d3fa80ce3f7
Status: Downloaded newer image for hello-world:latest

Hello from Docker!
This message shows that your installation appears to be working correctly.

To generate this message, Docker took the following steps:
 1. The Docker client contacted the Docker daemon.
 2. The Docker daemon pulled the "hello-world" image from the Docker Hub.
 (amd64)
 3. The Docker daemon created a new container from that image which runs the
 executable that produces the output you are currently reading.
 4. The Docker daemon streamed that output to the Docker client, which sent it
 to your terminal.

To try something more ambitious, you can run an Ubuntu container with:
 $ docker run -it ubuntu bash

Share images, automate workflows, and more with a free Docker ID:
 https://cloud.docker.com/

For more examples and ideas, visit:
 https://docs.docker.com/engine/userguide/

docker pull tensorflow/tensorflow

Status: Downloaded newer image for tensorflow/tensorflow:latest

docker run --name my-tensorflow -it -p 8888:8888 -v ~/tensorflow:/test/data tensorflow/tensorflow

[I 15:08:31.949 NotebookApp] Writing notebook server cookie secret to /root/.local/share/jupyter/runtime/notebook_cookie_secret
[W 15:08:31.970 NotebookApp] WARNING: The notebook server is listening on all IP addresses and not using encryption. This is not recommended.
[I 15:08:31.975 NotebookApp] Serving notebooks from local directory: /notebooks
[I 15:08:31.975 NotebookApp] 0 active kernels
[I 15:08:31.975 NotebookApp] The Jupyter Notebook is running at:
[I 15:08:31.975 NotebookApp] http://[all ip addresses on your system]:8888/"htmlcode">

http://[all ip addresses on your system]:8888/"text-align: center">

显示Jupyter Notebook，Jupyter Notebook（此前被称为 IPython notebook）是一个交互式笔记本。示例中已经显示了Tensorflow的入门教程，点开一个可以看见

如上面这个例子，是使用tensorflow来使两个array相加，我们点击run，就可以看到运行的结果了。


关闭容器




docker stop my-tensortflow


再次打开




docker start my-tensortflow


如果不喜欢用Jupyter Notebook，我们也可以创建基于命令行的容器


基于命令行的容器




docker run -it --name bash_tensorflow tensorflow/tensorflow /bin/bash


这样我们就创建了名为bash_tensorflow的容器


还是用start命令启动容器：




docker start bash_tensorflow


再连接上容器：




docker attach bash_tensorflow


可以看到我们用终端连接上了容器，和操作Linux一样了。
这个镜像默认没有装vim，所以自己又下载了vim来写代码。


至此，安装过程结束。
总结
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