参考教程：https://medium.com/codex/how-to-setup-jupyter-notebook-on-mac-b0c2e3c66e60
范例 #
There are many Jupyter images, depending on the variety of Python libraries installed and the additional setup of R and/or PySpark
use scipy-notebook, which contains most of the basic data science libraries that I need. It contains dask , pandas , numexpr , matplotlib , scipy , seaborn , scikit-learn , scikit-image , sympy , cython , patsy , statsmodel , cloudpickle , dill , numba , bokeh , sqlalchemy , hdf5 , vincent , beautifulsoup , protobuf , xlrd , bottleneck , and pytables packages.
# To download the image, start Docker and run the following command in your terminal:
docker image pull jupyter/scipy-notebook
# launch the container
docker container run --name jupyter -p 8888:8888 -v ~/Desktop/jupyter_notebook jupyter/scipy-notebook
docker container run speaks for itself. It creates a container from the image.
--name jupyter provides a name for our container. In this way, we can refer to it instead of a long technical name.
-p 8888:8888 indicates which ports will be used for communication between the container and your system. As Jupyter is a web-based application, you need to tell the system which port will be assigned to it.
-v ~/Desktop/jupyter_notebook defines which folder from your computer should be mounted to the container. Usually, this is a folder with project working files. For example, I have project files on my desktop and I would use ~/Desktop/jupyter_notebook.
To install additional packages, you could go into the container by running the following command in the terminal:
docker container exec -it jupyter bash
Extensions can be installed using the following command launched inside the docker container.
pip install jupyter_contrib_nbextensions && jupyter contrib nbextension install --user
After installation, you should stop and launch the container again to see a new tab with extensions in Jupyter.
常用方法： #
将 image 文件从仓库抓取到本地。
$ docker image pull library/hello-world
上面代码中，docker image pull是抓取 image 文件的命令。library/hello-world是 image 文件在仓库里面的位置，其中library是 image 文件所在的组，hello-world是 image 文件的名字。
查看image
$ docker images
REPOSITORY TAG IMAGE ID CREATED SIZE
jupyter/scipy-notebook latest c72931167ad2 29 hours ago 3.03GB
python 3.7.4 9fa56d0addae 3 years ago 918MB
$ docker image ls
REPOSITORY TAG IMAGE ID CREATED SIZE
jupyter/scipy-notebook latest c72931167ad2 29 hours ago 3.03GB
python
# 列出本机正在运行的容器
$ docker container ls
# 列出本机所有容器，包括终止运行的容器
$ docker container ls --all
终止运行的容器文件，依然会占据硬盘空间，可以使用docker container rm
命令删除。
$ docker container rm [containerID]/[containerName]
运行上面的命令之后，再使用docker container ls --all命令，就会发现被删除的容器文件已经消失了。
docker container run命令是新建容器，每运行一次，就会新建一个容器。同样的命令运行两次，就会生成两个一模一样的容器文件。如果希望重复使用容器，就要使用docker container start命令，它用来启动已经生成、已经停止运行的容器文件。
docker container kill命令终止容器运行，相当于向容器里面的主进程发出 SIGKILL 信号。而docker container stop命令也是用来终止容器运行，相当于向容器里面的主进程发出 SIGTERM 信号，然后过一段时间再发出 SIGKILL 信号。
$ docker container stop [containerID]/[containerName]
docker container logs命令用来查看 docker 容器的输出，即容器里面 Shell 的标准输出。如果docker run命令运行容器的时候，没有使用-it参数，就要用这个命令查看输出。
$ docker container logs [containerID]
docker container exec命令用于进入一个正在运行的 docker 容器。如果docker run命令运行容器的时候，没有使用-it参数，就要用这个命令进入容器。一旦进入了容器，就可以在容器的 Shell 执行命令了。
$ docker container exec -it [containerID] /bin/bash
docker container cp命令用于从正在运行的 Docker 容器里面，将文件拷贝到本机。下面是拷贝到当前目录的写法。
$ docker container cp [containID]:[/path/to/file] .
简单流程 #
docker container ls --all
docker container start jupyter
docker container logs jupyter
docker container exec -it jupyter bash
docker container stop jupyter
# maybe used
docker container cp ~/Desktop/joyful-pandas-master/data 89fcf2f6530c:/home/jovyan/
最后一次修改于 2022-12-06