That leaves us with the last task of authenticating to the server. Since we did not set up a LDAP server or OAuth, Jupyt
Jupyter Notebook provides a great platform to produce human-readable documents containing code, equations, analysis, and their descriptions. Some even consider it a powerful development when combining it with NBDev. For such an integral tool, the out of the box start up is not the best. Each use requires starting the Jupyter web application from the command line and entering your token or password. The entire web application relies on that terminal window being open. Some might “daemonize” the process and then use nohup to detach it from their terminal, but that’s not the most elegant and maintainable solution.
By the end of this post we will leverage the power of JupyterHub to access a Jupyter Notebook instance which can be accessed without a terminal, from multiple devices within your network, and a more user friendly authentication method.
services: # Configuration for Hub Proxy jupyterhub: build: . # Build the container from this folder. container_name: jupyterhub_hub # The service will use this container name. volumes: # Give access to Docker socket. - /var/run/docker.sock:/var/run/docker.sock - jupyterhub_data:/srv/jupyterlab environment: # Env variables passed to the Hub process. DOCKER_JUPYTER_IMAGE: jupyter/tensorflow-notebook DOCKER_NETWORK_NAME: ${COMPOSE_PROJECT_NAME}_default HUB_IP: jupyterhub_hub ports: - 8000:8000 restart: unless-stopped
JupyterHub brings the power of notebooks to groups of users. The idea behind JupyterHub was to scale out the use of Jupyter Notebooks to enterprises, classrooms, and large groups of users. Jupyter Notebook, however, is supposed to run as a local instance, on a single node, by a single developer. Unfortunately, there was no middle ground to have the usability and scalability of JupyterHub and the simplicity of running a local Jupyter Notebook. That is, until now.
The architecture of our JupyterHub server will consist of 2 services: JupyterHub and JupyterLab. JupyterHub will be the entry point and will spawn JupyterLab instances for any user. Each of these services will exist as a Docker container on the host.
I recommend doing this on the most powerful device you have and one that is turned on for most of the day, preferably all day. One of the benefits of this setup is that you will be able to use Jupyter Notebook from any device on your network, but have all the computation happen on the device we configure.
DOCKER_NETWORK_NAME is the name of the Docker network used by the services. This network gets an automatic name from Docker Compose, but the Hub needs to know this name to connect the Jupyter Notebook servers to it. To control the network name we use a little hack: we pass an environment variable COMPOSE_PROJECT_NAME to Docker Compose, and the network name is obtained by appending _default to it.
JupyterHub has pre-built Docker images that we can utilize to spawn a single notebook on a whim, with little to no overhead in technical complexity. We are going to use the combination of Docker and JupyterHub to access Jupyter Notebooks from anytime, anywhere, at the same URL.
Lucky for us, Jupyter has already come up with a solution to this problem by coming out with an extension of Jupyter Notebooks that runs as a sustainable web application and has built-in user authentication. To add a cherry on top, it can be managed and sustained through Docker allowing for isolated development environments.
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longer have a blank diagram. Each one of our nodes are pictured, and these are the “ingredients” for the architecture we want to build. The next steps will be to organize some of our nodes into logical groupings, and then link each of the nodes with edges.We have covered a great deal of the functions and methods for data analysis. There are, of course, a lot more offered by pandas but it is impossible to cover all in one article.
Since this is our home setup, we want to be able to stop idle instances to preserve memory on our machine. JupyterHub has services that can run along side it and one of them being jupyterhub-idle-culler. This service stops any instances that are idle for a prolonged duration.
The key environment variables to note are DOCKER_JUPYTER_IMAGE and DOCKER_NETWORK_NAME. JupyterHub will create Jupyter Notebooks with the images defined in the environment variable.For more information on selecting Jupyter images you can visit the following Jupyter documentation.
To access it on other devices on your network such asva laptop, an iPad, etc, identify the IP of the host machine by running ifconfig on Unix machines