Hi.
In this video we'll look how multiple Jupiter users can collaborate
in real time on some notebook.
We have to start a new container on another port because, uh,
this collaborative option is set at the start of Jupyter Lab.
This is the command that we'll use. Notice the e parameter with this.
We set an environment variable called notebook arguments,
and we set the option collaborative. With this, uh, the Jupyter Lab,
I will start in a collaborative mode. Also, notice, uh,
we have to use another port. Let's run this command.
Now
you have to also change your path, uh, to the folder, which you are mapping,
and let's try it. Now
we have to change the port,
and also this is a new container.
That means we do not have the environment prepared. I will not do this,
uh, in this video, uh,
because I think you will get the idea on how the collaborative editing works.
Anyway,
we see here a new tab called users. This is our user,
and let's open our notebook In module five folder,
let's open, uh, the same instance on Chrome.
And now when we are trying to make some changes in cells or in the notebook,
we see in real time what the other users are doing.
Now, when I run the notebook, it'll throw an error because of the environment.
Uh, but I think you get the idea how this, uh, collaborative rating works.
Okay, that is it for this module. Uh, let's summarize it. We learned,
uh, how the Jupyter Lab works. Uh, we learned about its user interface,
about its workspaces, and we learned how to work with notebooks,
uh, the Python console, the lock console, and also the, the bugger.
We have learned how to manage extensions where we installed and used variable
inspector and the link extension.
We tried collaborative editing and we also learned the basics of binary
classification,
where we trained logistic regression and a random forest classifier model
to predict if the email is spam or not. In our next video,
we will start our next module where we will be focused on data
analytics using pandas.