Jupyter Notebook offers a command shell for interactive computing as a web application. The tool can be used with several languages, including Python, Julia, R, Haskell, and Ruby. It is often used for working with data, statistical modeling, and machine learning.
This tutorial will walk you through setting up Jupyter Notebook to run from a Debian 9 server, as well as teach you how to connect to and use the notebook. Jupyter notebooks (or simply notebooks) are documents produced by the Jupyter Notebook app which contain both computer code and rich text elements (paragraph, equations, figures, links, etc.) which aid in presenting and sharing reproducible research.
By the end of this guide, you will be able to run Python 3 code using Jupyter Notebook running on a remote server.
In order to complete this guide, you should have a fresh Debian 9 server instance with a basic firewall and a non-root user with sudo privileges configured. You can learn how to set this up by running through our Initial Server Setup with Debian 9 guide.
To begin the process, we’ll download and install all of the items we need from the Debian repositories. We will use the Python package manager
pip to install additional components a bit later.
We first need to update the local
apt package index and then download and install the packages:
- sudo apt update
pip and the Python header files, which are used by some of Jupyter’s dependencies:
- sudo apt install python3-pip python3-dev
Debian 9 (“Stretch”) comes preinstalled with Python 3.5.
We can now move on to setting up a Python virtual environment into which we’ll install Jupyter.
Now that we have Python 3, its header files, and
pip ready to go, we can create a Python virtual environment for easier management. We will install Jupyter into this virtual environment.
To do this, we first need access to the
virtualenv command. We can install this with
pip and install the package by typing:
- sudo -H pip3 install --upgrade pip
- sudo -H pip3 install virtualenv
virtualenv installed, we can start forming our environment. Create and move into a directory where we can keep our project files:
- mkdir ~/myprojectdir
- cd ~/myprojectdir
Within the project directory, create a Python virtual environment by typing:
- virtualenv myprojectenv
This will create a directory called
myprojectenv within your
myprojectdir directory. Inside, it will install a local version of Python and a local version of
pip. We can use this to install and configure an isolated Python environment for Jupyter.
Before we install Jupyter, we need to activate the virtual environment. You can do that by typing:
- source myprojectenv/bin/activate
Your prompt should change to indicate that you are now operating within a Python virtual environment. It will look something like this:
You’re now ready to install Jupyter into this virtual environment.
With your virtual environment active, install Jupyter with the local instance of
Note: When the virtual environment is activated (when your prompt has
(myprojectenv) preceding it), use
pip instead of
pip3, even if you are using Python 3. The virtual environment’s copy of the tool is always named
pip, regardless of the Python version.
- pip install jupyter
At this point, you’ve successfully installed all the software needed to run Jupyter. We can now start the notebook server.
You now have everything you need to run Jupyter Notebook! To run it, execute the following command:
- jupyter notebook
A log of the activities of the Jupyter Notebook will be printed to the terminal. When you run Jupyter Notebook, it runs on a specific port number. The first notebook you run will usually use port
8888. To check the specific port number Jupyter Notebook is running on, refer to the output of the command used to start it:
Output[I 21:23:21.198 NotebookApp] Writing notebook server cookie secret to /run/user/1001/jupyter/notebook_cookie_secret
[I 21:23:21.361 NotebookApp] Serving notebooks from local directory: /home/sammy/myprojectdir
[I 21:23:21.361 NotebookApp] The Jupyter Notebook is running at:
[I 21:23:21.361 NotebookApp] http://localhost:8888/?token=1fefa6ab49a498a3f37c959404f7baf16b9a2eda3eaa6d72
[I 21:23:21.361 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
[W 21:23:21.361 NotebookApp] No web browser found: could not locate runnable browser.
[C 21:23:21.361 NotebookApp]
Copy/paste this URL into your browser when you connect for the first time,
to login with a token:
If you are running Jupyter Notebook on a local Debian computer (not on a Droplet), you can simply navigate to the displayed URL to connect to Jupyter Notebook. If you are running Jupyter Notebook on a Droplet, you will need to connect to the server using SSH tunneling as outlined in the next section.
At this point, you can keep the SSH connection open and keep Jupyter Notebook running or can exit the app and re-run it once you set up SSH tunneling. Let’s keep it simple and stop the Jupyter Notebook process. We will run it again once we have SSH tunneling working. To stop the Jupyter Notebook process, press
Y, and hit
ENTER to confirm. The following will be displayed:
Output[C 21:28:28.512 NotebookApp] Shutdown confirmed
[I 21:28:28.512 NotebookApp] Shutting down 0 kernels
We’ll now set up an SSH tunnel so that we can access the notebook.
In this section we will learn how to connect to the Jupyter Notebook web interface using SSH tunneling. Since Jupyter Notebook will run on a specific port on the server (such as
:8889 etc.), SSH tunneling enables you to connect to the server’s port securely.
The next two subsections describe how to create an SSH tunnel from 1) a Mac or Linux and 2) Windows. Please refer to the subsection for your local computer.
If you are using a Mac or Linux, the steps for creating an SSH tunnel are similar to using SSH to log in to your remote server, except that there are additional parameters in the
ssh command. This subsection will outline the additional parameters needed in the
ssh command to tunnel successfully.
SSH tunneling can be done by running the following SSH command in a new local terminal window:
- ssh -L 8888:localhost:8888 your_server_username@your_server_ip
ssh command opens an SSH connection, but
-L specifies that the given port on the local (client) host is to be forwarded to the given host and port on the remote side (server). This means that whatever is running on the second port number (e.g.
8888) on the server will appear on the first port number (e.g.
8888) on your local computer.
Optionally change port
8888 to one of your choosing to avoid using a port already in use by another process.
server_username is your username (e.g. sammy) on the server which you created and
your_server_ip is the IP address of your server.
For example, for the username
sammy and the server address
203.0.113.0, the command would be:
- ssh -L 8888:localhost:8888 email@example.com
If no error shows up after running the
ssh -L command, you can move into your programming environment and run Jupyter Notebook:
- jupyter notebook
You’ll receive output with a URL. From a web browser on your local machine, open the Jupyter Notebook web interface with the URL that starts with
http://localhost:8888. Ensure that the token number is included, or enter the token number string when prompted at
If you are using Windows, you can create an SSH tunnel using Putty.
First, enter the server URL or IP address as the hostname as shown:
Next, click SSH on the bottom of the left pane to expand the menu, and then click Tunnels. Enter the local port number to use to access Jupyter on your local machine. Choose
8000 or greater to avoid ports used by other services, and set the destination as
:8888 is the number of the port that Jupyter Notebook is running on.
Now click the Add button, and the ports should appear in the Forwarded ports list:
Finally, click the Open button to connect to the server via SSH and tunnel the desired ports. Navigate to
http://localhost:8000 (or whatever port you chose) in a web browser to connect to Jupyter Notebook running on the server. Ensure that the token number is included, or enter the token number string when prompted at
This section goes over the basics of using Jupyter Notebook. If you don’t currently have Jupyter Notebook running, start it with the
jupyter notebook command.
You should now be connected to it using a web browser. Jupyter Notebook is very powerful and has many features. This section will outline a few of the basic features to get you started using the notebook. Jupyter Notebook will show all of the files and folders in the directory it is run from, so when you’re working on a project make sure to start it from the project directory.
To create a new notebook file, select New > Python 3 from the top right pull-down menu:
This will open a notebook. We can now run Python code in the cell or change the cell to markdown. For example, change the first cell to accept Markdown by clicking Cell > Cell Type > Markdown from the top navigation bar. We can now write notes using Markdown and even include equations written in LaTeX by putting them between the
$$ symbols. For example, type the following into the cell after changing it to markdown:
# Simple Equation
Let us now implement the following equation:
$$ y = x^2$$
where $x = 2$
To turn the markdown into rich text, press
CTRL+ENTER, and the following should be the results:
You can use the markdown cells to make notes and document your code. Let’s implement that simple equation and print the result. Click on the top cell, then press
ALT+ENTER to add a cell below it. Enter the following code in the new cell.
x = 2
y = x**2
To run the code, press
CTRL+ENTER. You’ll receive the following results:
You now have the ability to import modules and use the notebook as you would with any other Python development environment!
Congratulations! You should now be able to write reproducible Python code and notes in Markdown using Jupyter Notebook. To get a quick tour of Jupyter Notebook from within the interface, select Help > User Interface Tour from the top navigation menu to learn more.
From here, you may be interested to read our series on Time Series Visualization and Forecasting.
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