IPython is an interactive command-line interface to Python. Jupyter Notebook offers an interactive web interface to many languages, including IPython.
This article will walk you through setting up a server to run Jupyter Notebook 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 (e.g. Python) and rich text elements (paragraph, equations, figures, links, etc.) which aid in presenting reproducible research.
By the end of this guide, you will be able to run Python 2.7 code using Ipython and Jupyter Notebook running on a remote server. For the purposes of this tutorial, Python 2 (2.7.x) is used since many of the data science, scientific computing, and high-performance computing libraries support 2.7 and not 3.0+.
To follow this tutorial, you will need the following:
All the commands in this tutorial should be run as a non-root user. If root access is required for the command, it will be preceded by
sudo. Initial Server Setup with Ubuntu 16.04 explains how to add users and give them sudo access.
In this section we will install Python 2.7 and Pip.
First, update the system’s package index. This will ensure that old or outdated packages do not interfere with the installation.
- sudo apt-get update
Next, install Python 2.7, Python Pip, and Python Development:
- sudo apt-get -y install python2.7 python-pip python-dev
python2.7 will update to the latest version of Python 2.7, and
python-pip will install Pip which allows us to manage Python packages we would like to use. Some of Jupyter’s dependencies may require compilation, in which case you would need the ability to compile Python C-extensions, so we are installing
python-dev as well.
To verify that you have python installed:
- python --version
This will output:
Depending on the latest version of Python 2.7, the output might be different.
You can also check if pip is installed using the following command:
- pip --version
You should something similar to the following:
Outputpip 8.1.1 from /usr/lib/python2.7/dist-packages (python 2.7)
Similarly depending on your version of pip, the output might be slightly different.
In this section we will install Ipython and Jupyter Notebook.
First, install Ipython:
- sudo apt-get -y install ipython ipython-notebook
Now we can move on to installing Jupyter Notebook:
- sudo -H pip install jupyter
Depending on what version of pip is in the Ubuntu apt-get repository, you might get the following error when trying to install Jupyter:
OutputYou are using pip version 8.1.1, however version 8.1.2 is available. You should consider upgrading via the 'pip install --upgrade pip' command.
If so, you can use pip to upgrade pip to the latest version:
- sudo -H pip install --upgrade pip
Upgrade pip, and then try installing Jupyter again:
- sudo -H pip install jupyter
You now have everything you need to run Jupyter Notebook! To run it, execute the following command:
- jupyter notebook
To ignore the error, you can press
Q and then press
Y to confirm.
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 are running will usually run on 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 NotebookApp] Serving notebooks from local directory: /home/sammy [I NotebookApp] 0 active kernels [I NotebookApp] The Jupyter Notebook is running at: http://localhost:8888/ [I NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
If you are running Jupyter Notebook on a local Linux computer (not on a Droplet), you can simply navigate to
localhost:8888 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 12:32:23.792 NotebookApp] Shutdown confirmed [I 12:32:23.794 NotebookApp] Shutting down kernels
In this section we will learn how to connect to the Jupyter Notebook web interface using SSH tunneling. Since Jupyter Notebook is running on a specific port on the Droplet (such as
:8889 etc.), SSH tunneling enables you to connect to the Droplet’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 the How To Use SSH Keys with DigitalOcean Droplets using Linux or Mac guide except there are additional parameters added 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:
- ssh -L 8000: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 (Droplet). This means that whatever is running on the second port number (i.e.
8888) on the Droplet will appear on the first port number (i.e.
8000) on your local computer. You should change
8888 to the port which Jupyter Notebook is running on. Optionally change port
8000 to one of your choosing (for example, if
8000 is used by another process). Use a port greater or equal to
8002, etc.) to avoid using a port already in use by another process.
server_username is your username (i.e. sammy) on the Droplet which you created and
your_server_ip is the IP address of your Droplet. For example, for the username
sammy and the server address
184.108.40.206, the command would be:
- ssh -L 8000:localhost:8888 firstname.lastname@example.org
If no error shows up after running the
ssh -L command, you can run Jupyter Notebook:
- jupyter notebook
Now, from a web browser on your local machine, open the Jupyter Notebook web interface with
http://localhost:8000 (or whatever port number you chose).
If you are using Windows, you can also easily create an SSH tunnel using Putty as outlined in How To Use SSH Keys with PuTTY on DigitalOcean Droplets (Windows users).
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 (ie
8002, etc.) 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.
This section goes over the basics of using Jupyter Notebook. By this point you should have Jupyter Notebook running, and you should 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. Automatically, Jupyter Notebook will show all of the files and folders in the directory it is run from.
To create a new notebook file, select New > Python 2 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. Select Insert > Insert Cell Below to insert and cell and enter the following code:
x = 2 y = x*x print y
To run the code, press
CTRL+ENTER. The following should be the results:
You now have the ability to include libraries and use the notebook as you would with any other Python development environment!
Congratulations! You should be now able to write reproducible Python code and notes using markdown using Jupyter notebook running on a Droplet. To get a quick tour of Jupyter notebook, select Help > User Interface Tour from the top navigation menu.
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