Update: Read my review of the Jupyter Notebook ecosystem in 2020 in this link

In my research, I usually work with remote servers to run deep learning models inside machines more powerful than my laptop. In some cases, I find myself needing to fire up a Jupyter notebook to draw quick-and-easy visualizations and short analyses. Here, I’ll show you how to run a Jupyter notebook from a remote server.

The main idea is that you will run a “headless” notebook from your server, but have the graphical interface (GUI) show up in your local machine’s web browser. The libraries, hardware, and all backend-related stuff depends on your remote machine, but the GUI is seen from your laptop.

Set-up: Here, let’s define the local user and host as localuser and localhost respectively. Similarly, let’s define the remote user and remote host as remoteuser and remotehost. Needless to say, make sure that Jupyter notebook and all its dependencies are installed in both machines. Here’s a quick diagram of the whole process, I will discuss them one-by-one in the next section:

Figure: A simple three-step process for running a remote Jupyter notebook.

Step 1: Run Jupyter Notebook from remote machine

Log-in to your remote machine the usual way you do. In most cases, this is simply done via an ssh command. Once the console shows, type the following:

remoteuser@remotehost: jupyter notebook --no-browser --port=XXXX

# Note: Change XXXX to the port of your choice. Usually, the default is 8888. 
# You can try 8889 or 8890 as well.
  • jupyter notebook: simply fires up your notebook
  • --no-browser: this starts the notebook without opening a browser
  • --port=XXXX: this sets the port for starting your notebook where the default is 8888. When it’s occupied, it finds the next available port.

Step 2: Forward port XXXX to YYYY and listen to it

In your remote, the notebook is now running at the port XXXX that you specified. What you’ll do next is forward this to port YYYY of your machine so that you can listen and run it from your browser. To achieve this, we write the following command:

localuser@localhost: ssh -N -f -L localhost:YYYY:localhost:XXXX remoteuser@remotehost
  • ssh: your handy ssh command. See man page for more info
  • -N: suppresses the execution of a remote command. Pretty much used in port forwarding.
  • -f: this requests the ssh command to go to background before execution.
  • -L: this argument requires an input in the form of local_socket:remote_socket. Here, we’re specifying our port as YYYY which will be binded to the port XXXX from your remote connection.

Step 3: Fire-up Jupyter Notebook

To open up the Jupyter notebook from your remote machine, simply start your browser and type the following in your address bar:


Again, the reason why we’re opening it at YYYY and not at XXXX is because the latter is already being forwarded to the former. XXXX and YYYY can be the “same” number (not the same port, technically) because they are from different machines.

If you’re successful, you should see the typical Jupyter Notebook home screen in the directory where you ran the command in Step 1. At the same time, if you look in your remote terminal, you should see some log actions happening as you perform some tasks.

In your first connection, you may be prompted to enter an Access Token as typical to most Jupyter notebooks. Normally, I’d just copy-paste it from my terminal, but to make things easier for you, you can set-up your own notebook password.

Closing all connections

To close connections, I usually stop my notebook from remote via CTRL + C then Y, and kill the process on YYYY via:

localuser@localhost: sudo netstat -lpn |grep :YYYY

# This will show the process ID (PID), e.g. ABCDEF of the one running in YYYY,
# you can kill it by simply typing

localuser@localhost: kill ABCDEF

Making life easy

Because I often perform these commands in my daily work, I simply created some functions where I only need to supply the port during connection. In my configuration file (this can be your .bashrc), I have the following commands:

Helper function to fire-up a Jupyter notebook in remote

function jpt(){
    # Fires-up a Jupyter notebook by supplying a specific port
    jupyter notebook --no-browser --port=$1

And thus I only need to write:

remoteuser@remotehost: jpt 8889

Helper function to listen to a port

function jptt(){
    # Forwards port $1 into port $2 and listens to it
    ssh -N -f -L localhost:$2:localhost:$1 remoteuser@remotehost

And so:

localuser@localhost: jptt 8889 8888

For me, this reads as “jupyter tunnel”, and the whole command is like an action “tunnel 8889 (the port from my remote) to 8888 (port from my local machine)”.

Using a config file

Update: May 31, 2021 Thanks to @a-berg, here’s a more hassle-free way of tunneling notebooks. This one involves updating your SSH config file. You can usually find it in $HOME/.ssh/config.

Open your config file, and add the following section

Host notebooks
    HostName host
    User myuser
    LocalForward YYYY localhost:XXXX


  • notebooks: an arbitrary “label” for the connection you’ll make
  • HostName: the username of the host that will connect
  • User: the identity of the user that will connect
  • LocalForward: specifies a connection that will forward the traffic to the remote machine

Given that, it’s now possible for us to connect as myuser@host in our remote server by just typing this command:

localuser@localhost: ssh -Nf notebooks

As @a-berg mentions, you can even add an IdentityFile to properly secure your connection! Awesome! To learn more about SSH configuration files, I’ll direct you to this wonderful tutorial from DigitalOcean.

And that’s basically it! The Jupyter notebook is really a wonderful tool for literal programming where you can intersperse text and code in one document. In my use-case, I don’t have the Tensorflow GPU library in my local machine so I need to run notebooks on-top of our laboratory’s server. This enabled me to decrease development time, and helped me perform fast analyses of my models.

Hi there, I’ve been using notebooks for a long time now and it may be good to give a review of the notebook ecosystem for 2020. You can read it in this link.

If you like this, you’ll enjoy:


  • 05-31-2021: Add section for using SSH config files (thanks to @a-berg!)
  • 03-06-2020: Add link for Jupyter Notebooks review
  • 09-29-2018: Update figure using Tikz for consistency
  • 09-11-2018: Add diagram to Jupyter notebook tutorial