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What this setup does
A VPS is a remote Linux computer with a public network address. JupyterLab runs on that computer, but the web server can listen only on its loopback interface, 127.0.0.1. That address is reachable from the VPS itself, not directly from the internet.
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An SSH tunnel makes a port on your own computer act as a secure entrance to the VPS-side port. Your browser connects to a local URL such as http://127.0.0.1:8888; SSH carries the requests through the encrypted session to Jupyter on the VPS. You do not need to open TCP port 8888 in the VPS firewall for this arrangement.
This is a single-user pattern. The Jupyter token or password remains important even though the service is bound to localhost: anyone who gains access to your VPS account or to the forwarded local port could otherwise reach the web application.
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Prerequisites and planning
What you need
- A Linux VPS that you can reach with SSH.
- A non-root VPS account with permission to install packages and run your project.
- An SSH client on your local computer. OpenSSH is available by default on most Linux and macOS systems; current Windows versions also provide
sshin PowerShell and Windows Terminal. - A Python environment for the project, created with conda, mamba, or a standard virtual environment.
- JupyterLab installed inside that environment.
- A local browser and a second local terminal, because one terminal will keep the tunnel open.
Choose ports deliberately
8888 is a common example and Jupyter’s familiar default, not a required or universal port. The remote port must be the port on which Jupyter is listening. The local port only needs to be free on your computer; it can be different from the remote port.
| Component | Example | Meaning |
|---|---|---|
| VPS bind address | 127.0.0.1 |
Jupyter accepts connections from the VPS loopback interface. |
| VPS Jupyter port | 8888 |
The port used by the Jupyter process on the VPS. |
| Local browser port | 8888 or 8890 |
The port your browser uses after SSH creates the tunnel. |
1. Log in and prepare a non-root account
Connect to the VPS from your local terminal using the account supplied by your VPS setup:
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ssh USER@VPS_HOSTUse a dedicated account for notebooks rather than running the server as root. A project-specific account limits the damage from an application mistake and keeps files, environments, and credentials separate from system administration.
Once logged in, confirm that Python is available and decide where the project will live. A typical project directory might be ~/projects/my-notebook. Keep data and notebooks in a location that is included in your backup or snapshot plan; a running Jupyter process is not a backup.
2. Create or activate the Python environment
Install Jupyter in the same environment that contains your project libraries. Otherwise, the server may start successfully while notebook kernels cannot import the packages your code needs.
Conda or mamba
Activate an existing environment, or create one with the Python version your project requires:
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Python virtual environment and pip
From the project directory, create and activate a virtual environment:
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python3 -m venv .venv
source .venv/bin/activateInstall JupyterLab with pip:
python -m pip install --upgrade pip
python -m pip install jupyterlabProject Jupyter documents conda, mamba, and pip as installation routes. Whichever route you use, verify that the executable resolves inside the activated environment:
which jupyter
jupyter lab --versionIf which jupyter points to a system location instead of your environment, activate the environment again and install JupyterLab there. Do not mix a system Jupyter executable with kernels and libraries installed in a different environment unless you have intentionally configured that arrangement.
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3. Start Jupyter on the VPS without opening a browser
A VPS normally has no graphical desktop, so tell Jupyter not to launch a browser. Bind it to loopback and choose a free port:
jupyter lab --no-browser --ip=127.0.0.1 --port=8888The process prints a URL containing an authentication token. Leave this terminal running. Copy the token-bearing URL privately, or copy only the token and use the local URL after the tunnel is active.
If port 8888 is already occupied, select another remote port:
jupyter lab --no-browser --ip=127.0.0.1 --port=8891The server options control three important behaviors here:
--no-browserprevents a browser-launch attempt on the headless VPS.--ip=127.0.0.1keeps the listener on the VPS loopback interface instead of a public network interface.--port=...selects the TCP port Jupyter listens on.
Jupyter Server generates a token by default. Do not configure an empty token merely to avoid pasting credentials: setting the token to an empty string disables authentication altogether and is explicitly not recommended. Keep the printed URL and token out of public issue trackers, screenshots, shell transcripts, and shared chats.
4. Create the SSH tunnel from your computer
Open a second terminal on your local computer; do not run this command in the VPS terminal that is hosting Jupyter. Forward a local port to the VPS loopback address and the Jupyter port:
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ssh -N -L 8888:127.0.0.1:8888 USER@VPS_HOSTOpenSSH’s -L option creates a local listening port and forwards traffic through the SSH connection to the specified host and port on the remote side. The explicit 127.0.0.1 target means the SSH server connects to Jupyter through the VPS loopback interface. The -N option tells SSH not to run a remote shell command; this terminal is dedicated to forwarding.
When local port 8888 is busy
Keep the VPS-side port at 8888 and choose another free port on your computer:
ssh -N -L 8890:127.0.0.1:8888 USER@VPS_HOSTThe first number is the local listening port. The final number is the remote Jupyter port. Browse to http://127.0.0.1:8890 in this example.
When Jupyter uses a different remote port
If Jupyter was started on 8891, map the remote side to 8891:
ssh -N -L 8888:127.0.0.1:8891 USER@VPS_HOSTYou can also use a different local port at the same time:
ssh -N -L 8890:127.0.0.1:8891 USER@VPS_HOST| SSH segment | Example value | Role |
|---|---|---|
| Local listen port | 8890 |
Port opened on your computer for the browser. |
| Forward target address | 127.0.0.1 |
Address resolved from the VPS side of the SSH connection. |
| Remote target port | 8888 |
Port where Jupyter is listening on the VPS. |
5. Open Jupyter in the browser
With both the Jupyter process and SSH tunnel running, open the local address that matches the first number in your -L option:
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http://127.0.0.1:8888when the command starts with-L 8888:.... - Use
http://127.0.0.1:8890when the command starts with-L 8890:....
Paste the token from the VPS terminal when Jupyter asks for it. If you copied the complete token-bearing URL, use that URL only on your own computer and verify that its host and port correspond to the local tunnel.
Keep the browser host as 127.0.0.1. Substituting the VPS public hostname can trigger host-header checks or a 403 response because the request is no longer addressed as a local connection.
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6. Keep the server and tunnel available
Manual sessions
The SSH tunnel exists only while its SSH process is connected. Likewise, Jupyter stops when the terminal running it receives Ctrl-C. For a short session, use this simple sequence:
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- Start the SSH tunnel in a second local terminal.
- Work in the browser.
- Press Ctrl-C in the local tunnel terminal when finished.
- Press Ctrl-C in the VPS Jupyter terminal to stop the server.
Long-running work
If a notebook job must survive closing your terminal, run Jupyter inside a terminal multiplexer such as tmux and keep the SSH tunnel separate. A typical pattern is:
tmux new -s jupyter
jupyter lab --no-browser --ip=127.0.0.1 --port=8888Detach from the multiplexer with its detach keystroke, then reconnect later with:
tmux attach -t jupyterThis keeps the process alive across an SSH disconnect, but it does not automatically restart Jupyter after a VPS reboot. For reboot persistence, use a service manager such as systemd and configure it to run as the dedicated user, from the project directory, with the intended environment and loopback bind. Treat the service definition as infrastructure: restrict its file permissions, decide how the token is supplied, and test both startup and shutdown before relying on it.
Security boundaries you should preserve
Local binding is not the same as authentication
Loopback binding prevents unsolicited internet connections to the notebook port, but it does not replace Jupyter’s token or password. Leave authentication enabled and protect the VPS account and SSH key as carefully as the notebook data.
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Changing --ip=127.0.0.1 to --ip=0.0.0.0 or a public address exposes Jupyter to interfaces beyond the VPS itself. That changes the firewall, TLS, and access-control requirements. A public deployment should use HTTPS and authentication, with deliberate firewall rules and a documented way to rotate credentials.
Use least privilege
- Run Jupyter as the project user, not root, unless a specific deployment requirement has been reviewed.
- Keep private keys, tokens, environment files, and notebook data outside publicly served directories.
- Use VPS firewall rules to limit SSH access where practical, while leaving the Jupyter port closed to the public internet for an SSH-only workflow.
- Rotate a token or password if it appears in a shared log, screenshot, ticket, or chat.
Know when this pattern is insufficient
This design suits one person or a tightly controlled workstation. It is not a multi-user access-control system. Teams that need separate users, centrally managed authentication, shared kernels, or policy-based access should evaluate a multi-user architecture rather than sharing one personal Jupyter process.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.SSH tunnel versus other deployment choices
The right option depends on who needs access and how much infrastructure you are prepared to operate.
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| Option | Exposure | Authentication | Persistence | Audience | Operational burden |
|---|---|---|---|---|---|
| Loopback plus SSH tunnel | Jupyter port stays private; access is through SSH. | SSH access plus Jupyter token or password. | Manual process, multiplexer, or a service manager. | Usually one user. | Low once SSH access works; each user needs a tunnel. |
| Public Jupyter behind HTTPS | Reachable through a public hostname and firewall rule. | Token, password, or an additional access layer. | Normally managed as a service. | Selected remote users. | Higher: TLS certificates, firewalling, updates, logging, and access control. |
| Multi-user server architecture | Published through a designed gateway or proxy. | Per-user identity, often with centralized authentication. | Managed services and user spawning. | Teams or classes. | Highest: administration, upgrades, resource limits, and policy. |
A direct public deployment can be simpler for a controlled team in some environments, but it carries responsibilities that the tunnel avoids. For an individual using a VPS from a trusted computer, the loopback-and-tunnel pattern minimizes exposed surface area without requiring a public notebook endpoint.
VPS sizing and location decisions
There is no single VPS size for Jupyter. Select resources from the workload rather than from the port number:
- CPU: matters for compilation, data transformation, simulations, and parallel kernels.
- RAM: determines how large a dataset and how many kernels can remain in memory before swapping.
- Storage: must cover the operating system, environments, package caches, datasets, notebook outputs, and snapshots.
- Region: a nearby region usually improves interactive terminal and browser responsiveness; data residency requirements may dictate another region.
- Bandwidth: large dataset transfers and rendered outputs consume more bandwidth than ordinary notebook interaction.
- Snapshots and backups: useful for recovery, but they do not replace exporting important notebooks and data.
- IPv6 and support: can affect how you reach the VPS and troubleshoot networking, even though the SSH tunnel itself does not require Jupyter to be publicly reachable.
Start with the smallest instance that comfortably fits the environment and dataset, then watch memory, CPU, disk, and network usage during real jobs. Upgrade based on observed pressure rather than treating 8888 as a capacity indicator.
Troubleshooting by symptom
The browser refuses the connection
- Confirm that the Jupyter process is still running in the VPS terminal.
- Check that the browser port equals the first number in the SSH
-Largument. - Confirm that the remote port equals the port shown in Jupyter’s startup output.
- Make sure the SSH terminal has not exited or been suspended.
SSH reports that the local port is busy
Another local application owns that port. Choose a free local port, such as 8890, while leaving the remote target unchanged, then browse to the new local port.
Jupyter printed a different port
Either stop the conflicting process and restart Jupyter on your chosen port, or change the final number in the tunnel to the actual remote port. The local browser port and remote Jupyter port do not have to match.
The page returns 403 or a host-header error
Use http://127.0.0.1:local-port in the browser rather than the VPS hostname. Review Jupyter’s allow_remote_access setting only when a reverse proxy or an intentionally non-local hostname is part of the design; it is not a reason to weaken a straightforward localhost tunnel.
The tunnel connects but the page hangs
- Verify that Jupyter is listening on the VPS loopback address and the port you forwarded.
- Check that the SSH account can reach that address and port from the VPS.
- Run the tunnel with verbose logging to inspect connection details:
ssh -v -N -L 8888:127.0.0.1:8888 USER@VPS_HOSTUse the resulting log to distinguish an SSH forwarding problem from a Jupyter process or VPS resource problem; do not assume a particular fix without examining the error.
The session disconnects unexpectedly
Reconnect the tunnel and check whether the VPS process survived. Inspect SSH verbose output, VPS memory and disk pressure, and any service-manager logs if Jupyter is managed as a service. A terminal multiplexer can protect a manual process from an ordinary SSH disconnect, but it cannot correct a VPS reboot, an out-of-memory kill, or a full disk.
Safe shutdown and repeatable startup
For a manual run, stop the local tunnel first with Ctrl-C, then stop Jupyter with Ctrl-C in the VPS session. On the next run, activate the same project environment, launch Jupyter on the intended loopback port, and recreate the SSH forward with a local port that is free.
Write down the environment name, project directory, remote port, local port, and startup command in a private operations note. Do not store the live token in that note. This small record prevents the most common repeat-run errors: activating the wrong environment, forwarding the wrong remote port, or opening the wrong local browser address.
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