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Marketplace

Pre-Installed JupyterLab

Get JupyterLab pre-installed and ready to go from the Database Mart Marketplace—just deploy and use, no setup hassle.

Overview

JupyterLab is a next-generation, open-source web-based interactive development environment (IDE) designed for data science, scientific computing, and machine learning. It provides a flexible and extensible interface where you can work with Jupyter notebooks, text editors, terminals, data files, and custom components side-by-side in a single, unified workspace. Supporting over 40 programming languages including Python, R, Julia, and Scala, JupyterLab enables you to combine live code, visualizations, narrative text, and rich media outputs to explore data, prototype algorithms, and share reproducible research.

Select a pre-installed JupyterLab Marketplace image and get a fully configured server that's ready to use immediately—no manual installation or complex setup required.

Supported Operating Systems

The JupyterLab Marketplace image is available for Ubuntu Server 22 LTS 64-bit and Ubuntu Server 24 LTS 64-bit.

Supported Products / Plans

The JupyterLab Marketplace image is currently available across the following plans:

Basic Linux VPS
Basic Plus Linux VPS
Professional Linux VPS
Professional Plus Linux VPS
Advanced Linux VPS
Advanced Plus Linux VPS
Express GPU VPS - 2GB
Basic GPU VPS - RTX 5060
Professional GPU VPS - RTX Pro 2000
Professional GPU VPS - RTX A4000
Advanced GPU VPS - RTX Pro 4000
Advanced GPU VPS - RTX Pro 5000
Advanced GPU VPS - RTX 5090
Enterprise GPU VPS - RTX Pro 6000

View compatible plans →

JupyterLab Version

Database Mart provides the v4.5.4 version of JupyterLab, pre-installed and ready to use.

What's Installed

When you select the JupyterLab Marketplace image, Database Mart automatically installs and configures:

Core Operating Environment

Operating System: Ubuntu Server 22.04 LTS or 24.04 LTS, pre-optimized for data science and development workloads
GPU Drivers & Acceleration: NVIDIA drivers, CUDA 12.x, and cuDNN pre-installed for GPU acceleration (on GPU-enabled plans)
Python Environment: Python 3.10+ with a dedicated virtual environment for dependency management

JupyterLab Core Components

JupyterLab v4.5.4: The web-based interactive development environment with multi-tab interface, file browser, and extensible plugin system
Jupyter Notebook Server: The backend server that manages notebook kernels, sessions, and file operations
IPython Kernel: The default Python kernel for executing code interactively
Core Extensions: Pre-installed essential extensions including the LSP (Language Server Protocol) for code autocompletion, Git integration for version control, and other productivity tools

Programming Languages & Libraries

Multi-Language Kernel Support: Pre-configured to support Python, R, Julia, and Scala, with the ability to add additional kernels as needed
Pre-installed Python Libraries: Common data science and ML libraries including NumPy, Pandas, Matplotlib, scikit-learn, TensorFlow, PyTorch, and more

Hardware & GPU Support

GPU-Accelerated Libraries: For GPU-enabled plans, CUDA libraries and cuPy are pre-installed for GPU-accelerated data processing and model training
JupyterLab GPU Dashboard: Optional performance monitoring tools for GPU usage

Installation Process

During server provisioning, Database Mart automatically performs the following steps:

Deploys the selected Linux operating system (Ubuntu Server 22.04 LTS or 24.04 LTS).
Installs JupyterLab v4.5.4 using pip or conda package manager.
Installs and configures all required components, including:
- Python 3.10+ with pip, virtual environment, and essential build tools
- JupyterLab core and all dependencies (Jupyter Notebook Server, IPython, and core extensions)
- Pre-configured configuration files (jupyter_lab_config.py) with optimized settings for remote access, port configuration, and security
- System dependencies (nodejs, npm, build-essential) for JupyterLab extension support
- Common data science libraries (NumPy, Pandas, Matplotlib, scikit-learn, TensorFlow, PyTorch)
- CUDA and cuDNN for GPU acceleration (on GPU-enabled plans)
- Pre-configured SSL/TLS certificates for secure browser connections

Everything is fully automated—your JupyterLab server is ready to use as soon as deployment is complete.

Getting Started After Deployment

After your server has been provisioned, follow these steps to begin using JupyterLab.

Step 1: Access the Server

Once deployment is complete, connect to your server via SSH using the credentials provided by Database Mart:

ssh administrator@your-server-ip

JupyterLab is pre-installed in a dedicated environment (typically using a Python virtual environment in /opt/jupyterlab or similar location).

Step 2: Start JupyterLab

JupyterLab can be started directly from the terminal:

# Activate the pre-configured environment
cd /opt/jupyterlab
source bin/activate

# Start JupyterLab
jupyter lab --ip=0.0.0.0 --port=8888 --no-browser

For production or persistent setups, JupyterLab can be configured as a system service to start automatically:

# Create a systemd service
sudo nano /etc/systemd/system/jupyterlab.service

Add the following configuration:

[Unit]
Description=JupyterLab
After=network.target

[Service]
Type=simple
User=administrator
WorkingDirectory=/home/administrator
ExecStart=/opt/jupyterlab/bin/jupyter-lab --ip=0.0.0.0 --port=8888 --no-browser
Restart=always

[Install]
WantedBy=multi-user.target

Then enable and start the service:

sudo systemctl enable jupyterlab
sudo systemctl start jupyterlab

Step 3: Access the Web Interface

Once JupyterLab is running, you can access it through your browser using the server's IP address and port:

http://your-server-ip:8888/lab

When accessing JupyterLab for the first time, you may be prompted for a token. Retrieve it using:

jupyter lab list

The output will display the URL with the token included, e.g.:

http://localhost:8888/lab?token=abc123...

For remote access, the pre-configured setup allows connections from any IP, but ensure your firewall settings (like UFW or security groups) allow port 8888.

Step 4: Use JupyterLab for Data Science and Development

Once logged in, you can:

Create new notebooks: Click the "Python 3" button under "Notebook" in the launcher to start coding
Open existing notebooks: Navigate to your project directory and open existing .ipynb files
Install packages: Use !pip install <package> in a notebook cell to install additional Python libraries
Use multiple kernels: Switch between Python, R, Julia, or Scala kernels from the kernel menu
Set up a dedicated project directory: Use jupyter lab --notebook-dir=/path/to/project to focus on a specific folder

Notes

SSL/TLS Configuration

For production environments, you can enable SSL/TLS to encrypt traffic and improve security. The pre-installed image includes self-signed certificates for testing, but for production use, consider obtaining certificates from a trusted CA.

Storage Recommendations

JupyterLab stores notebooks and data in the user's home directory by default. For larger datasets or persistent storage across deployments, consider using:

Bind mounts to map host directories into the JupyterLab workspace
Separate data disks for production workloads

Security Considerations

By default, JupyterLab uses token-based authentication for security
For production use, consider setting a password with the Jupyter server password to replace token authentication
Restrict access using firewall rules to allow only trusted IP addresses
Consider using a reverse proxy (like Nginx) with SSL termination for additional security

Extension Management

JupyterLab supports a wide range of extensions for enhanced functionality. Pre-installed extensions include:

jupyterlab-lsp: Language Server Protocol integration for code completion and linting
jupyterlab-git: Git version control support
Additional extensions can be installed using pip install or jlpm (a yarn-compatible package manager).

Frequently Asked Questions

Q: What version of JupyterLab is pre-installed?

Database Mart provides JupyterLab v4.5.4, the latest stable version with enhanced notebook rendering, real-time collaboration features, and an extended plugin ecosystem.

Q: What operating systems are supported?

The JupyterLab Marketplace image is available for Ubuntu Server 22.04 LTS and Ubuntu Server 24.04 LTS, both 64-bit.

Q: What programming languages are supported?

JupyterLab supports over 40 programming languages via kernels. Pre-installed kernels include Python, R, Julia, and Scala. Additional kernels (e.g., JavaScript, Java, C++) can be installed as needed.

Q: How do I access JupyterLab from a different browser?

JupyterLab is accessible through any modern browser using http://your-server-ip:8888/lab. If you're using Safari, ensure third-party cookies are enabled or switch to Chrome/Firefox, as JupyterLab requires cross-domain cookie support.

Q: Why are some notebook parts displayed as garbled text or not rendering correctly?

This often happens when a code cell is accidentally changed to Markdown format. The keyboard shortcut ESC+M changes the cell type to Markdown. You can revert it by selecting the cell and changing the type back to "Code" from the dropdown menu at the top.

Q: How do I back up my JupyterLab notebooks?

Notebooks are regular JSON files. You can:

Use Git for version control (pre-installed jupyterlab-git extension)
Copy files using scp/rsync from /home/administrator/ or your project directory
Use the Download option in the JupyterLab file browser