Can I run multiple JupyterLab environments simultaneously?

Yes, the skill supports multi-instance deployment. Use the --instance flag (e.g., --instance=2) to create and manage separate environments on different ports.

How do I enable GPU support for my Jupyter instance?

You can enable hardware acceleration by running 'ujust jupyter config --gpu-type=nvidia' (or amd/intel/auto) to configure the instance with the appropriate drivers.

What happens if I need to install additional Python packages?

You can use the 'ujust jupyter shell' command to open a terminal inside the container and run pip or conda commands directly.

How do I find the login URL for my JupyterLab instance?

Simply run the 'ujust jupyter url' command. If you are using a specific instance, add the --instance=N flag to get the correct port and address.

Jupyter ML/AI Environment

Name: Jupyter ML/AI Environment
Author: atrawog

byatrawog

Data Science & ML

Manages multiple JupyterLab development environments with GPU acceleration using Podman Quadlet containers.

About

This skill enables developers to easily deploy, configure, and manage isolated JupyterLab instances for machine learning and AI development. It leverages Podman Quadlets to run Jupyter as systemd user services, providing robust lifecycle management including multi-instance support, automatic or manual GPU detection (NVIDIA, AMD, Intel), and secure token authentication. Whether you need a local sandbox or a remote-accessible GPU-powered notebook server, this skill streamlines the entire setup, configuration, and maintenance process within your development workflow.

Key Features

Systemd integration via Podman Quadlet for reliable container management
0 GitHub stars
Multi-instance support for running isolated environments on different ports
Flexible authentication management including token-based security
Hardware acceleration support for NVIDIA, AMD, and Intel GPUs
Direct container shell access for package management and CLI tasks

Use Cases

Setting up separate, isolated PyTorch and TensorFlow environments on a single host
Deploying GPU-accelerated JupyterLab servers for deep learning and data science
Managing secure remote access to development notebooks via token authentication

About

Key Features

Systemd integration via Podman Quadlet for reliable container management
0 GitHub stars
Multi-instance support for running isolated environments on different ports
Flexible authentication management including token-based security
Hardware acceleration support for NVIDIA, AMD, and Intel GPUs
Direct container shell access for package management and CLI tasks

Use Cases

Setting up separate, isolated PyTorch and TensorFlow environments on a single host
Deploying GPU-accelerated JupyterLab servers for deep learning and data science
Managing secure remote access to development notebooks via token authentication