JupyterLab is a web-based interactive development environment (IDE) that allows users to create and share documents that contain live code, equations, visualizations, and narrative text. It is the latest evolution of the popular Jupyter Notebook and offers several advantages over its predecessor. We provide an overview the JupyterLab architecture and explain how to start using JupyterLab as a Docker extension.
Learn how running Microcks as a Docker extension enables developers to swiftly create, test, and iterate on APIs without leaving the Docker environment.
We explore the concept of using pretrained models with TensorFlow.js and Docker and delve into the potential applications and benefits offered.
Learn how using Memgraph as a Docker Extension offers a powerful and efficient way to leverage real-time analytics from a graph database.
We show how to use Docker Desktop to containerize JupyterLab 4.0 and evaluate its features without affecting your existing environment.
We walk through building and deploying a conversational AI chatbot using Rasa and Docker, highlighting the importance of containerization for scalability, consistency, and simplified management of machine learning models.
The Digma Docker extension acts as your own intelligent agent for monitoring code execution, especially in development and testing.
Explore the benefits of using the Grafana Cloud Docker Extension with Docker Desktop for enhanced observability.
Learn how the LambdaTest Tunnel Docker Extension can streamline your testing workflow.