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A robust and versatile Docker image designed for developers, featuring preinstalled Java, Node.js, Python, Docker, Kubernetes, Helm, and more. Ideal for building and testing applications in a streamlined and up-to-date environment. This image is also ready to be used as base for remote development environments, making it an excellent starting point for setting up your remote development workspace.
JNP provides a complete Docker image that integrates essential programming languages and runtimes such as Java, Node.js, and Python, along with crucial development tools such as Docker, Kubernetes, and Helm. This image is a starting point for developers who need a reliable and consistent development stack across multiple projects, and for anyone who wants to get started with remote development environments.
- Preinstalled Languages: Java, Node.js, Python.
- Docker & Kubernetes Ready: Includes Docker in Docker (DinD), Kubernetes (kubectl), and Helm.
- NVIDIA GPU Support: Comes with the NVIDIA Container Toolkit.
- Multi-Architecture Support: Built to run on both amd64 and arm64 architectures.
- Remote Development Ready: Optimized for setting up and using in remote development environments, making it easy to create a consistent and portable workspace.
- Ensure Docker is installed and running on your machine.
- Pull the Image: Get the latest JNP Docker image from Docker Hub:
docker pull mrcolorrain/jnp:base - Run the Container:
- To use Docker in Docker (DinD) start a new container in privileged mode using:
docker run -it --rm --privileged mrcolorrain/jnp:base- You can also start without privileged mode; the container will work, but the DinD service start will timeout and exit, so you will be able to use Docker only by either running it in privileged mode or by pointing Docker CLI to connect to another running Docker host, starting the container, and passing the env variable DOCKER_HOST:
docker run -it --rm -e DOCKER_HOST=mydockerhost:port mrcolorrain/jnp:base
- You can also start without privileged mode; the container will work, but the DinD service start will timeout and exit, so you will be able to use Docker only by either running it in privileged mode or by pointing Docker CLI to connect to another running Docker host, starting the container, and passing the env variable DOCKER_HOST:
- To use it with Nvidia GPU support add also the
--gpusflag:docker run -it --rm --gpus all --privileged mrcolorrain/jnp:base
- To use Docker in Docker (DinD) start a new container in privileged mode using:
- Operating System: Ubuntu 24.04 LTS
- Languages: Java (OpenJDK 21), Node.js (LTS), Python 3
- Development Tools: Docker, Kubernetes (kubectl), Helm, Build-essential, Git
- System Utilities: curl, wget, vim, nano, htop, jq, rsync
This image is suitable for remote development scenarios. Whether you are using a cloud-based development environment, a remote server, or a containerized on-premises setup, JNP provides the flexibility and tools you need to power your remote workspace environment.
Both images are designed to be versatile, allowing for easy customization:
- Modify the Dockerfile to suit your specific development needs.
- Extend the images by adding your own tools or scripts.
- For discussions, info, and feature requests, use the Discussion tab.
- For issues and bug reporting, use the Issue tab.
If you find this project helpful, consider supporting its development by leaving a star ⭐ or contributing to the repository. Your support is greatly appreciated!
This project builds upon and is inspired by other excellent open-source projects. Special thanks to:
- NVIDIA DinD for providing the base setup for Docker in Docker (DinD) with NVIDIA GPU support.
This project is licensed under the Mozilla Public License 2.0 (MPL 2.0).