How to Extend the Base LabNow AI Docker Images with Additional Development Tools

To extend LabNow AI Docker images, create a Dockerfile that inherits from labnow/docker_base or labnow/docker_atom, source the script-setup.sh helper library, and invoke specific setup_<tool>() functions to install Node.js, Rust, Go, Java, or other development tools.

The labnow-ai/lab-foundation repository provides a modular Docker image hierarchy designed for data science and AI workflows. When you need to extend the base LabNow AI Docker images with additional development tools, the project provides a systematic approach using helper scripts and standardized installation functions rather than manual package management.

Understanding the LabNow AI Image Hierarchy

The LabNow AI Docker ecosystem is built in layers, with each image serving a specific purpose:

• labnow/docker_base: Provides a minimal, Conda-based Python environment. Use this when you need a lightweight Python stack without full Ubuntu system utilities.
• labnow/docker_atom: Built on a full Ubuntu base with core system utilities and the script-setup.sh helper library pre-installed. This is the recommended starting point for complex development environments.
• labnow/docker_docker_kit: Extends the base image with Docker-Compose and Docker-Syncer capabilities, useful for container-in-container workflows.

Step-by-Step: How to Extend LabNow AI Docker Images

Choose Your Base Image

Select the appropriate foundation based on your target environment:

• Use FROM labnow/docker_base for Python-only data science stacks.
• Use FROM labnow/docker_atom when you need system-level tools, multiple languages, or the helper script library.

Leverage the Setup Script Library

The file docker_atom/work/script-setup.sh contains a comprehensive library of setup_<tool>() functions. These functions handle architecture detection, version fetching from upstream releases, extraction, and path registration automatically.

To access these helpers in your Dockerfile:

COPY work /opt/utils/
RUN source /opt/utils/script-setup.sh && setup_<tool>

Install Development Tools

The standard pattern for extending LabNow AI images involves sourcing the setup script, invoking the desired installation functions, and cleaning up temporary files:

FROM labnow/docker_base

COPY work /opt/utils/

RUN set -eux && \
    source /opt/utils/script-setup.sh && \
    setup_node_base && \
    setup_rust && \
    setup_GO && \
    source /opt/utils/script-utils.sh && install__clean

Practical Examples for Extending LabNow AI Images

Adding Node.js, Yarn, and pnpm

To extend the base image with JavaScript development tools:

FROM labnow/docker_base

COPY work /opt/utils/

RUN set -eux && \
    source /opt/utils/script-setup.sh && \
    setup_node_base && \
    setup_node_pnpm && \
    source /opt/utils/script-utils.sh && install__clean

The setup_node_base function installs Node.js and npm, while setup_node_pnpm adds the pnpm package manager. These functions are defined in docker_atom/work/script-setup.sh at lines 42-60.

Installing Java and Maven

For Java development environments:

FROM labnow/docker_atom

COPY work /opt/utils/

RUN set -eux && \
    source /opt/utils/script-setup.sh && \
    setup_java_base && \
    setup_java_maven && \
    source /opt/utils/script-utils.sh && install__clean

The setup_java_base function installs OpenJDK 11 by default, and setup_java_maven adds the Maven build tool. These helpers handle version detection and JAVA_HOME configuration automatically.

Setting Up R and Julia

For statistical computing and data science:

FROM labnow/docker_base

COPY work /opt/utils/

RUN set -eux && \
    source /opt/utils/script-setup.sh && \
    setup_R_base && \
    setup_julia && \
    source /opt/utils/script-utils.sh && install__clean

The setup_R_base function installs the R language base packages, while setup_julia downloads and configures the latest stable Julia release.

Adding System Packages with Custom apt Lists

To install additional OS-level dependencies:

Create a file named my_dev.apt:

build-essential
git
curl
wget
libpq-dev

Then reference it in your Dockerfile:

FROM labnow/docker_base

COPY work /opt/utils/
COPY my_dev.apt /opt/utils/

RUN set -eux && \
    source /opt/utils/script-setup.sh && \
    install_apt /opt/utils/my_dev.apt && \
    source /opt/utils/script-utils.sh && install__clean

The install_apt function processes the package list and handles installation cleanup.

Key Files and Functions Reference

Path	Purpose
docker_base/Dockerfile	Minimal Conda-Python base image
docker_atom/Dockerfile	Full Ubuntu image with core utilities and helper scripts
docker_docker_kit/Dockerfile	Adds Docker-Compose and Docker-Syncer capabilities
docker_atom/work/script-setup.sh	Library of setup_<tool>() functions for installing development languages and tools
docker_atom/work/script-utils.sh	Utility helpers including install_apt and install__clean
docker_atom/work/install_list_base.apt	Default APT packages installed in base images

Summary

• Extend LabNow AI Docker images by creating a new Dockerfile that uses FROM labnow/docker_base or FROM labnow/docker_atom.
• Leverage the helper library at docker_atom/work/script-setup.sh to access standardized setup_<tool>() functions for Node.js, Rust, Go, Java, R, Julia, and other languages.
• Follow the standard pattern: copy the work directory, source the setup script, invoke desired functions, and run install__clean to minimize image size.
• Add system packages by creating custom .apt list files and using the install_apt utility function.

Frequently Asked Questions

What is the difference between docker_base and docker_atom?

labnow/docker_base provides a minimal Conda-based Python environment suitable for lightweight data science workflows. labnow/docker_atom builds on a full Ubuntu base and includes system-level utilities, the script-setup.sh helper library, and broader compatibility for multi-language development environments. Choose docker_base for Python-only stacks and docker_atom when you need the helper scripts or full Ubuntu compatibility.

How do I add a custom programming language not covered by the setup scripts?

If your language isn't supported by the existing setup_<tool>() functions in docker_atom/work/script-setup.sh, you can either extend the script by adding a new function following the existing pattern (architecture detection, download, extraction, and path registration), or install the language manually in your Dockerfile using standard RUN commands after sourcing the utility scripts for environment consistency.

Can I install additional system packages using apt?

Yes. Create a text file with the .apt extension containing one package name per line, copy it into your image, and use the install_apt function from script-utils.sh. For example: install_apt /opt/utils/my_packages.apt. This approach ensures consistent package installation and automatic cleanup.

Where are the setup helper functions defined?

The setup helper functions are defined in docker_atom/work/script-setup.sh within the labnow-ai/lab-foundation repository. This file contains standardized setup_<tool>() functions for installing Node.js, Rust, Go, Java, R, Julia, Lua, Bazel, Gradle, and other development tools. The functions handle architecture detection, version resolution, and environment configuration automatically.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}