Python Wolfi-based Docker Image: nnthanh101/python

Overview

The nnthanh101/python:cloudops image is a secure, lightweight, and production-grade Python environment built on Chainguard's Wolfi Base. This image has been optimized to support multi-cloud environments (AWS, Azure) and cross-platform workflows for analytics, automation, and data science projects.

With a focus on modern CloudOps and DevOps practices, this image incorporates security, maintainability, and scalability into its design. It integrates essential extensions like JupyterLab, Vizro, and MkDocs for analytics and documentation workflows.

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Key Features

• Lightweight and Secure:
• Built on cgr.dev/chainguard/wolfi-base:cloudops, prioritizing security with minimal attack surface.
• Non-root os user pre-configured to ensure security.
• Cross-Platform Support: Supports multiple architectures, including linux/amd64 and linux/arm64.

• Cloud-Native Utilities: Pre-installed tools like starship, task, git-lfs, and support for pre-configured Python 3.12 virtual environment support for compatibility with modern workflows. Integrated also with awscli, azure-cli, and ansible for provisioning and automation.

• Pre-Bundled Python Extensions: Categorized for analytics, documentation, and cloud operations with AWS CLI, and Azure CLI.

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Usage

Run a Python Shell and Tasklist

Syntax: docker run -it -p 8888:8888 -v LOCAL_FOLDER:REMOTE_FOLDER registry/image

docker run -it --rm nnthanh101/python:cloudops python

## Run a Tasklist
docker run -it --rm nnthanh101/python:cloudops task

Run with Data Mounting

• ## TODO: -e DEBUG=1 &&

export HOST_AWS="$HOME/.aws"

docker run -it -p 8888:8888 \
  -v "$(pwd):/workspace:rw" \
  -v "${HOST_AWS}:/workspace/.aws:ro" \
  --user $(id -u):$(id -g) \
  nnthanh101/python:cloudops

Use for JupyterLab

USERNAME=os

docker run -it -p 8888:8888 \
 -v $HOME/notebooks:/home/$USERNAME/notebooks \
 nnthanh101/python:cloudops jupyter lab --ip=0.0.0.0 --no-browser

Extend the Image

To add custom Python packages:

FROM nnthanh101/python:cloudops

COPY requirements.txt requirements.txt
RUN pip install --no-cache-dir -r requirements.txt

DevContainer Usage

• Project/Folder Structure:

Ensure the host project directory is structured as follows:

├── .devcontainer       ## VS Code DevContainer configuration
│   ├── devcontainer.json
├── projects            ## Your code and Python projects
├── credentials         ## AWS credentials, config files, etc.
├── data                ## Raw and processed data files

• Run Command via VS Code:

1. Open the project folder in VS Code.
2. Press Ctrl+Shift+P, select Remote-Containers: Open Folder in Container....
3. Choose the folder containing .devcontainer.

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Technical Notes - Python Extensions:

• ✅ nnthanh101/python:jupyterlab
• ☑️ nnthanh101/python:techdocs
• ✅☑️ nnthanh101/python:latest
• ✔️ nnthanh101/python:cloudops
• nnthanh101/python:analytics

✅	Python Extension	Category	Description	Relevant URL
✔️	ansible	Automation	Configuration management and orchestration tool.	Ansible Docs
✔️	awscli	Cloud Management	Command-line interface for AWS services.	AWS CLI Docs
✔️	azure-cli	Cloud Management	Command-line tools for managing Azure resources.	Azure CLI Docs
✔️	boto3	Cloud Management	AWS SDK for Python to automate and manage AWS services programmatically.	Boto3 Docs
	MkDocs Extensions
☑️	mkdocs	Documentation Tools	A static site generator for project documentation.	MkDocs Docs
☑️	mkdocs-material	Documentation Tools	A modern, responsive theme for MkDocs.	Material Theme
☑️	mkdocstrings	Documentation Tools	Plugin for documenting Python code using docstrings.	MkDocstrings
☑️	mkdocstrings[python]	Documentation Tools	Enhanced Python support for mkdocstrings.	MkDocstrings Python
☑️	mkdocs-drawio-exporter	Documentation Tools	Export draw.io diagrams to integrate with MkDocs.	DrawIO Exporter
☑️	pymdown-extensions	Documentation Tools	Markdown extensions for advanced formatting.	Pymdown Extensions
☑️	pygments	Documentation Tools	Syntax highlighter for MkDocs and other tools.	Pygments Docs
	JupyterLab Ecosystem
✅	ipykernel	Interactive Computing	Core dependency for running Jupyter Notebook kernels.	IPyKernel Docs
✅	ipywidgets	Interactive Computing	Interactive widgets for Jupyter.	IPyWidgets Docs
✅	jupyterlab	Interactive Computing	Web-based interface for interactive Python computing.	JupyterLab Docs
✅	jupyterlab-git	Interactive Computing	Git integration for JupyterLab.	JupyterLab Git
⬜	jupyter-resource-usage	Interactive Computing	Monitor system resource usage during JupyterLab sessions.	Resource Usage
⬜	jupyter_scheduler	Interactive Computing	Schedule and manage Jupyter Notebook execution tasks.	Jupyter Scheduler
✔️	gunicorn	Web Application Hosting	Lightweight WSGI server for Python web applications.	Gunicorn Docs
✔️	pandas	Data Science	Library for data manipulation and analysis.	Pandas Docs
✔️	plotly	Data Visualization	Interactive and publication-quality data visualization.	Plotly Docs
✔️	vizro	Data Visualization	Lightweight dashboards for Python data visualization.	Vizro Docs

Directory Structure

.
├── Dockerfile                ## Main Dockerfile for building the image
├── start.sh                  ## Startup script to initialize services
├── starship.sh               ## Starship prompt configuration script
├── .banner.sh                ## Custom banner for interactive sessions
├── alias.sh                  ## Aliases for general use
├── .bashrc                   ## Bash runtime configuration
├── .config/
│   └── starship.toml         ## Starship prompt configuration
├── Taskfile.yaml             ## Taskfile for automation tasks