100 lines
3.4 KiB
Markdown
100 lines
3.4 KiB
Markdown
# RCEO-AIOS-Public-Tools-DocMaker-Computational Container
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This container is part of the AIOS-Public project and provides a comprehensive documentation and computational environment.
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## Overview
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The RCEO-AIOS-Public-Tools-DocMaker-Computational container extends the full documentation environment with computational tools for data analysis, scientific computing, and interactive notebooks. It's designed for CTO mode operations involving R&D and computational work.
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## Tools Included
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Inherits all tools from:
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- **RCEO-AIOS-Public-Tools-DocMaker-Full**: All documentation and LaTeX tools
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### Computational Tools
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- **R Programming Language**: Statistical computing and data analysis
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- **Python Scientific Stack**:
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- pandas - Data manipulation
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- numpy - Numerical computing
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- matplotlib - Visualization
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- scipy - Scientific computing
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- **Jupyter Notebooks**: Interactive computational environments
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- **GNU Octave**: Numerical computations (MATLAB alternative)
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- **bc**: Command-line calculator
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## Usage
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### Building the Computational Container
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```bash
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# From this directory
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cd /home/localuser/AIWorkspace/AIOS-Public/Docker/RCEO-AIOS-Public-Tools-DocMaker-Computational
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# Use the wrapper script to automatically detect and set user IDs
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./docker-compose-wrapper.sh build
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# Or run commands in the computational container with automatic user mapping
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./docker-compose-wrapper.sh run docmaker-computational [command]
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# Example: Run R analysis
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./docker-compose-wrapper.sh run docmaker-computational Rscript analysis.R
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# Example: Run Python analysis
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./docker-compose-wrapper.sh run docmaker-computational python analysis.py
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# Example: Convert a Markdown file to beautiful PDF using Eisvogel template
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./docker-compose-wrapper.sh run docmaker-computational pandoc input.md --template eisvogel -o output.pdf --pdf-engine=xelatex
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# Example: Start Jupyter notebook server
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./docker-compose-wrapper.sh up
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# Then access at http://localhost:8888
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```
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### Using with docker-compose directly
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```bash
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# Set environment variables and run docker-compose directly
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LOCAL_USER_ID=$(id -u) LOCAL_GROUP_ID=$(id -g) docker-compose up --build
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# Or export variables first
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export LOCAL_USER_ID=$(id -u)
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export LOCAL_GROUP_ID=$(id -g)
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docker-compose up
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```
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### Using the wrapper script
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```bash
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# Build and start the computational container with Jupyter access and automatic user mapping
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./docker-compose-wrapper.sh up --build
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# Start without rebuilding
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./docker-compose-wrapper.sh up
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# View container status
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./docker-compose-wrapper.sh ps
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# Stop containers
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./docker-compose-wrapper.sh down
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```
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## User ID Mapping (For File Permissions)
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The container automatically detects and uses the host user's UID and GID to ensure proper file permissions. This means:
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- Files created inside the container will have the correct ownership on the host
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- No more root-owned files after container operations
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- Works across different environments (development, CI/CD, cloud)
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The container detects the user ID from the mounted workspace volume. If needed, you can override the default values by setting environment variables:
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```bash
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# Set specific user ID and group ID before running docker-compose
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export LOCAL_USER_ID=1000
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export LOCAL_GROUP_ID=1000
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docker-compose up
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```
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Or run with inline environment variables:
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```bash
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LOCAL_USER_ID=1000 LOCAL_GROUP_ID=1000 docker-compose up
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```
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The container runs as a non-root user named `ReachableCEO-Tools` with the detected host user's UID/GID. |