Advanced Linux AI Development & Containerization

Master advanced Linux development tools, Python environments, Docker containerization, and professional AI deployment workflows on Linux systems.
Tier: Advanced
Difficulty: Advanced

Master advanced Linux development tools, Python environments, Docker containerization, and professional AI deployment workflows on Linux systems.
Tier: Advanced
Difficulty: Advanced

Learning Objectives

Configure advanced Linux development environments for AI
Master Python environment management with conda and pip
Implement Docker containerization for AI applications
Deploy AI models using Linux server environments
Build professional AI development workflows

Linux AI Development Tools & Environment

Setting Up Linux AI Development Tools

Package Management Mastery

Advanced APT Usage


# Update package database

sudo apt update

# Show package information

apt show python3-pip

# Search for packages

apt search "machine learning"

# List installed packages

apt list --installed | grep python

# Remove packages completely

sudo apt purge package-name
sudo apt autoremove

Essential AI Development Packages


# System dependencies for AI development

sudo apt install -y \
build-essential \
software-properties-common \
python3-dev \
python3-pip \
python3-venv \
libblas-dev \
liblapack-dev \
libatlas-base-dev \
gfortran \
libhdf5-dev \
libssl-dev \
libffi-dev \
libjpeg-dev \
libpng-dev \
libfreetype6-dev \
pkg-config

# Additional utilities

sudo apt install -y \
htop \
btop \
ncdu \
tree \
jq \
tmux \
screen \
neofetch \
speedtest-cli

Python Environment Management

Install Miniforge (Recommended over Anaconda)


# Download Miniforge installer

wget <https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh>

# Make executable and install

chmod +x Miniforge3-Linux-x86\_64.sh
./Miniforge3-Linux-x86\_64.sh

# Follow installation prompts, then restart shell

source ~/.bashrc

Create AI Development Environment


# Create conda environment for AI development

conda create -n ai-dev python=3.11 -y
conda activate ai-dev

# Install essential AI packages

conda install -y \
numpy \
pandas \
matplotlib \
seaborn \
scikit-learn \
jupyter \
jupyterlab \
ipython

# Install PyTorch (CPU version for now)

conda install pytorch torchvision torchaudio cpuonly -c pytorch

# Install additional packages with pip

pip install \
transformers \
openai \
anthropic \
langchain \
streamlit \
fastapi \
uvicorn \
requests \
beautifulsoup4 \
selenium

GPU Support Setup (If Available)


# Check for NVIDIA GPU

nvidia-smi

# Install CUDA toolkit (if GPU available)

wget <https://developer.download.nvidia.com/compute/cuda/repos/wsl-ubuntu/x86_64/cuda-wsl-ubuntu.pin>
sudo mv cuda-wsl-ubuntu.pin /etc/apt/preferences.d/cuda-repository-pin-600
sudo apt-key adv --fetch-keys <https://developer.download.nvidia.com/compute/cuda/repos/wsl-ubuntu/x86_64/3bf863cc.pub>
sudo add-apt-repository "deb <https://developer.download.nvidia.com/compute/cuda/repos/wsl-ubuntu/x86_64/> /"
sudo apt update
sudo apt install -y cuda-toolkit-12-2

# Install PyTorch with CUDA support

conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia

Development Tools Configuration

Configure Zsh with Oh My Zsh (Optional but Recommended)


# Install Zsh

sudo apt install -y zsh

# Install Oh My Zsh

sh -c "$(curl -fsSL <https://raw.github.com/ohmyzsh/ohmyzsh/master/tools/install.sh>)"

# Install useful plugins

git clone <https://github.com/zsh-users/zsh-autosuggestions> ~/.oh-my-zsh/custom/plugins/zsh-autosuggestions
git clone <https://github.com/zsh-users/zsh-syntax-highlighting> ~/.oh-my-zsh/custom/plugins/zsh-syntax-highlighting

# Edit ~/.zshrc to enable plugins

plugins=(git zsh-autosuggestions zsh-syntax-highlighting python conda-env)

Configure Git for AI Projects


# Advanced Git configuration for AI development

git config --global core.editor "code --wait"
git config --global merge.tool "code --wait"
git config --global diff.tool "code --wait"

# Git LFS for large model files

sudo apt install -y git-lfs
git lfs install

# Track common AI file types with LFS

git lfs track "*.pkl"
git lfs track "*.h5"
git lfs track "*.pth"
git lfs track "*.bin"
git lfs track "*.safetensors"
git lfs track "*.onnx"

# Add .gitattributes

echo "*.pkl filter=lfs diff=lfs merge=lfs -text" >> .gitattributes
echo "*.h5 filter=lfs diff=lfs merge=lfs -text" >> .gitattributes
echo "\*.pth filter=lfs diff=lfs merge=lfs -text" >> .gitattributes

Jupyter Lab Configuration


# Generate Jupyter config

jupyter lab --generate-config

# Install useful extensions

pip install \
jupyterlab-git \
jupyterlab-lsp \
jupyter-ai \
nbdime \
jupyterlab\_code\_formatter

# Configure Jupyter Lab

cat >> ~/.jupyter/jupyter\_lab\_config.py ~/.tmux.conf ~/monitor\_system.sh << 'EOF'
#!/bin/bash
echo "=== System Performance Monitor ==="
echo
echo "CPU Usage:"
top -bn1 | grep "Cpu(s)" | sed "s/.*, *\([0-9.]*\)%* id.\*/\1/" | awk '{print 100 - $1"%

---

## Docker Containerization for AI Applications

# Docker Containerization for AI ApplicationsMaster containerization to create reproducible, scalable AI applications that run consistently across development, testing, and production environments.

## Why Docker for AI Development?

### Reproducibility Challenges in AI
- Environment Dependencies: Complex Python packages, CUDA versions, system libraries
- Version Conflicts: Different projects requiring different package versions
- Deployment Consistency: "Works on my machine" syndrome
- Collaboration Issues: Team members with different OS and configurations

### Docker Solutions
- Isolated Environments: Each container runs independently
- Consistent Deployment: Same environment from development to production
- Easy Scaling: Horizontal scaling across multiple servers
- Version Control: Infrastructure as code with Dockerfiles

## Docker Installation and Setup

### Install Docker on WSL2

```bash

# Update package database

sudo apt update

# Install prerequisites

sudo apt install -y \
apt-transport-https \
ca-certificates \
curl \
gnupg \
lsb-release

# Add Docker's official GPG key

curl -fsSL <https://download.docker.com/linux/ubuntu/gpg> | sudo gpg --dearmor -o /usr/share/keyrings/docker-archive-keyring.gpg

# Add Docker repository

echo "deb [arch=$(dpkg --print-architecture) signed-by=/usr/share/keyrings/docker-archive-keyring.gpg] <https://download.docker.com/linux/ubuntu> $(lsb\_release -cs) stable" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null

# Install Docker Engine

sudo apt update
sudo apt install -y docker-ce docker-ce-cli containerd.io docker-compose-plugin

# Add user to docker group (avoid sudo)

sudo usermod -aG docker $USER

# Restart shell or run:

newgrp docker

# Test installation

docker --version
docker run hello-world

Configure Docker for AI Development


# Create Docker daemon configuration for better performance

sudo mkdir -p /etc/docker
sudo tee /etc/docker/daemon.json <<EOF
{
"storage-driver": "overlay2

---

Advanced Linux AI Development & Containerization

Linux AI Development Tools and Environment

Setting Up Linux AI Development Tools