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Lesson 0.1 - Setting up a local environment (optional)

3 min read

Prefer to run everything on your own machine? This guide walks you through setting up the course environment locally using our pre-configured Dev Container.

Using the Pre-configured Dev Container ⭐

This is the best way to get the same experience as Codespaces, but running locally.

Prerequisites

You’ll need:

Setup Steps

  1. Clone the repository:

    git clone https://github.com/Shrub24/raid-codespace.git

  2. Open the folder in VS Code:

  3. Reopen in Container:

    • A popup should appear in the bottom right: “Reopen in Container”
    • Click it and wait for the container to build (5-10 minutes first time)

    If no popup appears:

    • Press Ctrl+Shift+P (or Cmd+Shift+P on Mac)
    • Type “Dev Containers: Rebuild and Reopen in Container”
    • Select it and wait for the build

  4. Verify Setup:

    • Open Lesson 1/lesson1-litellm.ipynb
    • Run the first cell - if it works, you’re ready!

Dev Container Features

Your container includes:

  • ✅ Python 3.12 with all dependencies
  • ✅ LiteLLM pre-configured
  • ✅ Ollama with a local model
  • ✅ Jupyter Notebooks
  • ✅ All course materials

Troubleshooting

Container build fails:

  • Ensure Docker is running (docker ps should work)
  • Try restarting Docker Desktop
  • Check you have enough disk space (at least 10GB free)

“Cannot connect to Docker daemon”:

  • Docker Desktop may not be running
  • On Linux, ensure your user is in the docker group: sudo usermod -aG docker $USER (then log out and back in)

VS Code can’t find Docker:

  • Restart VS Code after installing Docker
  • On Windows, ensure WSL 2 is set up if using Docker Desktop

Getting API Keys

To use cloud models (like Google Gemini):

  • Google Gemini: Get a free API key at aistudio.google.com
  • Add to your .env file: GOOGLE_API_KEY=your_key_here

What’s Next?

Once your local environment is set up and verified, you’re ready to start learning!

→ Lesson 1 - Getting started with LiteLLM

Tips for Local Development

  • 💡 Use version control - Commit your work regularly with Git
  • 💡 Keep Ollama running - The local model server needs to stay active
  • 💡 Virtual environment - Always activate your venv before working
  • 💡 Port conflicts - If you have issues, check ports 8888 (Jupyter) and 11434 (Ollama) aren’t in use
  • 💡 Updates - Pull the latest changes occasionally: git pull origin main

Manual Setup (At Your Own Risk)

If you prefer to set up the environment manually without Dev Containers, you can configure it yourself using the files in the raid-codespace repository:

  • Requirements: requirements.txt
  • Ollama model: hf.co/unsloth/Phi-4-mini-instruct-GGUF

⚠️ Note: Manual setup is not officially supported or documented. We strongly recommend using the Dev Container setup above for a consistent experience.

Course Index: Intro to AI Development Course

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