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macOS setup for Python and local AI

Audience: beginners learning Python and local AI on macOS. The commands below use native zsh and Apple Silicon checks; adapt architecture checks for Intel Macs.

Hardware assumptions

Day 2: Python, editor and terminal setup

Required first practical lesson: macOS editor + terminal workbench. Do this before Python control flow. Use VS Code to edit/save and zsh in Terminal.app to run/inspect the same file. The lesson covers Control-A/E/P/N/R history/navigation, Command-C/V clipboard, Control-C interrupt, and the code . launcher.

For VS Code on Apple Silicon, use Command + Shift + P → Shell Command: Install 'code' command in PATH, then reopen Terminal; code . should open your folder. Use File → Open Folder if PATH setup is unsuccessful. Official macOS instructions. Copilot CLI is optional and not needed.

  1. Install a recent supported Python from python.org or Homebrew. For beginners who dislike package managers, the official signed Python installer is fine. Never replace/remove the Python owned by macOS.
  2. Install VS Code (or another editor). Open Terminal.app.
  3. Use uname -m to verify native arm64 (if you see x86_64, check Rosetta/terminal settings). Use python3 --version and which python3 to find the interpreter. The Python virtual environment comes later on Day 7, after the learner can edit and run files.
  4. Create a workspace in your own home directory. The sample paths below are suggested folder names; adjust them to your setup.
uname -m
python3 --version
which python3

mkdir -p ~/Projects/python-ai-playground
cd ~/Projects/python-ai-playground
# After installing the VS Code 'code' launcher:
code .
# Create hello.py in the editor, save with Command-S, then run:
python3 hello.py

The python-ai-playground folder is a scratch playground. When the learner downloads the course ZIP from the opening slide, extract it into ~/Projects/py-ai-studyplan and open that folder in VS Code. See the exact download/unzip path. Subsequent examples/ commands run from this extracted course root, not from an empty scratch folder.

For learners who want it later, Homebrew is an optional, useful package manager. Its native Apple Silicon default installation prefix is /opt/homebrew. Do not make Homebrew a prerequisite for the first Python exercise.

Day 13: Install Ollama (desktop app)

  1. Download the macOS application from ollama.com/download. Launch it so its local service is running.
  2. In Terminal, pull the balanced starter model gemma3:4b. The examples use this tag; choose gemma3:1b if memory or performance is limited.
  3. Optionally compare answers against qwen3:8b; do not pre-download multiple models before they have understood the first one.
ollama --version
ollama pull gemma3:4b
ollama run gemma3:4b

# Later, for comparative evaluation, optionally:
ollama pull qwen3:8b
ollama run qwen3:8b

# Confirm which models have been downloaded:
ollama list

As of 2026-10-26, the Ollama catalog lists gemma3:4b at about 3.4 GB and qwen3:8b at about 5.2 GB of model download data; these are not peak live RAM requirements. See Gemma 3 and Qwen 3 for current tags.

Use the standard Ollama application for this course; separate PyTorch configuration is not part of setup. Choose a local model when following the upstream quickstart: its cloud-model/API-key examples are a different route.

Day 14: Python calls Ollama

From the extracted course root, reuse the Day 7 environment or create one if it is missing. Activate it in every new Terminal tab, then install the declared sample dependencies:

cd ~/Projects/py-ai-studyplan
python3 -m venv .venv
source .venv/bin/activate
python -m pip install -r examples/requirements.txt
python examples/cli_chat.py --model gemma3:4b

Check that pwd ends in py-ai-studyplan and ls examples/requirements.txt finds the file before installing. If either check fails, find the extracted course folder first. Type quit to leave the CLI chat; use deactivate when finished with the virtual environment.

Optional shell enhancement after Day 7

macOS already ships with zsh; no framework is needed for shortcuts. Zsh for Humans (z4h) is macOS-compatible and offers autosuggestions, fzf history and completions, but its maintainer states that it has very limited support and most bugs will go unfixed. It modifies shell startup files with backup prompts. Keep it an optional, conscious experiment, not a prerequisite or automatic install. Read the workbench's tradeoffs before using the upstream installer.

Day 20: Docker Desktop (optional)

Docker Desktop supports Apple Silicon. Install only after the Python program and Streamlit app run without containers; a Docker image contains your UI, not the model weights. Run the sample through the project's Compose configuration. On Docker Desktop, the name host.docker.internal usually connects to host services; if it does not, diagnose host reachability and firewall behavior without making the local inference service internet-facing.

Common macOS gotchas