1.4.1 Follow-Along Workshop: Build a Reproducible AI Learning Lab

Workshop goal

This page is the practical bridge for Chapter 1. You will build a small repository named ai-learning-lab, run a Python environment check, save reports, make Git commits, and leave portfolio evidence that proves your workstation is usable.

What you will build

You will create a local project that answers one simple but important question: "Can I create, run, inspect, save, and explain a project on this computer?"

The final project will contain:

File or folder	Purpose
README.md	Explains the project goal, run command, and expected output
src/workstation_check.py	Runnable Python script that checks the current toolchain
notes/learning-log.md	Your daily command and troubleshooting notes
reports/workstation-check.json	Machine-readable environment report
reports/workstation-report.md	Human-readable portfolio evidence
.gitignore	Prevents cache, secrets, and local environments from being committed

This workshop uses only the Python standard library. You do not need a third-party SDK, a cloud account, or a paid service.

Step 0: Create a clean practice folder

Open a terminal and run:

mkdir ai-learning-lab
cd ai-learning-lab
pwd
python3 --version

Expected output will be similar to this:

/Users/zhangsan/ai-learning-lab
Python 3.12.3

The exact path and Python version can be different. For this workshop, Python 3.10 or newer is enough.

Windows note

In PowerShell, use python --version if python3 --version is not recognized. Keep using the same command consistently in the rest of the workshop.

Step 1: Understand the complete route first

Do not treat the tools as separate topics. In real work, they form one loop:

Step	Tool	What you do
1	Terminal	Move into the project folder and run commands
2	Python	Run the check script and generate evidence
3	Editor	Read and improve the files
4	Git	Save the stable state as a commit
5	Report	Keep output that proves the project can be rerun

When something fails later, you will usually debug one of these links: current folder, Python interpreter, dependency location, file path, or Git state.

Step 2: Create the project skeleton

Run these commands inside ai-learning-lab:

mkdir -p src notes reports notebooks screenshots
touch requirements.txt

Create a .gitignore file:

cat > .gitignore << 'EOF'
__pycache__/
*.pyc
.venv/
venv/
.env
.env.local
.ipynb_checkpoints/
.DS_Store
EOF

Create the first README:

cat > README.md << 'EOF'
# AI Learning Lab

This repository is my reproducible learning workspace for the AI full-stack course.

## Run

```bash
python3 src/workstation_check.py
```

## Expected output

The script prints the current project root, Python executable, Git branch, and report file paths.
EOF

Create the first learning log:

cat > notes/learning-log.md << 'EOF'
# Learning Log

| Time | Command or action | Result | Note |
|---|---|---|---|
EOF

Expected structure:

ai-learning-lab/
  README.md
  requirements.txt
  src/
  notes/
  reports/
  notebooks/
  screenshots/

If you installed tree, you can check it with tree -a -L 2. If not, find . -maxdepth 2 -type f is enough.

Step 3: Add the runnable environment check script

Create src/workstation_check.py and paste the complete code below.

from __future__ import annotations

import json
import platform
import subprocess
import sys
from datetime import datetime, timezone
from pathlib import Path
from typing import Any

PROJECT_ROOT = Path(__file__).resolve().parents[1]
REPORTS_DIR = PROJECT_ROOT / "reports"
NOTES_DIR = PROJECT_ROOT / "notes"
JSON_REPORT = REPORTS_DIR / "workstation-check.json"
MARKDOWN_REPORT = REPORTS_DIR / "workstation-report.md"
LEARNING_LOG = NOTES_DIR / "learning-log.md"


def utc_now() -> str:
    return datetime.now(timezone.utc).replace(microsecond=0).isoformat()


def run_command(command: list[str]) -> dict[str, Any]:
    try:
        completed = subprocess.run(
            command,
            cwd=PROJECT_ROOT,
            text=True,
            capture_output=True,
            check=False,
        )
    except FileNotFoundError:
        return {
            "command": " ".join(command),
            "returncode": 127,
            "stdout": "",
            "stderr": f"{command[0]} was not found",
        }
    return {
        "command": " ".join(command),
        "returncode": completed.returncode,
        "stdout": completed.stdout.strip(),
        "stderr": completed.stderr.strip(),
    }


def visible_project_files() -> list[str]:
    files: list[str] = []
    for path in sorted(PROJECT_ROOT.rglob("*")):
        if ".git" in path.parts or path.is_dir():
            continue
        files.append(str(path.relative_to(PROJECT_ROOT)))
    return files


def ensure_workspace_files() -> None:
    REPORTS_DIR.mkdir(exist_ok=True)
    NOTES_DIR.mkdir(exist_ok=True)
    if not LEARNING_LOG.exists():
        LEARNING_LOG.write_text(
            "# Learning Log\n\n| Time | Command or action | Result | Note |\n|---|---|---|---|\n",
            encoding="utf-8",
        )


def build_report() -> dict[str, Any]:
    return {
        "generated_at": utc_now(),
        "project_root": str(PROJECT_ROOT),
        "python_version": sys.version.split()[0],
        "python_executable": sys.executable,
        "platform": platform.platform(),
        "git_branch": run_command(["git", "branch", "--show-current"]),
        "git_status": run_command(["git", "status", "--short"]),
        "project_files": visible_project_files(),
    }


def write_reports(report: dict[str, Any]) -> None:
    JSON_REPORT.write_text(json.dumps(report, ensure_ascii=False, indent=2), encoding="utf-8")

    branch = report["git_branch"]["stdout"] or "(no branch yet)"
    status = report["git_status"]["stdout"] or "working tree clean"
    lines = [
        "# Workstation Report",
        "",
        f"- Generated at: {report['generated_at']}",
        f"- Project root: `{report['project_root']}`",
        f"- Python version: `{report['python_version']}`",
        f"- Python executable: `{report['python_executable']}`",
        f"- Git branch: `{branch}`",
        "",
        "## Git status",
        "",
        "```text",
        status,
        "```",
        "",
        "## Project files",
        "",
    ]
    lines.extend(f"- `{file}`" for file in report["project_files"])
    MARKDOWN_REPORT.write_text("\n".join(lines) + "\n", encoding="utf-8")


def append_learning_log(report: dict[str, Any]) -> None:
    branch = report["git_branch"]["stdout"] or "no branch"
    LEARNING_LOG.write_text(
        LEARNING_LOG.read_text(encoding="utf-8")
        + f"| {report['generated_at']} | python3 src/workstation_check.py | ok | branch: {branch} |\n",
        encoding="utf-8",
    )


def main() -> None:
    ensure_workspace_files()
    report = build_report()
    write_reports(report)
    append_learning_log(report)

    branch = report["git_branch"]["stdout"] or "(no branch yet)"
    print(f"[ok] project root: {PROJECT_ROOT}")
    print(f"[ok] python: {report['python_version']} at {report['python_executable']}")
    print(f"[ok] git branch: {branch}")
    print(f"[ok] wrote {JSON_REPORT.relative_to(PROJECT_ROOT)}")
    print(f"[ok] wrote {MARKDOWN_REPORT.relative_to(PROJECT_ROOT)}")
    print("[next] run git status, then commit the files when the output looks right")


if __name__ == "__main__":
    main()

Important beginner detail: the script uses Path(__file__).resolve().parents[1] to find the project root. That means it still works even if you run it from the project folder with python3 src/workstation_check.py.

Step 4: Initialize Git and run the script

Run the commands below:

git init
git branch -M main
git config user.name "AI Learner"
git config user.email "[email protected]"
python3 src/workstation_check.py

Expected output:

[ok] project root: /Users/zhangsan/ai-learning-lab
[ok] python: 3.12.3 at /usr/local/bin/python3
[ok] git branch: main
[ok] wrote reports/workstation-check.json
[ok] wrote reports/workstation-report.md
[next] run git status, then commit the files when the output looks right

Your Python path can be different. What matters is that the script runs, and both report files are created.

Check the generated evidence:

cat reports/workstation-report.md
git status --short

Expected git status --short output will look similar to this:

?? .gitignore
?? README.md
?? notes/
?? reports/
?? requirements.txt
?? src/

?? means Git can see the files, but it has not started tracking them yet.

Step 5: Make your first clean commit

git add .gitignore README.md requirements.txt src notes reports
git status --short
git commit -m "Initialize AI learning lab workstation"
git log --oneline

Expected output:

abc1234 Initialize AI learning lab workstation

You have now created the first stable checkpoint of your workstation.

Step 6: Practice a branch without breaking main

Create a small branch, add one learning note, rerun the script, then merge it back.

git checkout -b practice/add-daily-note
printf "\n- Practiced terminal, Python, and Git together.\n" >> notes/learning-log.md
python3 src/workstation_check.py
git diff -- notes/learning-log.md
git add notes/learning-log.md reports/workstation-check.json reports/workstation-report.md
git commit -m "Add daily tool practice note"
git checkout main
git merge practice/add-daily-note
git log --oneline --graph --all

PowerShell users can replace the printf line with:

Add-Content notes/learning-log.md "- Practiced terminal, Python, and Git together."

This exercise is small on purpose. The goal is not to learn a complex branching model yet; it is to feel that main is the stable line, and a practice branch is a safe place to try changes.

Step 7: Use VS Code and Jupyter as two work panels

Open the project in VS Code:

code .

Then check these items:

Check	Where to look
The folder name is ai-learning-lab	VS Code Explorer
The selected interpreter is the one you expect	Command Palette -> Python: Select Interpreter
The script runs	VS Code terminal: python3 src/workstation_check.py
Git changes are visible	Source Control panel

If you use Jupyter, create notebooks/01-workstation-review.ipynb and run a cell like this:

import json
from pathlib import Path

report = json.loads(Path("../reports/workstation-check.json").read_text(encoding="utf-8"))
print(report["python_version"])
print(report["git_branch"]["stdout"])
print(len(report["project_files"]))

Expected output:

3.12.3
main
7

The file count can be different. The important point is that the Notebook reads the same report generated by your script. This connects exploration (notebooks/) with project evidence (reports/).

Step 8: Troubleshoot by locating the broken link

When an error appears, slow down and locate which link is broken:

Symptom	First command to run	Likely cause	Fix
python3: command not found	python --version	Your system uses python instead of python3	Use python consistently, or configure PATH
No such file or directory	pwd and ls	You are in the wrong folder	cd into ai-learning-lab
ModuleNotFoundError	which python and python -m pip --version	The package was installed into another environment	Activate the intended environment, then install with python -m pip install ...
fatal: not a git repository	git status	You are outside the repository or forgot git init	Move into the project folder or run git init
VS Code runs a different Python	python3 -c "import sys; print(sys.executable)"	VS Code interpreter and terminal interpreter differ	Use Python: Select Interpreter
Jupyter cannot find the report	Path.cwd() in a Notebook cell	Notebook path is different from the project root	Use ../reports/... or move the Notebook

Do not only copy the last error line. Save the full command, the full output, and what you tried next in notes/learning-log.md.

Step 9: Package the portfolio evidence

Before leaving Chapter 1, your evidence pack should show both result and process:

Evidence	Minimum acceptable version	Stronger portfolio version
Run command	python3 src/workstation_check.py	README includes command, output, and troubleshooting note
Environment proof	Python version in terminal output	reports/workstation-check.json records executable and platform
Git proof	One commit	Multiple small commits with meaningful messages
Editor proof	Project opens in VS Code	Interpreter selected and run output recorded
Notebook proof	Optional	Notebook reads the generated report and explains it
Debug proof	One error note	A short "symptom -> cause -> fix" table

Mini exercises

1. Add a docs/commands.md file that records 10 commands you used in this chapter. Commit it with git commit -m "Add command practice notes".
2. Add a reports/terminal-transcript.txt file and paste the output of one successful run plus one mistake you fixed.
3. Add a second script named src/path_check.py that prints Path.cwd() and Path(__file__).resolve().
4. Create a branch named practice/readme-update, improve the README, then merge it back into main.

Final self-check

• I can explain the difference between current folder, project root, and Python file path.
• I can run the same script from the terminal and from VS Code.
• I can inspect Git state with git status --short.
• I can make a small branch, commit, and merge it back.
• I have a report file that proves my workstation can run code.

Once these are checked, Chapter 1 is no longer just a list of tools. It has become a working foundation you can reuse in every later chapter.