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Code Server Lab

Browser-based VS Code on Google Cloud

Overview

The Code Server Lab is where students do real software engineering work. Unlike Jupyter (which is cell-based and Python-centric), Code Server gives you the full VS Code experience inside the browser — file tree, multi-file editing, integrated terminal, git operations, extensions, and any language toolchain you need. It comes pre-loaded with the Claude Code CLI so students can pair-program with Anthropic's AI from the first command. This is the default environment for the Claude Code course and an alternate option for MLOps and Data Engineering students working on multi-file projects, Dockerfiles, Kubernetes manifests, CI/CD pipelines, and Airflow DAGs.

What You'll Do in This Lab

  • Write, refactor, and ship code across multiple files with the full VS Code IDE
  • Pair-program with Claude Code from the integrated terminal
  • Build, test, and run real applications (Node.js, Python, Docker)
  • Manage git workflows — commits, branches, pull requests — without leaving the browser
  • Install VS Code extensions and customize your dev environment just like locally
  • Run multi-service Docker Compose stacks or local Kubernetes clusters (kind / k3d)

Lab Workflow

1

Launch

Click "Start Lab" from your course dashboard. A pre-built VM boots in under 45 seconds with Claude Code, Docker, git, and your language toolchains ready.

2

Code

Your browser opens VS Code Server — same keybindings, same extensions, same integrated terminal as local VS Code. Open any course repo and start editing.

3

Pair with Claude

Run `claude` in the integrated terminal. The Claude Code CLI is authenticated and scoped to your course repo — use it to explore, plan, refactor, or write code.

4

Ship

Commit and push with git. For courses with Dockerfile or K8s exercises, `docker build` / `kubectl apply` work out of the box.

5

Shutdown

Labs auto-shutdown after 15 minutes of idle or 4 hours max session. Your workspace persists to disk and is restored on next launch.

Hardware & Environment

Machine Type n1-standard-2 (2 vCPU, 7.5 GB RAM)
Storage 60 GB SSD persistent disk (workspace persists across sessions)
GPU None (CPU-only — use GPU Training Lab for model training)
OS Ubuntu 22.04 with VS Code Server 4.x
Pre-installed Claude Code CLI · Node.js 20 · Python 3.11 · Docker · Git · GitHub CLI
Idle Timeout 15 min idle auto-shutdown, 4 hour max session

Pre-installed Tools

VS Code (code-server) Claude Code CLI pre-installed Integrated terminal Git + GitHub CLI Node.js 20 + Python 3.11 Docker + Docker Compose

Used In Courses

Frequently asked questions about this lab

What is the Code Server Lab? +
Full-featured VS Code IDE in your browser — with integrated terminal, file tree, git, extensions, and everything needed for real software engineering work. Pre-loaded with Claude Code CLI, Node.js, Python 3.11, Docker, and Git.
Which courses use this lab? +
This lab is included in: MLOps & AI Infrastructure, Data Engineering for AI, Claude Code — AI-Assisted Development, AI Solutions Architect: Design, Build & Lead Enterprise AI Systems.
What hardware does this lab run on? +
Browser-based VS Code on Google Cloud. Machine Type: n1-standard-2 (2 vCPU, 7.5 GB RAM); Storage: 60 GB SSD persistent disk (workspace persists across sessions); GPU: None (CPU-only — use GPU Training Lab for model training); OS: Ubuntu 22.04 with VS Code Server 4.x.
What software comes pre-installed? +
Comes pre-loaded with VS Code (code-server), Claude Code CLI pre-installed, Integrated terminal, Git + GitHub CLI, Node.js 20 + Python 3.11, Docker + Docker Compose. No local installs or dependency setup required — open your browser and start working.
What happens when I am idle in the lab? +
15 min idle auto-shutdown, 4 hour max session. Your files and notebooks persist on disk and will be available the next time you launch the lab.
Can I bring my own datasets and code into this lab? +
Yes. Datasets can be uploaded directly or synced from Google Cloud Storage. Notebooks and source files have built-in Git integration so you can push work to your own GitHub or GitLab repos.
Do I need to enroll in a course to use this lab? +
Yes. Lab environments are provisioned per-student as part of an AI Labs course enrollment. Browse the courses linked above to find programs that include this lab.

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Ready to Try This Lab?

Enroll in a course that uses this lab, or visit our Houston center for a hands-on demo.

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