AI Engineering Platform

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Infrastructure and tooling for AI Engineering bootcamps, providing secure, isolated development environments and automated participant onboarding.

Overview

This platform consists of the following components:

  1. Coder Deployment - Containerized development environments supported by Coder
  2. Participant Onboarding System - Secure, automated participant onboarding

1. Coder Deployment for GCP

The coder folder contains all resources needed to deploy a Coder instance on Google Cloud Platform (GCP), along with reusable workspace templates and Docker images for the workspace environment.

Structure

  • deploy/ - Terraform scripts and startup automation for provisioning the Coder server on a GCP VM
  • docker/ - Dockerfiles and guides for building custom images used by Coder workspace templates
  • templates/ - Coder workspace templates for reproducible, containerized development environments on GCP

Usage

  1. Provision Coder on GCP - Follow the steps in coder/deploy/README.md
  2. Build and Push Docker Images - See coder/docker/README.md
  3. Push Workspace Templates - See coder/templates/README.md

2. Participant Onboarding System

Automated API key distribution for bootcamp participants using GCP Secret Manager and workload identity.

  • Secrets never written to disk — exported into the shell via eval "$(onboard ...)"
  • Workspace VMs authenticate directly to Secret Manager via workload identity (no Firebase, no token service)
  • Per-participant JSON secrets named {bootcamp-name}-env-{github-handle}
  • Firestore tracks participant records and onboarded status

Architecture

Admin Phase
  1. Setup participants/teams in Firestore
  2. Generate API keys (Gemini, Langfuse, etc.)
  3. onboard admin setup-secrets  →  creates per-participant Secret Manager secrets
  4. Grant workspace SA roles/secretmanager.secretAccessor
  5. Add users to GitHub org

Participant Phase
  1. eval "$(onboard --bootcamp-name <name>)"  in Coder workspace
  2. Fetches env vars from Secret Manager via workload identity
  3. Runs integration tests (env vars injected, nothing written to disk)
  4. Marks participant as onboarded in Firestore

Key dates

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  • Released:
Newer release available (0.7.4)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for vector-institute from gravatar.com vector-institute

Credits

Author: Vector AI Engineering

License expression

Apache-2.0
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Requires

Python >=3.12

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