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kindofluke
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pulumi_infra

Automate DataRobot infrastructure deployment with Pulumi stacks.

Updated Mar 18, 2026
Search Tags:#deployment#infrastructure#pulumi#datarobot#custommodel#predictionenvironment
One-click install
npx skills add https://github.com/kindofluke/albatross-data --skill pulumi-infra
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pulumi_infra
Source: https://github.com/kindofluke/albatross-data/tree/main/.vibe/skills/pulumi_infra
Command: npx skills add https://github.com/kindofluke/albatross-data --skill pulumi-infra

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill enables teams to deploy and manage DataRobot resources (CustomModels, PredictionEnvironments, RegisteredModels, Deployments) as Pulumi infrastructure, ensuring reproducible and version-controlled deployments within the repo.

Core Features & Use Cases

  • Provides a minimal infra package (infra/) with an entry point deploy.py and helper modules (infra/init.py, agent_infra.py, utils.py) to orchestrate Pulumi deployments.
  • Demonstrates a complete deployment stack including runtime configuration, UseCase, CustomModel deployment, PredictionEnvironment, and CustomModelDeployment integration with DataRobot resources.
  • Supports upgrading and modifying resources by editing deploy.py and infra/ modules, then running Pulumi to provision changes.

Quick Start

Run Pulumi to provision the infrastructure defined in deploy.py (in the infra folder) for this Skill.

Frequently Asked Questions about pulumi_infra

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I deploy DataRobot infrastructure using Pulumi?▼

You deploy DataRobot infrastructure using Pulumi by running Pulumi to provision resources defined in the infra folder's deploy.py file. This orchestrates CustomModels, PredictionEnvironments, RegisteredModels, and Deployments reproducibly.

What is the best way to manage DataRobot CustomModels and PredictionEnvironments as code?▼

The best way to manage DataRobot CustomModels and PredictionEnvironments as code is by defining them within a Pulumi infrastructure stack. This ensures reproducible, version-controlled deployments integrated directly within your repository.

Can I modify existing DataRobot deployments with Pulumi?▼

Yes, you can modify existing DataRobot deployments with Pulumi by editing the deploy.py file and helper modules in the infra package. Running Pulumi subsequently provisions the upgrades and applies the changes to your resources.

Do I need a specific folder structure to deploy DataRobot resources with Pulumi?▼

Yes, deploying DataRobot resources with Pulumi requires a minimal infra package containing an entry point deploy.py and helper modules like infra/__init__.py, agent_infra.py, and utils.py to orchestrate the infrastructure stack.

What components are included in a Pulumi DataRobot deployment stack?▼

A Pulumi DataRobot deployment stack includes runtime configuration, a UseCase, CustomModel deployment, PredictionEnvironment, and CustomModelDeployment integration. These components are orchestrated through Pulumi and DataRobot utility modules.

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