pulumi-datarobot 0.10.30

A Pulumi package for creating and managing DataRobot resources.

DataRobot Pulumi Provider for Python

The DataRobot Resource Provider lets you manage DataRobot resources with Pulumi Infrastructure as Code.

Installation

Install the package using pip:

pip install pulumi_datarobot

Configuration

Configure the provider using environment variables or Pulumi config:

# Environment variables
export DATAROBOT_API_TOKEN=your_api_token
export DATAROBOT_ENDPOINT=https://your.datarobot.instance/api/v2

# OR using Pulumi config
pulumi config set datarobot:apikey --secret your_api_token
pulumi config set datarobot:endpoint https://your.datarobot.instance/api/v2

Quick Start

import pulumi
import pulumi_datarobot as dr

# Create a DataRobot use case
use_case = dr.UseCase("my-use-case",
    name="ML Project Use Case",
    description="Created with Pulumi")

# Create a project from a dataset
project = dr.Project("my-project",
    name="Customer Churn Prediction",
    dataset_url="https://s3.amazonaws.com/datarobot-datasets/churn.csv",
    use_case_id=use_case.id)

# Create a deployment
deployment = dr.Deployment("my-deployment",
    project_id=project.id,
    model_id=project.recommended_model_id,
    environment_id="your-prediction-environment-id")

# Export important values
pulumi.export("use_case_id", use_case.id)
pulumi.export("project_id", project.id)
pulumi.export("deployment_id", deployment.id)

Examples

Complete examples are available in the examples directory.

Air-Gapped Environments

For air-gapped deployments:

1. Store state locally

pulumi login --local

2. Install Python dependencies offline

Create wheel directory and download packages:

mkdir wheels
pip wheel pulumi-datarobot -w wheels/
tar cf wheels.tar wheels/

Transfer wheels.tar to your air-gapped system, then install:

tar xf wheels.tar
pip install wheels/* -f wheels/ --no-index

3. Download DataRobot plugin manually

Download the plugin binary from the releases page:

# Replace v0.10.30 with your version, e.g., v0.10.14
pulumi plugin install resource datarobot v0.10.30 --server \
  https://github.com/datarobot-community/pulumi-datarobot/releases/v0.10.30/

4. Skip update checks

export PULUMI_SKIP_UPDATE_CHECK=true

Advanced Usage

Custom Authentication

import pulumi_datarobot as dr

# Using API token credential
api_token = dr.ApiTokenCredential("my-token",
    name="Production API Token",
    api_token="your-secure-token")

# Using basic authentication
basic_auth = dr.BasicCredential("my-basic-auth",
    name="Basic Auth Credential",
    username="your-username",
    password="your-password")

Working with Models

# Register a custom model
custom_model = dr.CustomModel("my-custom-model",
    name="Customer Segmentation Model",
    target_type="Regression",
    target_name="revenue",
    description="Custom model for customer revenue prediction")

# Create a registered model from leaderboard
registered_model = dr.RegisteredModelFromLeaderboard("my-registered-model",
    project_id=project.id,
    model_id="model-id-from-leaderboard",
    name="Best Performing Model")

Resources

• DataRobot Platform
• Pulumi Documentation
• Provider Documentation
• Report Issues

Version

Package version: v0.10.30