Nebari MLflow Plugin

Table of Contents

• Features
• Installation
• Usage
• License

Introduction

This MLflow extension supports "Azure", "GCP", and "local" Nebari deployments, and aims to eventually support Nebari deployments on "AWS" and "existing" k8s clusters as well. It provides a robust, collaborative environment for AI/ML professionals to manage experiments, track metrics, and deploy models.

Features

Centralized Artifact Repository: Store and manage all your metrics, parameters, and artifacts in a single location, accessible across the multi-tenant platform.

Experiment Tracking: Log, query, and visualize metrics to understand and compare different runs and models.

Automated Configuration: Simply type import mlflow in your Python script, and you're already configured to communicate with the remote multi-tenant MLflow server—no additional setup required.

Installation

Prerequisites:

• Nebari must be deployed using the Azure, GCP, or local provider
• Nebari version 2024.10.1 or later

Installing the MLflow extension is as straightforward as installing a Python package. Run the following commands:

git clone nebari-mlflow-plugin
cd nebari-mlflow-plugin
pip install nebari-mlflow-plugin

This command installs the Python package and also creates the necessary infrastructure to run MLflow on the AI Platform.

Configuration

After installation, the MLflow extension is automatically configured to work with the AI Platform. To access the MLflow interface, navigate to https://[your-nebari-domain]/mlflow.

For Azure, your app registration will need RBAC permissions in addition to the typical Contributor permissons. We recommend you create a custom role scoped at the resource_group (usually named "<project_name>-<namespace>" where the values are what you set in nebari-config.yaml), and add the following permissions:

• Microsoft.Authorization/roleAssignments/read
• Microsoft.Authorization/roleAssignments/write
• Microsoft.Authorization/roleAssignments/delete

Then create a role assignment of that role to the nebari app registration service principal.

For GCP, your service account will need additional IAM permissions beyond the standard roles. The service account used for Nebari deployment requires the Service Account Admin role to manage workload identity bindings.

Add this role using the gcloud CLI:

gcloud projects add-iam-policy-binding YOUR_PROJECT_ID \
  --member="serviceAccount:YOUR_SERVICE_ACCOUNT@YOUR_PROJECT_ID.iam.gserviceaccount.com" \
  --role="roles/iam.serviceAccountAdmin"

Or via the GCP Console:

1. Navigate to the IAM & Admin page in the Google Cloud Console
2. Find your Nebari service account in the list
3. Click the pencil icon to edit permissions
4. Click "ADD ANOTHER ROLE"
5. Search for and select "Service Account Admin"
6. Click "SAVE"

This role includes the iam.serviceAccounts.setIamPolicy permission required for the MLflow plugin to create workload identity bindings.

Configuring MLflow Tracking URL

You may set the MLFLOW_TRACKING_URL to configure mlflow in individual users' Nebari instances by adding or updating an additional block in your Nebari configuration file. Be sure to replace {project_name} and {namespace} with the values from your own nebari config file e.g. http://mynebari-mlflow-tracking.dev.svc:5000.

jupyterhub:
  overrides:
    singleuser:
      extraEnv:
        MLFLOW_TRACKING_URI: "http://{project_name}-mlflow-tracking.{namespace}.svc:5000" 

Helm Chart Overrides

You can pass custom Helm chart values to override the default MLflow configuration. This is useful for enabling specific features like the MLflow run server or customizing resource limits. The overrides are specified in your Nebari configuration file under the mlflow section.

mlflow:
  enabled: true
  overrides:
    tracking:
      resources:
        limits:
          memory: "4Gi"
          cpu: "2"
    # ... additional overrides

Usage

Getting started with the MLflow extension is incredibly simple. To track an experiment:

Navigate to the MLFLow extension URL and create a new experiment. In your Python script, import MLflow and start logging metrics.

import mlflow

# Start an experiment
with mlflow.start_run() as run:
    mlflow.log_metric("accuracy", 0.9)
    mlflow.log_artifact("path/to/your/artifact")

With the above code, your metrics and artifacts are automatically stored and accessible via the MLFlow extension URL.

License

nebari-mlflow-plugin is distributed under the terms of the Apache license.