mlflow-kubernetes-plugins 1.2.0

Kubernetes-backed WorkspaceProvider implementation and Kubernetes authorization plugin for MLflow workspaces.

MLflow Kubernetes Plugins

This repository packages two MLflow extensions for Kubernetes-backed deployments:

• a workspace provider that maps MLflow workspaces to Kubernetes namespaces
• an optional authorization plugin that enforces Kubernetes RBAC for MLflow requests

These plugins build on top of MLflow's 3.10 workspace support. If you are new to MLflow workspaces, start with the official guide: https://mlflow.org/docs/latest/self-hosting/workspaces/getting-started/. It covers the core MLflow server requirements, how workspace context is set by clients, and the upstream workspace lifecycle model.

Components

Entry point	MLflow hook	Purpose
kubernetes	mlflow.workspace_provider	Exposes Kubernetes namespaces as MLflow workspaces.
kubernetes-auth	mlflow.app	Wraps the MLflow server with Kubernetes-based authorization checks.

Install

Install from PyPI:

pip install mlflow-kubernetes-plugins

For local development:

pip install -e ".[dev]"

Quick Start

1. Enable MLflow workspaces on an MLflow server backed by a SQL store.
2. Install this package into the same environment as the MLflow server.
3. Configure the workspace provider and, if needed, the auth plugin.

export MLFLOW_K8S_WORKSPACE_LABEL_SELECTOR="mlflow-enabled=true"
export MLFLOW_K8S_DEFAULT_WORKSPACE="team-a"

mlflow server \
  --backend-store-uri postgresql://user:pass@localhost/mlflow \
  --default-artifact-root s3://mlflow-artifacts \
  --enable-workspaces \
  --workspace-store-uri "kubernetes://" \
  --app-name kubernetes-auth

Use --app-name kubernetes-auth only when you want request authorization enforced by Kubernetes RBAC.

Documentation

• docs/index.md: docs index
• docs/workspace-provider.md: workspace provider behavior, configuration, and startup
• docs/authorization-plugin.md: auth modes, headers, and request handling
• docs/kubernetes-rbac.md: RBAC requirements and example manifests
• config/crd/bases/mlflow.kubeflow.org_mlflowconfigs.yaml: generated MLflowConfig CRD manifest

Development

Run the main local checks from the repository root:

pip install -e ".[dev]"
make generate-k8s
ruff check .
pytest
python -m build