mlflow-oidc-auth
OpenID Connect (OIDC) authentication and authorization plugin for MLflow.
This plugin allows you to use OIDC for user management in MLflow, enabling single sign-on (SSO) capabilities and centralized user management.
Disclaimer
This project is not affiliated with, endorsed by, or sponsored by the MLflow Project, Databricks, the Linux Foundation, or LF Projects, LLC. MLflow and related marks are trademarks of their respective owners. Maintained by Kharkevich Engineering Lab.
Features
- OIDC-based authentication for MLflow UI and API
- User management through OIDC provider
- User-level access control
- Group-based access control
- Permissions management based on regular expressions (allows or denies access to specific MLflow resources based on regular expressions and assigns permissions to users or groups)
- Support for session, JWT, and basic authentication methods
- Compatible with mlflow-client (basic auth)
Documentation
For detailed documentation, please refer to the docs. AI generated documentation is available at DeepWiki.
Quick Start
To get the full version (with entire MLflow and all dependencies), run:
python3 -m venv venv
source venv/bin/activate
python3 -m pip install mlflow-oidc-auth[full]
mlflow server --app-name oidc-auth --host 0.0.0.0 --port 8080
Webhook secret encryption key 🔐
Webhook secrets are stored encrypted in the database using a Fernet key. If you plan to use MLflow webhooks with secrets, set the encryption key in the environment variable MLFLOW_WEBHOOK_SECRET_ENCRYPTION_KEY before creating any webhooks. Generate a key with:
MLFLOW_WEBHOOK_SECRET_ENCRYPTION_KEY=$(python -c "from cryptography.fernet import Fernet; print(Fernet.generate_key().decode())")
export MLFLOW_WEBHOOK_SECRET_ENCRYPTION_KEY
Important: keep this key stable across application restarts and replicas. If the key is lost or changed after webhooks are created, previously stored secrets cannot be decrypted and will cause webhook listing to fail until you restore the original key or remove/rotate the affected webhook secrets.
Development
For development quick start, please refer to the Development and Contribution section. Contribution guidelines are available in CONTRIBUTING.md.
License
Apache 2 Licensed. For more information, please see LICENSE.
Based on MLflow basic-auth plugin
https://github.com/mlflow/mlflow/tree/master/mlflow/server/auth
1 maintainer
Release files for mlflow-oidc-auth 7.20.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| mlflow_oidc_auth-7.20.0.tar.gz | 1.2 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| mlflow_oidc_auth-7.20.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 2.6 MB
Release files / mlflow_oidc_auth-7.20.0.tar.gz
| Download URL | mlflow_oidc_auth-7.20.0.tar.gz |
|---|---|
| Size | 1.2 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Transparency logRelease files / mlflow_oidc_auth-7.20.0-py3-none-any.whl
| Download URL | mlflow_oidc_auth-7.20.0-py3-none-any.whl |
|---|---|
| Size | 1.4 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Sep 24, 2026.
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