kedro-mlflow

library2.0.3pypypi✓ verified 85d ago

A Kedro plugin that integrates MLflow for experiment tracking, model registry, and pipeline logging. Version 2.0.2 supports Kedro >=1.0.0 and MLflow >=3.0.0 (dropped support for MLflow 2.x). Released roughly every few months.

ai-ml devops workflow

pip install kedro-mlflow

github ↗package ↗docs ↗

INSTALL

IMPORT

SIG · KEDRO-MLFLOW

kedro-mlflow

ai-mlpythonv2.0.3

Install

41.5s avg

Import

—

Disk

816MB

Pass rate

5/ 10

Env Coverage5 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v2.0.3 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.95 runs

build_error

glibc

py 3.10–3.95 runs

installs and imports cleanly · install 41.5s · import 0.000s · 780MB

816MB installed

● package 816MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

mlflow.yml config

✓ kedro mlflow init

Configuration is done via CLI command, not direct import.

MlflowModelTrackingDataset

✓ from kedro_mlflow.io.models import MlflowModelTrackingDataset

✗ from kedro_mlflow.io import MlflowModelTrackingDataset

Common mistake: using wrong subpackage path.

Basic usage: configure Kedro project, create a Kedro session, and save a model using MlflowModelTrackingDataset.

from pathlib import Path from kedro.framework.project import configure_project configure_project(Path.cwd().name) from kedro.framework.session import KedroSession from kedro_mlflow.io.models import MlflowModelTrackingDataset import mlflow with KedroSession.create() as session: context = session.load_context() # Example: log a model with MlflowModelTrackingDataset data_set = MlflowModelTrackingDataset( filepath="model.pkl", flavor="mlflow.sklearn", model_name="test_model", save_args={"registered_model_name": "test_model"} ) # simulate using the dataset import pandas as pd data = pd.DataFrame({"a": [1, 2], "b": [3, 4]}) data_set.save(data) print("Model saved to MLflow.")

kedro --version

Debug

Known issues

breakingv2.0.0 dropped support for MLflow <3.0.0 and Kedro <1.0.0. If upgrading from v1.x, you must upgrade both Kedro and MLflow.

fix

Update Kedro to >=1.0.0 and MLflow to >=3.0.0. See migration guide.

affects: >=2.0.0

breakingv2.0.0 removed the `run_id` argument from `MlflowModelTrackingDataset`. Use `load_args={"model_uri": "models:/<model_name>/<version>"}` instead.

fix

Replace `run_id` in dataset instantiation with `load_args` containing `model_uri`.

affects: >=2.0.0

gotchaOn Databricks, autologging is enabled by default and conflicts with kedro-mlflow. You must disable autologging in mlflow.yml.

fix

Set `tracking.disable_tracking.disable_autologging: true` in mlflow.yml.

affects: all

gotchaMLflow thread-safety can cause tracking to be lost if nodes run in parallel. The plugin reopens the run before each node, but custom logging outside nodes may be lost.

fix

Ensure all MLflow logging happens inside Kedro node functions or callbacks that the plugin manages.

affects: all

Errors

Common errors & fixes

ModuleNotFoundError: No module named 'kedro_mlflow'

kedro-mlflow not installed in the current environment.

fix

pip install kedro-mlflow

ImportError: cannot import name 'MlflowModelTrackingDataset' from 'kedro_mlflow.io'

The correct import path is from kedro_mlflow.io.models.

fix

Use `from kedro_mlflow.io.models import MlflowModelTrackingDataset`

mlflow.exceptions.MlflowException: Unsupported model URI scheme

Using an incorrect model URI format in load_args in MLflow 3.x.

fix

Use `load_args={"model_uri": "models:/<model_name>/<version>"}` instead of `run_id`.

Upgrade

Version history

2.0.3latest on PyPI · released May 10, 2026

Audit

Dependencies

kedrorequiredCore framework dependency; requires >=1.0.0

mlflowrequiredML tracking and model registry; requires >=3.0.0

Agent activity

7 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/Galileo-Galilei/kedro-mlflow ↗

packagehttps://pypi.org/project/kedro-mlflow/ ↗

docshttps://kedro-mlflow.readthedocs.io/en/stable/ ↗