dagster-mlflow

library0.29.3pypypi✓ verified 85d ago

Dagster integration with MLflow, enabling tracking of ML experiments, models, and parameters within Dagster pipelines. Current version 0.29.3, supports Python >=3.10,<3.15. Releases follow Dagster core release cadence (approximately bi-weekly).

workflow ai-ml observability

pip install dagster-mlflow

github ↗package ↗homepage ↗

INSTALL

IMPORT

SIG · DAGSTER-MLFLOW

dagster-mlflow

workflowpythonv0.29.3

harness data pending

Install & Compatibility

Where this runs

No compatibility data collected yet for this library.

Code

Verified usage

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

mlflow_tracking

✓ from dagster_mlflow import mlflow_tracking

Standard import for the MLflow tracking resource

EndTimeLoggedRun

✓ from dagster_mlflow import EndTimeLoggedRun

Context manager for logging run end times

MLflowRunContext

✓ from dagster_mlflow import MLflowRunContext

Type alias for the MLflow run context

Minimal working example: defines an op logging params/metrics, a resource wrapping MLflow tracking, and a job to run it.

from dagster import job, op, resource from dagster_mlflow import mlflow_tracking @op(required_resource_keys={'mlflow'}) def train_model(context): mlflow = context.resources.mlflow mlflow.log_param('epochs', 10) mlflow.log_metric('accuracy', 0.95) @resource(config_schema={'experiment_name': str}) def mlflow_resource(init_context): import mlflow mlflow.set_experiment(init_context.resource_config['experiment_name']) mlflow.start_run() yield mlflow mlflow.end_run() @job(resource_defs={'mlflow': mlflow_resource}) def my_ml_job(): train_model() if __name__ == '__main__': my_ml_job.execute_in_process( run_config={ 'resources': { 'mlflow': { 'config': {'experiment_name': 'demo'} } } } )

Debug

Known issues

gotchaThe `mlflow_tracking` resource is deprecated in favor of manually creating an MLflow resource using `dagster_mlflow.resources.mlflow_resource`. Do not use `mlflow_tracking` in new code.

fix

Use `from dagster_mlflow.resources import mlflow_resource` and configure as a resource.

affects: 0.28.0+

breakingIn dagster-mlflow 0.28.0+, the `mlflow_run` context manager changed signature. Old usage `with mlflow_run(context) as run:` no longer works; use `EndTimeLoggedRun`.

fix

Replace with `from dagster_mlflow import EndTimeLoggedRun` and use `with EndTimeLoggedRun(context, mlflow_run=context.resources.mlflow):`

affects: >=0.28.0

deprecatedThe `MLflowRunContext` type alias may be removed in future versions. Consider using `context.resources.mlflow` directly.

fix

Directly access `context.resources.mlflow` instead of relying on `MLflowRunContext`.

affects: all

Errors

Common errors & fixes

ModuleNotFoundError: No module named 'dagster_mlflow'

dagster-mlflow not installed or wrong Python environment.

fix

Run `pip install dagster-mlflow` in the correct environment.

AttributeError: 'mlflow_tracking' object has no attribute 'log_param'

Using the deprecated `mlflow_tracking` resource incorrectly or mixing old and new APIs.

fix

Define your own MLflow resource as shown in the quickstart, or use `from dagster_mlflow.resources import mlflow_resource`.

Upgrade

Version history

0.29.3latest on PyPI · released Apr 30, 2026

Audit

Dependencies

dagsterrequiredCore Dagster framework required

mlflowrequiredMLflow tracking integration

Agent activity

30 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/dagster-io/dagster ↗

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

homepagehttps://dagster.io ↗