dagster-mlflow
Package for mlflow Dagster framework components.
Decision gist · record as of 2026-08-14
Yes. The package is actively maintained, has no known vulnerabilities, and low install friction. It is worth installing if you are already using both Dagster and MLflow and want to unify experiment tracking with your data orchestration. If you use only one of these tools, it adds no value.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later (supports up to 3.14); dagster and mlflow must be installed as runtime dependencies.
- Low friction install with a pure Python wheel.
- Active maintenance with a release on 2026-08-14 and 15996 GitHub stars indicate a well-maintained project.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 licensed under a permissive license, allowing use in commercial and proprietary projects with minimal restrictions beyond attribution.
last release 2026-08-14 (0 days) · last repo commit 2026-08-14 · 15,996 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 144,479 downloads/mo, #11,151 on PyPI
Alternatives
Verify before relying
pip install dagster-mlflow
import dagster as dg
from dagster_mlflow import mlflow_tracking
@dg.asset
@mlflow_tracking
def my_model():
return trained_model- Specific MLflow version compatibility constraints beyond the runtime dependency declaration.
- Whether the integration supports all MLflow tracking APIs or a subset of common operations.
- Performance overhead of the integration when logging large model artifacts or many experiments.
What it is and what it does
dagster-mlflow is a library that bridges MLflow's experiment tracking and model registry with Dagster's asset-oriented orchestration framework. It allows you to instrument Dagster assets with MLflow tracking decorators so that model training runs, metrics, parameters, and artifacts are automatically logged to MLflow while remaining part of your Dagster lineage graph. This is useful when you want to track ML experiments and models alongside your data pipelines, keeping both the orchestration and the ML metadata in one place.
The package depends on dagster, mlflow, pandas, and protobuf. It is maintained as part of the main Dagster project and receives regular updates. The integration is designed to work within Dagster's declarative asset model, so you define your ML workflows as Dagster assets and use the integration to connect them to MLflow's tracking backend.
Use it for
- Log model training metrics and parameters to MLflow while orchestrating the training job as a Dagster asset.
- Track model artifacts and versions in MLflow as outputs of Dagster asset runs.
- Build reproducible ML pipelines where experiment tracking is integrated into the asset lineage and observability.
- Manage model promotion and versioning through MLflow's registry while keeping orchestration logic in Dagster.
- Monitor ML experiment runs alongside data pipeline execution in a unified Dagster web UI.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package is actively maintained, has no known vulnerabilities, and low install friction. It is worth installing if you are already using both Dagster and MLflow and want to unify experiment tracking with your data orchestration. If you use only one of these tools, it adds no value.
Install
dagster-mlflow on PyPI
Before you install
Low friction install with a pure Python wheel. Active maintenance with a release on 2026-08-14 and 15996 GitHub stars indicate a well-maintained project. Depends on dagster, mlflow, pandas, and protobuf—all stable, widely-used packages.
Requires Python 3.10 or later (supports up to 3.14); dagster and mlflow must be installed as runtime dependencies.
License in practice
Apache-2.0 licensed under a permissive license, allowing use in commercial and proprietary projects with minimal restrictions beyond attribution.
Quickstart
pip install dagster-mlflow
import dagster as dg
from dagster_mlflow import mlflow_tracking
@dg.asset
@mlflow_tracking
def my_model():
return trained_modelVerify before relying
- Specific MLflow version compatibility constraints beyond the runtime dependency declaration.
- Whether the integration supports all MLflow tracking APIs or a subset of common operations.
- Performance overhead of the integration when logging large model artifacts or many experiments.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release <3.15,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagesdagstermlflowpandasprotobuf |
| Maintenance | Actively maintained 0 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 144,479 / month, #11,151 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: dagster_mlflow-0.29.18-py3-none-any.whl
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See also dagster · dagster-dbt · dagster-dg-core · dagster-cloud-cli · dagster-docker · dagster-webserver · dagster-pyspark · whylogs · mlflow · mlflow-skinny