1. Clone the MLflow Pipelines template repo locally: git clone https://github.com/mlflow/mlp-regression-template.git.
2. Enter the root of the template: cd mlp-regression-template.
3. Install required packages: pip install mlflow-1.24.1.dev0-py3-none-any.whl && pip install -r requirements.txt

To log pipeline runs to a particular MLflow experiment,

1. Open profiles/databricks.yaml or profiles/local.yaml, depending on your running environment.
2. Uncomment the experiment section, specify the name of the experiment.

Sync this repo and run notebooks/databricks on an DBR 11.x cluster with workspace files support enabled.

Note We recommend to open at least 3 browser tabs to facilitate easier development:

• One tab for pipeline.yaml
• One tab for changing step function defined in steps/{step}.py
• One tab for the driver notebook (notebooks/databricks)

You should be able to find experiments and runs on the Databricks ML Experiments page.

Launch Jupyter Lab via command jupyter-lab Open notebooks/jupyter.ipynb under the current Python environment.

First cd to the template root directory. Then try the following MLflow commands to get started. Note that step_name is optional: running pipeline commands without specifying the step_name parameter will act on the entire pipeline.

mlflow pipelines --help
mlflow pipelines inspect --step step_name
mlflow pipelines run --step step_name
mlflow pipelines clean --step step_name

To check MLflow experiment and runs from pipeline execution, try the following command from the template root directory.

mlflow ui --backend-store-uri sqlite:///metadata/mlflow/mlruns.db --default-artifact-root ./metadata/mlflow/mlartifacts --host localhost -p 5001

Then open a browser tab pointing to http://127.0.0.1:5001