Quick start Python

MLflow is an open source platform for managing the end-to-end machine learning lifecycle. MLflow provides simple APIs for logging metrics (for example, model loss), parameters (for example, learning rate), and fitted models, making it easy to analyze training results or deploy models later on.

Install MLflow

If you’re using Databricks Runtime for Machine Learning, MLflow is already installed. Otherwise, install the MLflow package from PyPI.

Automatically log training runs to MLflow

MLflow provides mlflow.<framework>.autolog() APIs to automatically log training code written in many ML frameworks. You can call this API before running training code to log model-specific metrics, parameters, and model artifacts.

# Also autoinstruments tf.keras
import mlflow.tensorflow
mlflow.tensorflow.autolog()
# Use import mlflow.tensorflow and mlflow.tensorflow.autolog() if using tf.keras
import mlflow.keras
mlflow.keras.autolog()
import mlflow.xgboost
mlflow.xgboost.autolog()
import mlflow.lightgbm
mlflow.lightgbm.autolog()
import mlflow.sklearn
mlflow.sklearn.autolog()

If performing tuning with pyspark.ml, metrics and models are automatically logged to MLflow. See Apache Spark MLlib and automated MLflow tracking

View results

After executing your machine learning code, you can view results in the Experiment Runs sidebar:

  1. Click the Experiment icon Experiment in the notebook context bar. The Experiments Runs sidebar displays. In the sidebar, you can view the run’s parameters and metrics:

    Runs
  2. Click the External Link icon External Link in the Experiment Runs context bar to view the experiment:

    View experiment
  3. In the experiment, click a date:

    Select run

    The run details display:

    Run details
  4. In the experiment, click a source:

    Experiment source

    The notebook revision used in the run displays:

    Notebook revision

Track additional metrics, parameters, and models

You can log additional information by directly invoking the MLflow Tracking logging APIs.

  • Numerical metrics:

    import mlflow
    mlflow.log_metric("accuracy", 0.9)
    
  • Training parameters:

    import mlflow
    mlflow.log_param("learning_rate", 0.001)
    
  • Models:

    import mlflow.sklearn
    mlflow.sklearn.log_model(model, "myModel")
    
    import mlflow.spark
    mlflow.spark.log_model(model, "myModel")
    
    import mlflow.xgboost
    mlflow.xgboost.log_model(model, "myModel")
    
    import mlflow.tensorflow
    mlflow.tensorflow.log_model(model, "myModel")
    
    import mlflow.keras
    mlflow.keras.log_model(model, "myModel")
    
    import mlflow.pytorch
    mlflow.pytorch.log_model(model, "myModel")
    
    import mlflow.spacy
    mlflow.spacy.log_model(model, "myModel")
    
  • Other artifacts (files):

    import mlflow
    mlflow.log_artifact("/tmp/my-file", "myArtifactPath")
    

Example notebooks

Requirements

Databricks Runtime 6.4 or above or Databricks Runtime 6.4 ML or above.

Notebooks

The recommended way to get started using MLflow tracking with Python is to use the MLflow autolog() API. With MLflow’s autologging capabilities, a single line of code automatically logs the resulting model, the parameters used to create the model, and a model score. The following notebook shows you how to set up a run using autologging.

MLflow Autologging Quick Start Python notebook

Open notebook in new tab

If you need more control over the metrics logged for each training run, or want to log additional artifacts such as tables or plots, you can use the MLflow logging API functions demonstrated in the following notebook.

MLflow Logging API Quick Start Python notebook

Open notebook in new tab