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Get started with MLflow experiments

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This article gives an overview of how to use MLflow in Azure Databricks to automatically log training runs and track parameters, metrics, and models. For more details about using MLflow to track model development, see Track ML and deep learning training runs.

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

With Databricks Runtime 10.4 LTS ML and above, Databricks Autologging is enabled by default and automatically captures model parameters, metrics, files, and lineage information when you train models from a variety of popular machine learning libraries.

With Databricks Runtime 9.1 LTS ML, 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.

TensorFlow

Note

Keras models are also supported in mlflow.tensorflow.autolog().

# Also autoinstruments tf.keras
import mlflow.tensorflow
mlflow.tensorflow.autolog()

XGBoost

import mlflow.xgboost
mlflow.xgboost.autolog()

LightGBM

import mlflow.lightgbm
mlflow.lightgbm.autolog()

scikit-learn

import mlflow.sklearn
mlflow.sklearn.autolog()

PySpark

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 using the Experiment Runs sidebar. See View notebook experiment for instructions on how to view the experiment, run, and notebook revision used in the quickstart.

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

scikit-learn

 import mlflow.sklearn
 mlflow.sklearn.log_model(model, "myModel")

PySpark

 import mlflow.spark
 mlflow.spark.log_model(model, "myModel")

XGBoost

 import mlflow.xgboost
 mlflow.xgboost.log_model(model, "myModel")

TensorFlow

 import mlflow.tensorflow
 mlflow.tensorflow.log_model(model, "myModel")

Keras

 import mlflow.keras
 mlflow.keras.log_model(model, "myModel")

PyTorch

 import mlflow.pytorch
 mlflow.pytorch.log_model(model, "myModel")

SpaCy

 import mlflow.spacy
 mlflow.spacy.log_model(model, "myModel")

Other artifacts (files)

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

Example notebooks

Note

With Databricks Runtime 10.4 LTS ML and above, Databricks Autologging is enabled by default, and the code in these example notebooks is not required. The example notebooks in this section are designed for use with Databricks Runtime 9.1 LTS ML.

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 quickstart Python notebook

Get notebook

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 quickstart Python notebook

Get notebook

Learn more