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.
In this section:
- Install MLflow
- Automatically log training runs to MLflow
- View results
- Track additional metrics, parameters, and models
- Example notebooks
- Learn more
Automatically log training runs to MLflow
With Databricks Runtime 10.3 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 10.2 and below, 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.
Keras models are also supported in
# Also autoinstruments tf.keras import mlflow.tensorflow mlflow.tensorflow.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.
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.
import mlflow mlflow.log_metric("accuracy", 0.9)
import mlflow mlflow.log_param("learning_rate", 0.001)
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")
With Databricks Runtime 10.3 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 10.2 ML and below.
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
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.