MLflow examples

Quick Start example

• quickstart/mlflow_tracking.py is a basic example to introduce MLflow concepts.

Tutorials

Various examples that depict MLflow tracking, project, and serving use cases.

• h2o depicts how MLflow can be use to track various random forest architectures to train models for predicting wine quality.
• hyperparam shows how to do hyperparameter tuning with MLflow and some popular optimization libraries.
• keras modifies a Keras classification example and uses MLflow's mlflow.keras.autolog() API to automatically log metrics and parameters to MLflow during training.
• multistep_workflow is an end-to-end of a data ETL and ML training pipeline built as an MLflow project. The example shows how parts of the workflow can leverage from previously run steps.
• pytorch uses CNN on MNIST dataset for character recognition. The example logs TensorBoard events and stores (logs) them as MLflow artifacts.
• remote_store has a usage example of REST based backed store for tracking.
• r_wine demonstrates how to log parameters, metrics, and models from R.
• sklearn_elasticnet_diabetes uses the sklearn diabetes dataset to predict diabetes progression using ElasticNet.
• sklearn_elasticnet_wine_quality is an example for MLflow projects. This uses the Wine Quality dataset and Elastic Net to predict quality. The example uses MLproject to set up a Conda environment, define parameter types and defaults, entry point for training, etc.
• sklearn_logisic_regression is a simple MLflow example with hooks to log training data to MLflow tracking server.
• tensorflow contains end-to-end one run examples from train to predict for both TensorFlow 1.X and 2.0. It includes usage of MLflow's mlflow.tensorflow.autolog() API, which captures TensorBoard data and logs to MLflow with no code change.
• docker demonstrates how to create and run an MLflow project using docker (rather than conda) to manage project dependencies
• fastai modifies a fastai classification example and highlights MLflow's mlflow.fastai.autolog() API to track parameters, metrics, and artifacts while training a simple MNIST model.