Experiment tracking# Experiment tracking Configure mlflow inside your project Context: mlflow tracking under the hood The mlflow.yml file Configure the tracking server Configure the tracking and registry uri Configure the credentials Default credentials with environment variables Authentication with expiring tokens Deactivate tracking under conditions Configure mlflow experiment Configure the run Extra tracking configuration Configure the user interface Overwrite configuration at runtime Track parameters Automatic parameters tracking Frequently asked questions Track Datasets as artifacts What is artifact tracking? How to track data in a kedro project? Frequently asked questions Track models What is model tracking? How to track models using MLflow in Kedro project? Frequently asked questions How can I save model locally and log it in MLflow in one step? Track metrics What is metric tracking? How to version metrics in a kedro project? Saving a single float as a metric with MlflowMetricDataset Saving the evolution of a metric during training with MlflowMetricHistoryDataset Saving several metrics with their entire history with MlflowMetricsHistoryDataset How to return metrics from a node? Visualise experiments Open the mlflow UI The mlflow user interface The kedro-mlflow helper Interactive use How to use kedro-mlflow in a notebook Reminder on mlflow’s limitations with interactive use Setup mlflow configuration in your notebook Difference with running through the CLI Guidelines and best practices suggestions
