ptional stack components provided by integrations:Experiment Tracker Flavor Integration Notes Comet comet comet Add Comet experiment tracking and visualization capabilities to your ZenML pipelines MLflow mlflow mlflow Add MLflow experiment tracking and visualization capabilities to your ZenML pipelines Neptune neptune neptune Add Neptune experiment tracking and visualization capabilities to your ZenML pipelines Weights & Biases wandb wandb Add Weights & Biases experiment tracking and visualization capabilities to your ZenML pipelines Custom Implementation custom custom

If you would like to see the available flavors of Experiment Tracker, you can use the command:

zenml experiment-tracker flavor list

How to use it

Every Experiment Tracker has different capabilities and uses a different way of logging information from your pipeline steps, but it generally works as follows:

first, you have to configure and add an Experiment Tracker to your ZenML stack

next, you have to explicitly enable the Experiment Tracker for individual steps in your pipeline by decorating them with the included decorator

in your steps, you have to explicitly log information (e.g. models, metrics, data) to the Experiment Tracker same as you would if you were using the tool independently of ZenML

finally, you can access the Experiment Tracker UI to browse and visualize the information logged during your pipeline runs. You can use the following code snippet to get the URL of the experiment tracker UI for the experiment linked to a certain step of your pipeline run:

from zenml.client import Client

pipeline_run = Client().get_pipeline_run("<PIPELINE_RUN_NAME>")

step = pipeline_run.steps["<STEP_NAME>"]

experiment_tracker_url = step.run_metadata["experiment_tracker_url"].value

Experiment trackers will automatically declare runs as failed if the corresponding ZenML pipeline step fails.