On this page
Seamlessly integrate Comet's powerful experiment tracking capabilities with your ZenML pipelines. Visualize metrics, models, and datasets from your automated MLOps workflows in Comet's intuitive UI, making it easy to monitor and share pipeline results across your team.
Features with ZenML
- Automatically log metrics, parameters, models, and more from ZenML steps to Comet experiments
- Easily enable Comet tracking in steps using the
@stepdecorator - Retrieve Comet experiment URLs for each pipeline run via ZenML metadata
- Organize experiments with automatic
pipeline_nameandpipeline_run_nametags - Configure additional experiment settings using
CometExperimentTrackerSettings
Main Features
- Interactive web-based UI to visualize and compare experiments
- Supports logging metrics, hyperparameters, datasets, models, and more
- Workspaces and projects to organize experiments across teams
- Extensive visualization and charting of tracked data
- Easy sharing of experiment results and insights
How to use ZenML with Comet
from zenml import step
@step(experiment_tracker="comet_tracker")
def my_step():
...
# go through some experiment tracker methods
experiment_tracker.log_metrics({"my_metric": 42})
experiment_tracker.log_params({"my_param": "hello"})
# or use the Experiment object directly
experiment_tracker.experiment.log_model(...)
# or pass the Comet Experiment object into helper methods
from comet_ml.integration.sklearn import log_model
log_model(
experiment=experiment_tracker.experiment,
model_name="SVC",
model=model,
)
...
