Attach metadata to an artifact

Learn how to log metadata for artifacts and models in ZenML.

Metadata plays a critical role in ZenML, providing context and additional information about various entities within the platform. Anything which is metadata in ZenML can be compared in the dashboard.

This guide will explain how to log metadata for artifacts and models in ZenML and detail the types of metadata that can be logged.

Logging Metadata for Artifacts

Artifacts in ZenML are outputs of steps within a pipeline, such as datasets, models, or evaluation results. Associating metadata with artifacts can help users understand the nature and characteristics of these outputs.

To log metadata for an artifact, you can use the log_artifact_metadata method. This method allows you to attach a dictionary of key-value pairs as metadata to an artifact. The metadata can be any JSON-serializable value, including custom classes such as Uri, Path, DType, and StorageSize. Find out more about these different types here.

Here's an example of logging metadata for an artifact:

from zenml import step, log_artifact_metadata
from zenml.metadata.metadata_types import StorageSize

def process_data_step(dataframe: pd.DataFrame) ->  Annotated[pd.DataFrame, "processed_data"],:
    """Process a dataframe and log metadata about the result."""
    # Perform processing on the dataframe...
    processed_dataframe = ...

    # Log metadata about the processed dataframe
            "row_count": len(processed_dataframe),
            "columns": list(processed_dataframe.columns),
            "storage_size": StorageSize(processed_dataframe.memory_usage().sum())
    return processed_dataframe

Fetching logged metadata

Once metadata has been logged in an artifact, or step, we can easily fetch the metadata with the ZenML Client:

from zenml.client import Client

client = Client()
artifact = client.get_artifact_version("my_artifact", "my_version")


Grouping Metadata in the Dashboard

When logging metadata passing a dictionary of dictionaries in the metadata parameter will group the metadata into cards in the ZenML dashboard. This feature helps organize metadata into logical sections, making it easier to visualize and understand.

Here's an example of grouping metadata into cards:

from zenml.metadata.metadata_types import StorageSize

        "model_metrics": {
            "accuracy": 0.95,
            "precision": 0.92,
            "recall": 0.90
        "data_details": {
            "dataset_size": StorageSize(1500000),
            "feature_columns": ["age", "income", "score"]

In the ZenML dashboard, "model_metrics" and "data_details" would appear as separate cards, each containing their respective key-value pairs.

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