README.md

📊 Visualize statistics

This examples show-cases the built-in FacetStatisticsVisualizer using the Facets Overview integration. Facets is an awesome project that helps users visualize large amounts of data in a coherent way.

🗺 Overview

Here, we are using the Boston Housing Price Regression dataset. We create a simple pipeline that returns two pd.DataFrames, one for the training data and one for the test data. In the post-execution workflow we then plug in the visualization class that visualizes the statistics of these DataFrames for us.

This visualization is produced with the following code:

from zenml.post_execution import get_pipelines
from zenml.integrations.facets.visualizers.facet_statistics_visualizer import (
    FacetStatisticsVisualizer,
)

def visualize_statistics():
    pipe = get_pipelines()[-1]
    importer_outputs = pipe.runs[-1].get_step(step="importer")
    FacetStatisticsVisualizer().visualize(importer_outputs)

visualize_statistics()

It produces the following visualization:

🖥 Run it locally

⏩ SuperQuick statistics run

If you're really in a hurry and just want to see this example pipeline run without wanting to fiddle around with all the individual installation and configuration steps, just run the following:

zenml example run facets_visualize_statistics

👣 Step-by-Step

📄 Prerequisites

In order to run this example, you need to install and initialize ZenML:

# install CLI
pip install zenml

# install ZenML integrations
zenml integration install tensorflow facets

# pull example
zenml example pull facets_visualize_statistics
cd zenml_examples/facets_visualize_statistics

# Initialize ZenML repo
zenml init

# Start the ZenServer to enable dashboard access
zenml up

▶️ Run the Code

Now we're ready. Execute:

python run.py

Alternatively, if you want to run based on the config.yaml you can run with:

zenml pipeline run pipelines/facets_pipeline/facets_pipeline.py -c config.yaml

🧽 Clean up

In order to clean up, delete the remaining ZenML references.

rm -rf zenml_examples