Prodigy

Integrate W&B with Prodigy to track annotation workflows, log training metrics, and manage labeled datasets.

Prodigy is an annotation tool for creating training and evaluation data for machine learning models, error analysis, and data inspection and cleaning. W&B Tables let you log, visualize, analyze, and share datasets (and more) inside W&B.

This guide shows you how to use the W&B integration with Prodigy to upload your Prodigy-annotated dataset directly to W&B so you can explore and share it as an interactive Table. Use this when you want to inspect annotation quality, compare versions of a labeled dataset, or share results with collaborators.

With a few lines of code, like these:

import wandb
from wandb.integration.prodigy import upload_dataset

with wandb.init(project="prodigy"):
    upload_dataset("news_headlines_ner")

you can produce visual, interactive, shareable tables like this one:

Quickstart

Use wandb.integration.prodigy.upload_dataset to upload your annotated Prodigy dataset directly from the local Prodigy database to W&B in the Table format. For more information about Prodigy, including installation and setup, see the Prodigy documentation.

When you upload a dataset, W&B automatically converts images and named entity fields to wandb.Image and wandb.Html respectively, so they render as interactive visualizations in your Table. W&B may add extra columns to the resulting table to include these visualizations.

Read through a detailed example

To see what’s possible with the integration, explore the Visualizing Prodigy Datasets Using W&B Tables report for example visualizations generated with the W&B Prodigy integration.