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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:
Prodigy annotation table

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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.

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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.