renumics-spotlight 1.7.3

Visualize and maintain datasets to develop and understand data-driven algorithms.

Renumics Spotlight

Spotlight helps you to identify critical data segments and model failure modes. It enables you to build and maintain reliable machine learning models by curating a high-quality datasets.

Introduction

Spotlight is built on the idea that you can only truly understand unstructured datasets if you can interactively explore them. Its core principle is to identify and fix critical data segments by leveraging data enrichments (e.g. features, embeddings, uncertainties). We are building Spotlight for cross-functional teams that want to be in control of their data and data curation processes. Currently, Spotlight supports many use cases based on image, audio, video and time series data.

Quickstart

Get started by installing Spotlight and loading your first dataset.

What you'll need

• Python version 3.9-3.13

Install Spotlight via pip

pip install renumics-spotlight

We recommend installing Spotlight and everything you need to work on your data in a separate virtual environment

Load a dataset and start exploring

import pandas as pd
from renumics import spotlight

df = pd.read_csv("https://spotlight.renumics.com/data/mnist/mnist-tiny.csv")
spotlight.show(df, dtype={"image": spotlight.Image, "embedding": spotlight.Embedding})

pd.read_csv loads a sample csv file as a pandas DataFrame.

spotlight.show opens up spotlight in the browser with the pandas dataframe ready for you to explore. The dtype argument specifies custom column types for the browser viewer.

Load a Hugging Face dataset

import datasets
from renumics import spotlight

dataset = datasets.load_dataset("olivierdehaene/xkcd", split="train")
df = dataset.to_pandas()
spotlight.show(df, dtype={"image_url": spotlight.Image})

The datasets package can be installed via pip.