🚀 Getting Started

💡 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 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). Pre-defined templates for typical data curation workflows get you started quickly and connect your stack to the data-centric AI ecosystem.

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 3.7-3.10

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

python

import pandas as pd
from renumics import spotlight

df = pd.read_csv("https://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.

CLI

curl https://renumics.com/data/mnist/mnist-tiny.csv -o mnist-tiny.csv
spotlight mnist-tiny.csv --dtype image=Image --dtype embedding=Embedding

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.

Disclaimer

Usage Tracking

We have added crash report and perfomance collection.
We do NOT collect user data other than an anonymized Machine Id obtained by py-machineid, and only log our own actions.
We do NOT collect folder names, dataset names, or row data of any kind only aggregate performance statistics like total time of a table_load, crash data, etc.
Collecting spotlight crashes will help us improve stability.

Too opt out of the crash report collection define an environment variable called SPOTLIGHT_OPT_OUT and set it to true.

e.G.

export SPOTLIGHT_OPT_OUT=true

🧭 Start by use case

You can adapt Spotlight to your data curation tasks. To get you started quickly, we are continuously developing pre-defined recipes for common workflows.

Get started quickly with our 📒 Playbook:

• Create embeddings
• Detect duplicates
• Detect leakage
• Detect data drift
• Detect label errors
• Detect outliers

Or jump right into using spotlight in one of our hosted Huggingface 🤗 spaces:

• Explore data slices in the CIFAR-100 benchmark
• Navigate Cleanlab Data Issues in CIFAR-100 with Spotlight
• Explore data slices in the CIFAR-100 benchmark
• Stable Diffusion Strengths Weaknesses

Tell us which data curation task is important for your work:

• Open an issue on Github
• Have a coffee talk with us
• Join our Discord