The multimodal data stack

Open source log handling and visualization for spatial and embodied AI. Managed infrastructure to ingest, store, analyze, and stream data at scale with built-in visual debugging. Fast, flexible, and easy to use.

Rerun is open source 6,200

Start visualizing in seconds

Powerful and flexible visualization for spatial and embodied AI that's shockingly easy to get started with. Simple no sign-up installation and minimal code to get up and running.

pip install rerun-sdk
rerun

Infrastructure to simplify your data engine

Run & record

Run your systems and record data for analysis and training. Use the Rerun SDK to log data or interpret existing log files.

Find issues

Use the Rerun viewer to understand behavior and pinpoint issues. Extract signals from logs for further analysis.

Improve & deploy

Visualize training and eval, debug prototypes, and extract time aligned training samples from messy logs.

How it works

1

Model

Use the SDK to model your data and write it to storage or a live viewer. Rerun's data model, a time aware Entity Component System, makes common scenarios simple but is flexible enough to handle custom data.

import rerun as rr
rr.init("my_data_generating_application")
rr.connect()  # Connect to a remote viewer
…
rr.log("points", rr.Points3D(positions))
rr.log("camera", rr.Transform3D(pos, rot))
rr.log("camera/image", rr.Pinhole(intrinsics))
rr.log("camera/image", rr.Image(tensor))
rr.log("reprojection_error", rr.Scalar(err))
2

View

Get powerful interactive visualizations of live and recorded data. Time travel through your data, with the industry's fastest multimodal viewer. It's open source and runs both natively and in the browser.

3

Build

Build layouts and customize visualizations directly through code or interactively in the UI. Build visualization into your tools by embedding the viewer in your Rust, JS, or Gradio apps.

4

Query

Use Rerun's query APIs to extract time-aligned datasets from messy robotics recordings. Rerun returns Apache Arrow data that plugs in to your favorite dataframe library for further analysis.

import rerun as rr
…
# Create an aligned dataframe based on time-points where an Image was logged
aligned_samples = rr.dataframe
    .load_recording("raw.rrd")
    .view(index="sensor_time", contents="/**")
    .filter_is_not_null("/camera/image:ImageBuffer")
    .fill_latest_at()
    .select()

# Write aligned samples to a parquet file for later training
with pq.ParquetWriter("train.parquet", aligned_samples.schema()) as writer:
    for chunk in aligned_samples:
        writer.write_batch(chunk)
Read the documentation

The community loves building on Rerun

LeRobot

LeRobot is Huggingface’s State-of-the-art AI for real-world robotics project. They are using Rerun as an integrated part of their visualization tools.

LeRobot

LeRobot is Huggingface’s State-of-the-art AI for real-world robotics project. They are using Rerun as an integrated part of their visualization tools.

Go to project →
Aria Dataset Explorer

Project Aria is a research platform developed by Meta Reality Labs Research to push the state of the art in egocentric AI research. Rerun is used to visualize sequences in their Aria Dataset Explorer.

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Peng

Peng is a minimal quadrotor autonomy framework for simulating quadrotor dynamics and control. It comes with Rerun as a vizualisation tool and is shown here on Soulscircuit's Pilet 5, a 5-inch cyberdeck powered by rpi5.

Go to project →
kornia-rs

The kornia-rs crate is a low level library for Computer Vision written in Rust 🦀 by the Kornia team. You can use the library to perform image I/O, Rerun vizualisation and other low level operations in your machine learning and data-science projects in a thread-safe and efficient way.

Go to project →

Open source vs commercial

Open source

Visualization and simple log handling

Dual licensed under MIT and Apache 2

Join on GitHub
  • The fastest & easiest to use multimodal visualizer out there
  • Visualizer runs native, on the web and is embeddable in notebooks and web apps
  • An SDK for modeling and handling multimodal logs

Commercial

Data management at scale

Under development with select design partners

Sign up for waitlist
  • Data platform for embodied AI where all data is instantly visualizable and debuggable
  • Ingestion, storage engine and index management for large scale data
  • Dataset management for both recordings and structured tables