Getting Started


If you would like to take advantage of the most recent features, please install via conda/

Table of Contents


This section covers the following:

  1. Installing av2.
  2. Downloading the Argoverse 2 and TbV datasets.


We highly recommend using conda with the conda-forge channel for package management.

You will need to install conda on your machine. We recommend installing miniforge:

wget -O "$(uname)-$(uname -m).sh"
bash -bp "${HOME}/conda"

Then, install av2:

bash conda/ && conda activate av2


You may need to run a post-install step to initialize conda:

$(which conda) init $SHELL

If conda is not found, you will need to add the binary to your PATH environment variable.

Installing via pip

Installation via PyPI requires manually installing Rust via rustup.

Run the following and select the default installation:

curl --proto '=https' --tlsv1.2 -sSf | sh

Make sure to adjust your PATH as:

export PATH=$HOME/.cargo/bin:$PATH

We use the nightly release of Rust for SIMD support. Set it as your default toolchain:

rustup default nightly

Then, install av2:

pip install git+

Downloading the data

Our datasets are available for download from AWS S3.

For the best experience, we highly recommend using the open-source s5cmd tool to transfer the data to your local filesystem. Please note that an AWS account is not required to download the datasets.

Installing s5cmd

The easiest way to install s5cmd is through conda using the conda-forge channel:

conda install s5cmd -c conda-forge

Manual Installation

s5cmd can also be installed with the following script:

#!/usr/bin/env bash

export INSTALL_DIR=$HOME/.local/bin
export S5CMD_URI=$(uname | sed 's/Darwin/macOS/g')-64bit.tar.gz

mkdir -p $INSTALL_DIR
curl -sL $S5CMD_URI | tar -C $INSTALL_DIR -xvzf - s5cmd

Note that it will install s5cmd in your local bin directory. You can always change the path if you prefer installing it in another directory.

Download the Datasets

Run the following command to download the one or more of the datasets:

#!/usr/bin/env bash

# Dataset URIs
# s3://argoverse/datasets/av2/sensor/ 
# s3://argoverse/datasets/av2/lidar/
# s3://argoverse/datasets/av2/motion-forecasting/
# s3://argoverse/datasets/av2/tbv/

export DATASET_NAME="sensor"  # sensor, lidar, motion_forecasting or tbv.
export TARGET_DIR="$HOME/data/datasets"  # Target directory on your machine.

s5cmd --no-sign-request cp "s3://argoverse/datasets/av2/$DATASET_NAME/*" $TARGET_DIR

The command will all data for $DATASET_NAME to $TARGET_DIR. Given the size of the dataset, it might take a couple of hours depending on the network connectivity.

When the download is finished, the dataset is ready to use!


Why manage dependencies in conda instead of pip?

conda enables package management outside of the python ecosystem. This enables us to specify all necessary dependencies in environment.yml. Further, gpu-based packages (e.g., torch) are handled better through conda.

Why conda-forge?

conda-forge is a community-driven channel of conda recipes. It includes a large number of packages which can all be properly tracked in the conda resolver allowing for consistent environments without conflicts.