Ubuntu installation¶

Use the official installer on Ubuntu 22.04/24.04, on x86_64 or ARM64:

curl -fsSL https://raw.githubusercontent.com/dimensionalOS/dimos/main/scripts/install.sh | bash

Choose system packages for the CI-tested path. The installer sets up apt dependencies, uv, Python 3.12, and dimOS, then verifies the CLI and native libraries.

Choose library for the published package or dev for a source checkout. For example:

curl -fsSL https://raw.githubusercontent.com/dimensionalOS/dimos/main/scripts/install.sh | bash -s -- --mode dev --no-nix --project-dir ./dimos
cd dimos
source .venv/bin/activate

An existing checkout is reused without pulling or switching branches. Developer mode includes test and lint dependencies; see testing for additional groups.

Both modes pass CPU installation CI on Ubuntu 22.04/24.04 and x86_64/ARM64. Linux ARM64 excludes the unsupported scene extra. Jetson CUDA setup is not supported.

See installer options.

Manual installation¶

Use these steps if you need to install without the guided script.

sudo apt-get update
sudo apt-get install -y ca-certificates curl git g++ portaudio19-dev git-lfs libturbojpeg pre-commit libgl1 libegl1 libglib2.0-0 ffmpeg libsndfile1 pkg-config
curl -LsSf https://astral.sh/uv/install.sh | sh
export PATH="$HOME/.local/bin:$PATH"

Library environment¶

mkdir dimos-app && cd dimos-app
uv venv --python 3.12
source .venv/bin/activate
uv pip install --torch-backend cpu 'dimos[base,unitree,sim]'
uv run dimos --help

Developer checkout¶

GIT_LFS_SKIP_SMUDGE=1 git clone https://github.com/dimensionalOS/dimos.git
cd dimos
uv sync --locked --python 3.12 --extra manipulation --extra unitree --extra cpu --group tests --group lint
source .venv/bin/activate
uv run --no-sync dimos --help

Library mode selects CPU PyTorch wheels. On Linux x86_64 with a CUDA-capable GPU, use --torch-backend cu128 for library mode or replace --extra cpu with --extra cuda to add GPU inference dependencies in developer mode.

Developer installs use the locked PyTorch build. On Linux x86_64 it includes CUDA libraries and also supports CPU execution without an NVIDIA GPU. Selecting cpu skips optional CUDA extras; it does not select a CPU-only PyTorch wheel. Use uv run --no-sync to use the installed environment, and repeat your selected extras when running uv sync.