Installation

Installing headless AugeLab Studio varies depending on your operating system. Follow the instructions below for your specific platform.

Windows

Using the AugeLab Studio installer manually sets up the headless runtime already and is the recommended way to install Headless Studio on Windows.

Linux

Download the Linux installer script from account.augelab.com. The installer supports Debian and Debian-based distributions, including Ubuntu. It creates an isolated virtual environment at ~/studio_venv, installs the selected studio package profile, and creates desktop launchers when UI mode is selected.

The installer does not install NVIDIA drivers. For AI/GPU mode, install the NVIDIA driver on the host first. AugeLab Studio ships the CUDA runtimes it needs. On Jetson devices, use the JetPack/NVIDIA driver stack for your device.

Full installation

Use this for the regular desktop + headless installation:

chmod +x installer.sh
./installer.sh --ui

This installs studio[ui], creates ~/studio_venv, adds the augelab_studio launcher, and creates desktop entries.

After installation, run:

augelab_studio

Full installation with AI/GPU support

Install the NVIDIA driver first, then run:

chmod +x installer.sh
./installer.sh --ui --ai

This installs studio[ui,gpu].

Headless only

Use this when you only need the Python API and do not need the desktop UI:

chmod +x installer.sh
./installer.sh --headless

This installs studio. UI launcher and desktop entries are not created.

Headless with AI/GPU support

Install the NVIDIA driver first, then run:

chmod +x installer.sh
./installer.sh --headless --ai

This installs studio[gpu]. UI launcher and desktop entries are not created.

Interactive install

Run the installer without mode flags to choose install mode interactively:

chmod +x installer.sh
./installer.sh

The installer asks whether to install headless or UI mode, then whether to include the AI/GPU extra.

Non-interactive install with environment variables

Use environment variables when running from automation:

INSTALL_UI=1 INSTALL_AI=0 ./installer.sh
INSTALL_UI=1 INSTALL_AI=1 ./installer.sh
INSTALL_UI=0 INSTALL_AI=0 ./installer.sh
INSTALL_UI=0 INSTALL_AI=1 ./installer.sh

INSTALL_UI=1 selects UI mode. INSTALL_AI=1 includes the AI/GPU extra.

Uninstall

./installer.sh --uninstall

This removes installer-created files:

• ~/studio_venv

• ~/.local/bin/augelab_studio

• AugeLab Studio desktop entries

• AugeLab Studio icons

Parent directories are removed only if empty.

Manual Python install

Use the installer when possible. If you need a manual Python environment, install into an isolated virtual environment instead of system Python:

apt-get update -y && \
    apt-get install -y --no-install-recommends \
    libdmtx0b \
    zbar-tools \
    build-essential \
    libgl1-mesa-glx \
    curl \
    ca-certificates \
    && rm -rf /var/lib/apt/lists/*

curl -fsSL https://astral.sh/uv/install.sh | sh
uv venv -p 3.12 studio_venv
uv pip install --python studio_venv/bin/python \
    studio \
    --extra-index-url https://pyrepo.augelab.com \
    --extra-index-url https://download.pytorch.org/whl/cpu \
    --index-strategy unsafe-best-match

For AI/GPU manual install, install the NVIDIA driver first, then replace studio with studio[gpu]. AugeLab Studio ships the CUDA runtimes it needs.

Docker with CPU/CUDA on x86_64

For customer deployments, build the container image from a Dockerfile. AugeLab Studio is a closed-source application, so Docker installs the studio package from the AugeLab package index instead of building Studio from source.

Use Docker Example as the canonical Docker guide. It includes:

• CPU Dockerfile

• GPU/CUDA Dockerfile

• docker-compose.yml

• .env

• scenario runner script

• mounted output folder

CPU Docker

Use CPU Docker when your scenario does not need CUDA acceleration. The Docker example builds from python:3.12-slim-bookworm and installs studio.

GPU/CUDA

AugeLab Studio supports CUDA acceleration for headless scenarios. GPU Docker requires compatible NVIDIA drivers and NVIDIA Container Toolkit on the host.

Use the GPU/CUDA Dockerfile in Docker Example. It builds from an NVIDIA CUDA runtime image and installs studio[gpu].

ARM64 (Raspberry Pi, Jetson Nano/AGX)

AugeLab Studio supports ARM64 architecture for headless usage on Debian-based devices such as Raspberry Pi and NVIDIA Jetson series.

Download the Linux ARM64 installer scripts from account.augelab.com, then run the installer on the device.

Full ARM64 installation

chmod +x installer.sh
./installer.sh --ui

This installs studio[ui], creates ~/studio_venv, adds the augelab_studio launcher, and creates desktop entries.

Full ARM64 installation with AI/GPU support

Install the correct JetPack/NVIDIA driver stack for your device first.

chmod +x installer.sh
./installer.sh --ui --ai

This installs studio[ui,gpu].

Headless ARM64 only

chmod +x installer.sh
./installer.sh --headless

This installs studio. UI launcher and desktop entries are not created.

Headless ARM64 with AI/GPU support

Install the correct JetPack/NVIDIA driver stack for your device first.

chmod +x installer.sh
./installer.sh --headless --ai

This installs studio[gpu]. UI launcher and desktop entries are not created.

ARM64 uninstall

./installer.sh --uninstall

The ARM64 installer supports Debian and Debian-based distributions. If your device uses another Linux family, contact AugeLab for deployment guidance.

For Jetson GPU deployments, match the installer, JetPack/NVIDIA driver stack, and AugeLab package profile for your device.