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

Installing AugeLab Studio is very straightforward on Linux systems. Follow these steps.

Installing on system Python is not recommended. Use virtual environments (e.g., `venv` or `uv`) to isolate the installation. { % endhint %}

Install dependencies

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/*

Install Python 3.12

There are many ways to install python 3.12 on Linux systems. Our recommended way is to use uv. If you have uv installed, skip to the next step.

curl -fsSL https://astral.sh/uv/0.9.17/install.sh | bash -s -- -p /usr/local -v 1.4.6
uv python install 3.12
uv venv

Install AugeLab Studio Headless

uv pip install studio --extra-index-url https://pyrepo.augelab.com --extra-index-url https://download.pytorch.org/whl/cpu --index-strategy unsafe-best-match

Docker with CPU/CUDA on x86_64

There are several pre-built Docker images for running Headless Studio. First way is to pull an existing image from our Docker Hub repository:

Docker CPU image

Option 1: Pull the image

# change the version tag as needed
docker pull public.ecr.aws/o9c1t9b5/augelab-studio/ubuntu-cpu-studio:3.4.5

Option 2:Create from scratch

FROM python:3.12-slim-bookworm

# Set a working directory inside the container
WORKDIR /app

# 2. Install System Dependencies identified from the workflow, install uv
RUN 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/*

# Download the latest installer
ADD https://astral.sh/uv/0.9.17/install.sh /uv-installer.sh

# Run the installer then remove it
RUN sh /uv-installer.sh && rm /uv-installer.sh

# Ensure the installed binary is on the `PATH`
ENV PATH="/root/.local/bin/:$PATH"

# 4. Install Python Dependencies using pip
RUN uv pip install studio==3.4.5 --system --extra-index-url https://pyrepo.augelab.com --extra-index-url https://download.pytorch.org/whl/cpu --index-strategy unsafe-best-match

GPU/CUDA image

AugeLab Studio also supports CUDA acceleration for headless scenarios. Precompiled Docker images are available for various CUDA versions with:

# change the version tag as needed
docker pull public.ecr.aws/o9c1t9b5/augelab-studio/ubuntu-gpu-studio:3.4.5

Building yourself can take 3-4 hours and several iterations. If you want to build your own image nevertheless, contact us for further instructions.

ARM64 (Raspberry Pi, Jetson Nano/AGX)

AugeLab Studio supports ARM64 architecture for headless usage on devices like Raspberry Pi and NVIDIA Jetson series. Below are the installation instructions for these platforms.

Raspberry Pi 4x

To install Headless Studio on Raspberry Pi OS (64-bit), follow the same steps as for Linux above. Make sure to have Python 3.12 installed via uv or another method before installing the Studio package.

Jetson Nano/AGX

Jetson devices run on ARM64 architecture and require specific CUDA versions and several compilations.

CPU installation is similar to Linux above, but for GPU/CUDA support, contact us for detailed instructions and precompiled packages suitable for Jetson devices.