Docker Example

This page shows a reproducible Docker setup for running an AugeLab Studio .pmod scenario headlessly.

The image is built locally from a Dockerfile. Customers do not need the AugeLab Studio source code. The container installs the closed-source studio package from the AugeLab package index during image build.

Prerequisites

• Docker Desktop or Docker Engine with Docker Compose.

• Access to the AugeLab package index.

• An AugeLab verification code.

• A .pmod scenario that can run headlessly.

• For GPU/CUDA: NVIDIA Container Toolkit installed on the host.

Quick path

1. Create the folder layout below.

2. Put your .pmod file in app/.

3. Add your verification code to .env.

4. Use the CPU or GPU Dockerfile.

5. Run docker compose up --build.

6. Check output_on_host.

Project layout

augelab-docker-example/
  .env
  docker-compose.yml
  docker-compose.cuda.yml
  output_on_host/
  app/
    Dockerfile
    Dockerfile.cuda
    run_scenario.py
    your_scenario.pmod

Replace your_scenario.pmod with your own scenario file.

Environment file

Create .env next to docker-compose.yml:

.env

AUGELAB_VERIFICATION_CODE=PASTE_YOUR_VERIFICATION_CODE_HERE
SCENARIO_PATH=your_scenario.pmod

Do not commit .env if it contains a real verification code.

Scenario runner

Create app/run_scenario.py:

app/run_scenario.py

import os
from pathlib import Path

from studio import StudioScenario


OUTPUT_DIR = Path("/app/app_output")
OUTPUT_DIR.mkdir(parents=True, exist_ok=True)

verification_code = os.environ.get("AUGELAB_VERIFICATION_CODE")
if not verification_code:
    raise RuntimeError("AUGELAB_VERIFICATION_CODE is not set.")

scenario_path = os.environ.get("SCENARIO_PATH", "your_scenario.pmod")
if not Path(scenario_path).is_file():
    raise FileNotFoundError(f"Scenario file not found: {scenario_path}")

scenario = StudioScenario(verification_code=verification_code)
scenario.enable_logging_stdout()
scenario.load_scenario(scenario_path)

try:
    print("Scenario result:", scenario.run())
finally:
    scenario.cleanup()

If your scenario writes files, configure the scenario to write them under /app/app_output.

CPU Dockerfile

Use this for regular headless CPU runs.

app/Dockerfile

FROM python:3.12-slim-bookworm

WORKDIR /app

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/*

ADD https://astral.sh/uv/0.9.17/install.sh /uv-installer.sh
RUN sh /uv-installer.sh && rm /uv-installer.sh

ENV PATH="/root/.local/bin/:$PATH"

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

RUN python -c "from studio import StudioScenario; print('studio import ok')"

COPY run_scenario.py .
COPY your_scenario.pmod .

CMD ["python", "run_scenario.py"]

GPU/CUDA Dockerfile

Use this when your scenario needs CUDA acceleration. The host must have compatible NVIDIA drivers and NVIDIA Container Toolkit.

app/Dockerfile.cuda

FROM nvidia/cuda:12.8.0-cudnn-runtime-ubuntu22.04

WORKDIR /app

RUN apt-get update -y && \
    apt-get install -y --no-install-recommends \
    python3 \
    python3-venv \
    python3-pip \
    libdmtx0b \
    zbar-tools \
    build-essential \
    libgl1-mesa-glx \
    curl \
    ca-certificates \
    && rm -rf /var/lib/apt/lists/*

ADD https://astral.sh/uv/0.9.17/install.sh /uv-installer.sh
RUN sh /uv-installer.sh && rm /uv-installer.sh

ENV PATH="/root/.local/bin/:$PATH"
ENV NVIDIA_VISIBLE_DEVICES=all
ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility

RUN uv pip install "studio[gpu]" --system \
    --extra-index-url https://pyrepo.augelab.com \
    --index-strategy unsafe-best-match

RUN python3 -c "from studio import StudioScenario; print('studio gpu import ok')"

COPY run_scenario.py .
COPY your_scenario.pmod .

CMD ["python3", "run_scenario.py"]

Compose file

Use this for the CPU Dockerfile:

docker-compose.yml

services:
  augelab-headless:
    build:
      context: ./app
      dockerfile: Dockerfile
    env_file:
      - .env
    volumes:
      - ./output_on_host:/app/app_output

This mounts ./output_on_host from your project folder into the container as /app/app_output.

On Windows, use forward slashes in absolute Docker mount paths, for example C:/work/augelab_output:/app/app_output. Relative mounts like ./output_on_host:/app/app_output are usually easier to share across machines.

GPU/CUDA compose file

Use this for Dockerfile.cuda:

docker-compose.cuda.yml

services:
  augelab-headless-gpu:
    build:
      context: ./app
      dockerfile: Dockerfile.cuda
    env_file:
      - .env
    volumes:
      - ./output_on_host:/app/app_output
    gpus: all

Build and run

CPU:

docker compose up --build

GPU/CUDA:

docker compose -f docker-compose.cuda.yml up --build

You should see AugeLab Studio logs and the scenario result in the terminal.

Verify output

Check output_on_host on your host machine. Files written by the scenario to /app/app_output should appear there.

Troubleshooting

Problem	Check
studio install fails	Confirm network access and AugeLab package index access.
License/activation fails	Confirm AUGELAB_VERIFICATION_CODE is set and the container has internet access.
Scenario file not found	Confirm SCENARIO_PATH matches the .pmod copied by the Dockerfile.
Output folder empty	Confirm the .pmod writes to /app/app_output.
Windows volume does not mount	Use forward slashes and confirm Docker Desktop can access the drive.
GPU container cannot see GPU	Install NVIDIA Container Toolkit and test with docker run --rm --gpus all nvidia/cuda:12.8.0-base-ubuntu22.04 nvidia-smi.