Get Started#

This guide walks you through the installation, configuration, and first-run of the Take-Away Order Accuracy system.

Table of Contents#

  1. Prerequisites

  2. Installation

  3. Configuration

  4. Starting the Services

  5. Verifying Installation

  6. First Order Validation

  7. Troubleshooting

Prerequisites#

For detailed hardware and software requirements, see the System Requirements guide.

Hardware Requirements#

Component

Minimum

Recommended

CPU

Intel Xeon 8 cores

Intel Xeon 16+ cores

RAM

16GB

32GB+

GPU

Intel Arc A770 (8GB)

Intel Arc

Storage

50GB SSD

200GB NVMe

Software Requirements#

Software

Version

Purpose

Docker

24.0+

Container runtime

Docker Compose

V2+

Service orchestration

Intel GPU Driver

Latest

GPU support

Verify Prerequisites#

docker --version          # Docker version 24.0.x or higher
docker compose version    # Docker Compose version v2.x.x

Installation#

Step 1: Clone the Repository#

git clone https://github.com/intel-retail/order-accuracy.git
cd order-accuracy/take-away

Step 2: Initialize Git Submodules#

make update-submodules

Step 3: Create Environment File#

make init-env

Edit the generated .env file as needed. See Configuration below.

Step 4: Setup OVMS Models (First Time Only)#

Set TARGET_DEVICE in your .env before running this step. The script reads that value to export the model in the correct format for the target device.

cd ../ovms-service
./setup_models.sh
cd ../take-away

This downloads and exports:

  • Qwen2.5-VL-7B-Instruct (OpenVINO format)

  • YOLOv11 model (INT8 OpenVINO)

  • EasyOCR detection and recognition models

Note: Re-run this step any time you change TARGET_DEVICE in .env.

Step 5: Build and Start#

# Pull images from registry (default)
make build
make up

# OR build locally from source
make build REGISTRY=false
make up

Configuration#

Environment File (.env)#

# =============================================================================
# VLM Backend
# =============================================================================
VLM_BACKEND=ovms
OVMS_ENDPOINT=http://ovms-vlm:8000
OVMS_MODEL_NAME=Qwen/Qwen2.5-VL-7B-Instruct
TARGET_DEVICE=GPU            # 'GPU', 'CPU', or 'NPU' — also set OPENVINO_DEVICE to match

# =============================================================================
# Inference Device (must match TARGET_DEVICE)
# =============================================================================
OPENVINO_DEVICE=GPU          # Used by benchmark targets

# =============================================================================
# Semantic Service
# =============================================================================
SEMANTIC_VLM_BACKEND=ovms
DEFAULT_MATCHING_STRATEGY=hybrid   # 'exact', 'semantic', or 'hybrid'
SIMILARITY_THRESHOLD=0.85
OVMS_TIMEOUT=60

# =============================================================================
# MinIO Storage
# =============================================================================
MINIO_ROOT_USER=<your-minio-username>
MINIO_ROOT_PASSWORD=<your-minio-password>
MINIO_ENDPOINT=minio:9000

Changing the inference device: Set both TARGET_DEVICE and OPENVINO_DEVICE to the same value (GPU, CPU, or NPU), then re-run ./setup_models.sh to re-export the model for that device.

Validate Configuration#

make check-env    # Validate required variables
make show-config  # Show current configuration

Starting the Services#

Single Mode (default)#

make up

Parallel Worker Mode (multi-station)#

make up-parallel WORKERS=4

RTSP Streamer (live stream testing)#

To feed a looping video as a live RTSP stream:

Prerequisite: Place a test video at storage/videos/test.mp4 first, or run make download-sample-video.

WORKERS=1 docker compose --profile parallel up -d --no-deps rtsp-streamer

Access From

URL

Host machine

rtsp://localhost:8554/station_1

Other containers

rtsp://rtsp-streamer:8554/station_1

Increase WORKERS for multiple stations (station_1, station_2, etc.).

View Logs#

make logs

Verifying Installation#

Health Check#

make test-api

This checks both the order accuracy API (localhost:8000) and OVMS (localhost:8001).

Service URLs#

Service

URL

Order Accuracy API

http://localhost:8000

OVMS VLM

http://localhost:8001

Gradio UI

http://localhost:7860

MinIO Console

http://localhost:9001

Semantic Service

http://localhost:8080

Service Status#

make status

First Order Validation#

Via Gradio UI#

  1. Open http://localhost:7860

  2. Upload a test video or enter an RTSP URL

  3. Click “Upload and Start Processing”

  4. View results showing matched, missing, and extra items

Via REST API#

# Upload video and validate
curl -X POST http://localhost:8000/upload-video \
  -F "file=@storage/videos/test.mp4" \
  -F "video_id=test_001"

# Check results
curl http://localhost:8000/results/test_001

Via Make Target#

Prerequisite: Ensure storage/videos/test.mp4 exists. Run make download-sample-video if needed.

make benchmark

Troubleshooting#

OVMS Not Starting#

# Check logs
docker logs oa_ovms_vlm

# Verify model files exist
ls -la ../ovms-service/models/

Connection Refused to OVMS (port 8001)#

OVMS can take 2–5 minutes to load the model. Wait and check:

docker logs -f oa_ovms_vlm | grep "Serving"

MinIO Bucket Errors#

# Recreate MinIO with fresh volumes
make down
docker volume rm take-away_minio_data
make up

GPU Not Detected#

sudo usermod -aG render $USER
# Log out and log back in, then restart services
make down && make up

GPU OOM / Out of Memory#

# Switch to CPU: set both in .env, then re-export model
TARGET_DEVICE=CPU
OPENVINO_DEVICE=CPU
# Then:
cd ../ovms-service && ./setup_models.sh
cd ../take-away && make down && make up

Quick Reference#

make update-submodules      # Init submodules (first time)
make download-sample-video  # Download sample video
make init-env               # Create .env from template
make build && make up       # Build images and start services
make status                 # Check service status
make logs                   # View logs
make test-api               # Test API health
make down                   # Stop services
make clean                  # Stop and remove volumes
make benchmark              # Run fixed-workers benchmark
make benchmark-stream-density  # Run stream density benchmark

Next Steps#