Atriva Video Pipeline FFMPEG x86 Installation

Prerequisites

System Requirements

• OS: Linux (Ubuntu 20.04+ recommended)
• Architecture: x86_64
• Docker: 20.10+
• Docker Compose: 1.29+ (optional)

Hardware Requirements

Minimum

• CPU: 2 cores
• RAM: 2GB
• Storage: 10GB free space

Recommended (for hardware acceleration)

• CPU: 4+ cores
• RAM: 4GB+
• GPU: NVIDIA/Intel with hardware acceleration support
• Storage: 50GB+ for frame storage

Software Dependencies

• Docker Engine
• NVIDIA Container Toolkit (for CUDA support)
• Intel Media Driver (for QSV/VAAPI support)

Installation Methods

Method 1: Docker Build

# Clone the repository
git clone https://github.com/atriva-ai/video-pipeline-ffmpeg-x86.git
cd video-pipeline-ffmpeg-x86

# Build the Docker image
docker build -t atriva-video-pipeline-x86 .

# Create required directories
mkdir -p frames videos
chmod -R 777 frames videos

# Run the container
docker run -d \
  --name video-pipeline \
  -p 8002:8002 \
  --device /dev/dri:/dev/dri \
  -v $(pwd)/frames:/app/frames \
  -v $(pwd)/videos:/app/videos \
  atriva-video-pipeline-x86

Method 2: Docker Compose

Create a docker-compose.yml:

version: '3.8'

services:
  video-pipeline:
    build: .
    container_name: atriva-video-pipeline-x86
    ports:
      - "8002:8002"
    volumes:
      - shared-frames:/app/frames
      - video-storage:/app/videos
    devices:
      - /dev/dri:/dev/dri
    environment:
      - FFMPEG_PATH=ffmpeg
      - FFPROBE_PATH=ffprobe
    restart: unless-stopped

volumes:
  shared-frames:
  video-storage:

Run with:

docker-compose up -d

Hardware Acceleration Setup

NVIDIA CUDA

1. Install NVIDIA drivers:

sudo apt-get update
sudo apt-get install -y nvidia-driver-470

2. Install NVIDIA Container Toolkit:

distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list

sudo apt-get update
sudo apt-get install -y nvidia-container-toolkit
sudo systemctl restart docker

3. Run with CUDA support:

docker run --runtime=nvidia \
  -e NVIDIA_VISIBLE_DEVICES=all \
  atriva-video-pipeline-x86

Intel QSV/VAAPI

1. Install Intel Media Driver:

sudo apt-get update
sudo apt-get install -y \
  intel-media-va-driver-non-free \
  vainfo

2. Verify VAAPI access:

vainfo

3. Run with device access:

docker run --device /dev/dri:/dev/dri \
  atriva-video-pipeline-x86

Configuration

Environment Variables

Set these in your Docker run command or docker-compose.yml:

FFMPEG_PATH=ffmpeg              # FFmpeg executable path
FFPROBE_PATH=ffprobe            # FFprobe executable path
UPLOAD_FOLDER=/app/videos       # Video upload directory
OUTPUT_FOLDER=/app/frames       # Frame output directory

Volume Mounts

Required Volumes:

• /app/frames - Frame output (should be shared with AI inference containers)
• /app/videos - Video storage (optional, for file uploads)

Example:

-v /host/path/to/frames:/app/frames
-v /host/path/to/videos:/app/videos

Verification

Check Service Health

curl http://localhost:8002/api/v1/video-pipeline/health/

Expected response:

{
  "status": "healthy",
  "service": "video-pipeline"
}

Check Hardware Acceleration

curl http://localhost:8002/api/v1/video-pipeline/hw-accel-cap/

Test Video Processing

curl -X POST "http://localhost:8002/api/v1/video-pipeline/video-info/" \
  -F "video=@test_video.mp4"

Troubleshooting

Container Won’t Start

1. Check Docker logs:

docker logs video-pipeline

2. Verify port availability:

netstat -tuln | grep 8002

Hardware Acceleration Not Working

1. Verify device access:

docker exec video-pipeline vainfo  # For VAAPI
docker exec video-pipeline nvidia-smi  # For CUDA

2. Check FFmpeg hardware support:

docker exec video-pipeline ffmpeg -hwaccels

Permission Issues

1. Ensure volume permissions:

chmod -R 777 /path/to/frames
chmod -R 777 /path/to/videos

2. Check container user:

docker exec video-pipeline whoami

Network Issues

1. Verify container networking:

docker network ls
docker inspect video-pipeline | grep NetworkMode

Production Deployment

Security Considerations

1. Use read-only mounts where possible
2. Implement authentication/authorization
3. Use secrets management for sensitive data
4. Enable logging and monitoring

Resource Limits

Set appropriate resource limits:

services:
  video-pipeline:
    deploy:
      resources:
        limits:
          cpus: '4'
          memory: 4G
        reservations:
          cpus: '2'
          memory: 2G

High Availability

• Use container orchestration (Kubernetes, Docker Swarm)
• Implement health checks
• Set up auto-restart policies
• Use load balancers for multiple instances