TalkEdit/DOCKER.md

Docker Deployment Guide for VideoTranscriber

This guide explains how to run VideoTranscriber in a Docker container while using Ollama models on your host system.

Architecture Overview

┌─────────────────────────────────────────┐
│ Host System                             │
│ ┌─────────────────┐  ┌──────────────────│
│ │ Ollama Service  │  │ Video Files      │
│ │ (port 11434)    │  │ Directory        │
│ └─────────────────┘  └──────────────────│
│         ▲                     ▲         │
│         │                     │         │
│ ┌───────┼─────────────────────┼─────────│
│ │ Docker Container            │         │
│ │ ┌─────▼─────────┐          │         │
│ │ │ VideoTranscriber         │         │
│ │ │ - Streamlit App          │         │
│ │ │ - Whisper Models         │         │
│ │ │ - ML Dependencies        │         │
│ │ └───────────────┘          │         │
│ └────────────────────────────┼─────────│
│                              │         │
│         Mounted Volumes ─────┘         │
└─────────────────────────────────────────┘

Quick Start

Prerequisites

1. Docker & Docker Compose installed
2. Ollama running on host:

   # Install Ollama (if not already installed)
   curl -fsSL https://ollama.ai/install.sh | sh
   
   # Start Ollama service
   ollama serve
   
   # Pull a model (in another terminal)
   ollama pull llama3
   

1. Setup Environment

# Copy environment template
cp docker.env.example .env

# Edit .env file with your paths
# Key settings to update:
VIDEO_PATH=/path/to/your/videos
OUTPUT_PATH=/path/to/save/outputs
HF_TOKEN=your_huggingface_token_if_needed

2. Create Required Directories

# Create directories for mounting
mkdir -p videos outputs cache config

3. Build and Run

# Build and start the container
docker-compose up -d

# View logs
docker-compose logs -f

# Access the application
# Open browser to: http://localhost:8501

Configuration Options

Environment Variables

Variable	Description	Default	Required
VIDEO_PATH	Host directory containing video files	./videos	Yes
OUTPUT_PATH	Host directory for outputs	./outputs	Yes
CACHE_PATH	Host directory for model cache	./cache	No
OLLAMA_API_URL	Ollama API endpoint	http://host.docker.internal:11434/api	No
HF_TOKEN	HuggingFace token for advanced features	-	No
CUDA_VISIBLE_DEVICES	GPU devices to use	-	No

Volume Mounts

Host Path	Container Path	Purpose
${VIDEO_PATH}	/app/data/videos	Input video files
${OUTPUT_PATH}	/app/data/outputs	Generated transcripts/summaries
${CACHE_PATH}	/app/data/cache	Model and processing cache
${CONFIG_PATH}	/app/config	Configuration files

Platform-Specific Setup

Windows (Docker Desktop)

# In docker-compose.yml - use bridge networking
networks:
  - videotranscriber-network

environment:
  - OLLAMA_API_URL=http://host.docker.internal:11434/api

macOS (Docker Desktop)

Same as Windows - uses host.docker.internal to access host services.

Linux

Option 1 - Host Networking (Recommended):

# In docker-compose.yml
network_mode: host

environment:
  - OLLAMA_API_URL=http://localhost:11434/api

Option 2 - Bridge Networking:

environment:
  - OLLAMA_API_URL=http://172.17.0.1:11434/api  # Docker bridge IP

GPU Support

NVIDIA GPU Setup

1. Install NVIDIA Container Toolkit:

   # Ubuntu/Debian
   curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg
   curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
       sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
       sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
   sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit
   sudo systemctl restart docker
   

2. Enable in docker-compose.yml:

   deploy:
     resources:
       reservations:
         devices:
           - driver: nvidia
             count: 1
             capabilities: [gpu]
   

Usage in Container

Application Settings

When running in Docker, update these settings in the VideoTranscriber UI:

1. Base Folder: Set to /app/data/videos
2. Ollama Models: Should auto-detect from host
3. GPU Settings: Will use container GPU if configured

File Access

• Input Videos: Place in your ${VIDEO_PATH} directory on host
• Outputs: Generated files appear in ${OUTPUT_PATH} on host
• Cache: Models cached in ${CACHE_PATH} for faster subsequent runs

Troubleshooting

Common Issues

1. Can't Connect to Ollama

Symptoms: "Ollama service is not available" message

Solutions:

• Verify Ollama is running: curl http://localhost:11434/api/tags
• Check firewall settings
• For Linux, try host networking mode
• Verify OLLAMA_API_URL in environment

2. No Video Files Detected

Symptoms: "No recordings found" message

Solutions:

• Check VIDEO_PATH points to correct directory
• Ensure directory contains supported formats (.mp4, .avi, .mov, .mkv)
• Check file permissions

3. GPU Not Detected

Symptoms: Processing is slow, no GPU utilization

Solutions:

• Install NVIDIA Container Toolkit
• Uncomment GPU section in docker-compose.yml
• Verify: docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi

4. Permission Issues

Symptoms: Cannot write to output directory

Solutions:

# Fix permissions
sudo chown -R $(id -u):$(id -g) outputs cache config
chmod -R 755 outputs cache config

Debugging

# View container logs
docker-compose logs -f videotranscriber

# Execute shell in container
docker-compose exec videotranscriber bash

# Check Ollama connectivity from container
docker-compose exec videotranscriber curl -f $OLLAMA_API_URL/tags

# Monitor resource usage
docker stats videotranscriber

Advanced Configuration

Custom Dockerfile

For specialized requirements, modify the Dockerfile:

# Add custom dependencies
RUN pip install your-custom-package

# Set custom environment variables
ENV YOUR_CUSTOM_VAR=value

# Copy custom configuration
COPY custom-config.yaml /app/config/

Multi-Instance Deployment

Run multiple instances for different use cases:

# Copy docker-compose.yml to docker-compose.prod.yml
# Modify ports and paths
docker-compose -f docker-compose.prod.yml up -d

CI/CD Integration

# .github/workflows/docker.yml
name: Build and Deploy
on:
  push:
    branches: [main]
jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      - name: Build Docker image
        run: docker build -t videotranscriber .

Performance Optimization

Memory Management

# In docker-compose.yml
deploy:
  resources:
    limits:
      memory: 8G
    reservations:
      memory: 4G

Model Caching

• Use persistent volumes for /app/data/cache
• Pre-download models to reduce startup time
• Configure appropriate cache size limits

Network Optimization

• Use host networking on Linux for better performance
• Consider running Ollama and VideoTranscriber on same machine
• Use SSD storage for cache directories