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video-accessibility/infra/cloud-run
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michael 79440929f4 feat: add Cloud Run HTTP services for Whisper and FFmpeg 
Migrate CPU-intensive workloads to Cloud Run for autoscaling:

- Add Whisper HTTP service (FastAPI) with /transcribe endpoint
- Add FFmpeg HTTP service (FastAPI) with /encode, /probe, /extract-frame, etc.
- Add Dockerfiles for both services (8 vCPU, 32GB RAM, Gen2)
- Add Cloud Build config for CI/CD deployment
- Add Cloud Run service YAML configs with scale-to-zero
- Update whisper_transcribe.py to call Cloud Run when WHISPER_SERVICE_URL set
- Update video_renderer.py to call Cloud Run when FFMPEG_SERVICE_URL set
- Update whisper_service.py for Cloud Run compatibility (no settings dependency)
- Add ffmpeg_service_url and whisper_service_url to config.py

Services scale 0→N based on request load, falling back to local
execution when service URLs are not configured (hybrid mode).

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
2026-01-02 10:12:50 -06:00
..
api-service.yaml initial commit 2025-08-24 16:28:33 -05:00
cloudbuild-http-services.yaml feat: add Cloud Run HTTP services for Whisper and FFmpeg 2026-01-02 10:12:50 -06:00
cloudbuild.yaml feat: switch Whisper model from large-v3 to medium 2025-12-28 22:35:47 -06:00
deploy.sh initial commit 2025-08-24 16:28:33 -05:00
ffmpeg-http-service.yaml feat: add Cloud Run HTTP services for Whisper and FFmpeg 2026-01-02 10:12:50 -06:00
main.tf initial commit 2025-08-24 16:28:33 -05:00
README.md initial commit 2025-08-24 16:28:33 -05:00
terraform.tfvars.example initial commit 2025-08-24 16:28:33 -05:00
tts-worker-service.yaml feat: add dedicated TTS worker with parallel per-cue synthesis 2025-12-30 14:23:11 -06:00
whisper-http-service.yaml feat: add Cloud Run HTTP services for Whisper and FFmpeg 2026-01-02 10:12:50 -06:00
whisper-worker-service.yaml feat: switch Whisper model from large-v3 to medium 2025-12-28 22:35:47 -06:00
worker-service.yaml initial commit 2025-08-24 16:28:33 -05:00

README.md

Cloud Run Deployment

This directory contains deployment configurations for running the Accessible Video Platform on Google Cloud Run.

Files

  • cloudbuild.yaml - Cloud Build configuration for CI/CD
  • api-service.yaml - Cloud Run service definition for API
  • worker-service.yaml - Cloud Run service definition for Celery workers
  • main.tf - Terraform infrastructure as code
  • terraform.tfvars.example - Example Terraform variables
  • deploy.sh - Manual deployment script
  • README.md - This file

Prerequisites

  1. Google Cloud Project with billing enabled

  2. Required APIs enabled (script will enable them):

    • Cloud Build API
    • Cloud Run API
    • Container Registry API
    • Secret Manager API
    • Cloud Trace API
    • Cloud Monitoring API
    • Translate API
    • Text-to-Speech API
    • Cloud Storage API
    • AI Platform API

  3. Secrets created in Secret Manager:

    • mongodb-url - MongoDB Atlas connection string
    • redis-url - Redis connection string (Cloud Memorystore)
    • jwt-secret - JWT signing secret
    • jwt-refresh-secret - JWT refresh token secret
    • gemini-api-key - Google Gemini API key
    • sendgrid-api-key - SendGrid API key for emails
    • elevenlabs-api-key - ElevenLabs API key for TTS
    • sentry-dsn - Sentry error tracking DSN

Deployment Options

Option 1: Terraform (Recommended)

# Initialize Terraform
cd infra/cloud-run
terraform init

# Copy and configure variables
cp terraform.tfvars.example terraform.tfvars
# Edit terraform.tfvars with your project details

# Plan deployment
terraform plan

# Deploy infrastructure
terraform apply

Option 2: Manual Script

# Set environment variables
export PROJECT_ID="your-gcp-project-id"
export REGION="us-central1"

# Run deployment script
./infra/cloud-run/deploy.sh

Option 3: Cloud Build Trigger

Set up a Cloud Build trigger connected to your Git repository that uses cloudbuild.yaml for automatic deployments on code changes.

Local Development

Use the provided docker-compose.yml in the project root:

# Copy environment file
cp .env.example .env
# Edit .env with your development credentials

# Start all services
docker-compose up -d

# View logs
docker-compose logs -f api
docker-compose logs -f worker

Service Architecture

  • API Service: Handles HTTP requests, authentication, job management

    • Memory: 2Gi, CPU: 2000m
    • Autoscaling: 1-10 instances
    • Public access via HTTPS

  • Worker Service: Processes video files with AI models

    • Memory: 4Gi, CPU: 4000m
    • Autoscaling: 0-5 instances
    • Internal access only

Monitoring

Both services include:

  • OpenTelemetry tracing exported to Cloud Trace
  • Prometheus metrics for monitoring
  • Sentry error tracking for error reporting
  • Health checks for service reliability

Security

  • Services run with least-privilege service accounts
  • Secrets managed via Secret Manager
  • No hardcoded credentials in containers
  • Network isolation for worker service