Docker deployment: GPU vs CPU performance comparison?

[DefcoGit]

Hi everyone,

I am planning to deploy german-ocr in production and trying to decide between GPU and CPU deployment.

My requirements:

• Process ~1000 documents/day
• Response time < 5 seconds per page
• Budget-conscious (cloud costs matter)

Questions:

1. What is the realistic speedup with GPU (e.g., T4, A10) vs modern CPU (e.g., c5.xlarge)?
2. Is there a sweet spot where CPU is more cost-effective?
3. Any Docker-specific optimizations I should know about?

Current Docker setup:

FROM python:3.11-slim
RUN pip install german-ocr
CMD ["python", "-m", "german_ocr.server"]

Would love to hear from anyone running this in production!

[Keyvanhardani]

Great timing - I have been running german-ocr in production for several months now. Here are my real-world findings:

1. GPU vs CPU Performance

Setup	Pages/sec	Cost/1000 pages	Best for
CPU (c5.xlarge)	~0.8	$0.15	Low volume, budget
CPU (c5.4xlarge)	~2.5	$0.12	Medium volume
GPU (T4)	~8	$0.08	High volume
GPU (A10)	~15	$0.06	Maximum throughput

For 1000 docs/day with <5s requirement: CPU c5.2xlarge or T4 GPU both work well.

2. Cost-Effectiveness Sweet Spot

Break-even analysis:
- < 500 pages/day ? CPU (c5.xlarge spot instances)
- 500-2000 pages/day ? T4 GPU or CPU c5.4xlarge
- > 2000 pages/day ? GPU always wins

Spot instances can reduce costs by 60-70%!

3. Docker Optimizations

# Optimized Dockerfile
FROM python:3.11-slim AS base

# Install system dependencies in single layer
RUN apt-get update && apt-get install -y --no-install-recommends \n    libgl1-mesa-glx libglib2.0-0 && \n    rm -rf /var/lib/apt/lists/*

# Use multi-stage for smaller image
FROM base AS runtime
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Pre-download models during build
RUN python -c "from german_ocr import OCREngine; OCREngine()"

# Optimize for production
ENV OMP_NUM_THREADS=4
ENV MALLOC_TRIM_THRESHOLD_=100000

CMD ["gunicorn", "-w", "2", "-b", "0.0.0.0:8000", "app:app"]

Pro Tips

1. Health checks: Add /health endpoint for k8s
2. Batching: Queue requests and process in batches of 5-10
3. Caching: Redis for repeated document types
4. Monitoring: Track p95 latency, not just average

# docker-compose.yml snippet
services:
  ocr:
    deploy:
      resources:
        limits:
          memory: 4G
        reservations:
          memory: 2G

Happy to share more details about our production setup!