Production Guide
MinerU: From pip install to Production
6 chapters. Docker to Ray clusters. Everything the English docs don't cover about self-hosting MinerU at scale.
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Chapter 1: Architecture & Pipeline Design
Text-based PDF? Scanned document? Mixed? Choose the wrong pipeline and your output is garbage.
What you'll get:
- Decision tree: text-based vs scanned vs mixed pipeline selection
- CPU vs GPU path — when GPU acceleration actually matters
- vLLM vs sglang vs transformers backend selection
- Full pipeline configuration for each document type
Chapter 2: Docker Production Setup
"Works on my machine" doesn't count when you're processing 10,000 PDFs.
What you'll get:
- Multi-stage production Dockerfile with all dependencies pinned
- docker-compose.yml with MinerU + vLLM + Redis services
- Model download caching strategy (no re-downloading 2GB models per rebuild)
- Health checks, resource limits, volume mounts
Chapter 3: Multi-Node Batch Processing
One GPU can only do so much. Scale to thousands of PDFs with Ray clusters.
What you'll get:
- Ray cluster setup for distributed PDF processing
- Shared storage architecture (NFS/S3)
- Queue management with failure recovery and retry logic
- Autoscaling configuration for spot/preemptible instances
Chapter 4: Performance Tuning
Your GPU is at 40% utilization and you don't know why. Fix it.
What you'll get:
- Batch size optimization for your specific GPU
- GPU memory allocation tuning (CUDA OOM is not a feature)
- Concurrent worker scaling formula
- Benchmarking scripts + performance reference table
Chapter 5: Error Troubleshooting Bible
20+ errors, diagnosed and fixed. Stop translating Chinese GitHub issues at midnight.
What you'll get:
- Complete error reference (20+ entries with diagnosis + fix)
- Debugging checklist for production incidents
- Memory profiling and OOM troubleshooting scripts
- Model download corruption detection and recovery
Chapter 6: MinerU vs Docling vs Marker
MinerU isn't always the right tool. Know when to switch.
What you'll get:
- Accuracy benchmarks across document types (text, scanned, mixed, table-heavy)
- Speed comparison at scale (100/1K/10K documents)
- Decision matrix: which tool for which document type
- Migration guide: Marker → MinerU and Docling → MinerU
Bonus: CI/CD Pipeline (Paid Only)
GitHub Actions automated PDF processing pipeline. Push a PDF, get Markdown back.
What you'll get:
- Full GitHub Actions workflow for automated document processing
- Self-hosted runner setup for GPU access
- Artifact storage and notification integration
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