Lilly Tech Systems
Advanced
Best Practices
Production-ready guidelines for optimizing performance, managing costs, and operating ML workloads on AWS Inferentia and Trainium.
Compilation Optimization
- Pre-compile models: Always compile during CI/CD, never at runtime; cache compiled artifacts in S3
- Pin input shapes: Use fixed batch sizes and sequence lengths to avoid recompilation; pad inputs to match
- Use parallel compilation: Run
neuron_parallel_compileto compile subgraphs concurrently - Version-lock SDK: Pin your Neuron SDK version; compiled models are not portable across versions
- Benchmark data types: Compare BF16 vs FP16 accuracy and throughput for your specific model
Performance Tuning
Batch Optimization
Use dynamic batching to accumulate requests and maximize NeuronCore utilization. Tune batch size vs latency tradeoff.
Model Parallelism
Shard large models across NeuronCores using tensor parallelism. Match parallelism degree to model size and instance type.
Memory Management
Monitor HBM usage with neuron-top. Use model weight sharing for multi-model endpoints serving similar architectures.
Pipeline Optimization
Overlap CPU preprocessing with NeuronCore inference using async execution and double buffering.
Cost Optimization
- Right-size instances: Start with the smallest Inf2 instance that fits your model; scale horizontally
- Use Savings Plans: Commit to 1-year or 3-year compute savings plans for predictable workloads
- Spot instances: Use Spot for fault-tolerant batch inference workloads for up to 90% savings
- Multi-model endpoints: Serve multiple models on a single instance to maximize utilization
- Auto-scaling: Scale to zero during off-peak hours; use scheduled scaling for predictable patterns
Monitoring and Operations
| Metric | Tool | Alert Threshold |
|---|---|---|
| NeuronCore utilization | neuron-monitor + CloudWatch | < 50% (underutilized) |
| Memory usage | neuron-top | > 90% HBM |
| Inference latency | Application metrics | > SLA threshold |
| Error rate | CloudWatch / application logs | > 0.1% |
| Throughput | neuron-monitor | < expected baseline |
Production Checklist
- Model validation: Compare Neuron inference output against CPU/GPU baseline for accuracy regression
- Load testing: Benchmark with realistic traffic patterns before production deployment
- Rollback plan: Maintain GPU-based fallback endpoints for critical workloads
- Health checks: Implement NeuronCore health checks in your serving container
- Logging: Capture compilation logs, runtime errors, and inference metrics
- SDK updates: Test new Neuron SDK versions in staging before production upgrades
Congratulations! You've completed the AWS Inferentia & Trainium course. You now understand custom silicon architecture, the Neuron SDK, deployment patterns, and production best practices. Continue exploring through the AWS Neuron documentation and the Neuron GitHub samples repository.