Lilly Tech Systems
Learn AWS Inferentia & Trainium
Master AWS custom silicon for machine learning. Learn how Inferentia accelerates inference workloads and Trainium powers cost-effective model training, delivering up to 50% savings compared to GPU-based instances.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
Understand AWS custom silicon strategy, Inferentia and Trainium chip architectures, and when to use them over GPUs.
2. Inf2 Instances
Deep dive into Inf2 instance types, NeuronCores, memory configurations, and optimal inference workload placement.
3. Trainium
Explore Trn1 and Trn2 instances for model training, distributed training capabilities, and cost comparisons with GPUs.
4. Neuron SDK
Learn the AWS Neuron SDK, compiler, runtime, tools, and framework integrations with PyTorch and TensorFlow.
5. Deployment
Deploy models on Inferentia with SageMaker, ECS, EKS, and custom EC2 setups for production inference.
6. Best Practices
Optimization techniques, model compilation strategies, monitoring, and cost-performance best practices.
What You'll Learn
By the end of this course, you'll be able to:
Understand Custom Silicon
Know when and why to choose Inferentia or Trainium over traditional GPU instances for your ML workloads.
Deploy on Inferentia
Compile, optimize, and deploy models on Inf2 instances for high-throughput, low-latency inference.
Train on Trainium
Set up distributed training jobs on Trn1 instances with the Neuron SDK and popular ML frameworks.
Optimize Costs
Achieve significant cost savings by leveraging AWS custom chips for inference and training workloads.