Cost-Effective RL Post-Training with Ray and AWS Trainium
summary
Port of a Ray-based RL post-training pipeline to AWS Trainium uses AWS Neuron and PyTorch so existing Ray recipes run unchanged except for device="neuron". Rollout and trainer workers are reimplemented to use vLLM/Hugging Face models, FSDP/tensor parallelism, static-shape compiled decoders, and weight synchronization via all-gather across Trainium cores. GRPO training matches GPU learning curves and runs up to 1.7x faster on Trainium 2 versus a P5 baseline (8× H100), showing numeric fidelity and concrete end-to-end performance gains.
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chapters (7)
- 0:00Introduction to talk
- 1:53Ray for RL orchestration
- 6:05AWS Trainium overview
- 11:04Trainium integration with Ray
- 19:56Implementation details
- 20:59Performance results
- 21:20Summary and next steps
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