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AWS adds training session APIs to Amazon SageMaker AI

AWS released training session APIs for Amazon SageMaker AI to let developers write custom model training loops without managing GPUs.

AWS adds training session APIs to Amazon SageMaker AI — image from AWS
Image: AWS

Summary

Amazon SageMaker AI added training session APIs to let developers write custom model training loops in Python while the service handles the underlying GPU compute. The APIs expose operations like forward_backward, optim_step, and sample for custom supervised fine-tuning, reinforcement learning, and preference optimization. Usage is billed per token processed so you pay only for the compute your loop consumes.

What changed

  • AWS introduced training session APIs in Amazon SageMaker AI to abstract compute instead of algorithms.
  • Developers can call forward_backward, optim_step, and sample directly from their own Python processes.
  • AWS released a training sessions cookbook with ready-to-run SFT and RL recipes as blueprints.

Availability

Available now

“Today, serverless model customization in Amazon SageMaker AI expands with training session APIs, which remove that tradeoff by drawing the abstraction boundary one level lower.”

Founder takeaway

Review the SageMaker training sessions cookbook and new APIs if you need to build custom fine-tuning or reinforcement learning loops without managing GPU clusters.

Source

AWSWrite your own training loop — without managing GPUs — with Amazon SageMaker training session APIs | Amazon Web Services

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