Amazon SageMaker AI adds APIs for custom training loops
- Amazon SageMaker AI has introduced training session APIs that let developers define their own training loop while the service operates the GPU infrastructure.
- A session provides training and sampling operations against a model kept on GPUs. Developers can combine these calls for supervised fine-tuning, preference optimization, or reinforcement learning.
- Developers control the data, loop logic, and reward function, while SageMaker AI handles GPU-bound work. Charges are based on processed tokens, and local reward computation does not incur GPU charges.
- AWS has also released a cookbook with ready-to-run supervised fine-tuning and reinforcement learning examples. Its Code RL recipe uses competitive programming tasks and unit tests to score generated solutions.
Our take
Compared with SageMaker Training Jobs or HyperPod, sessions reduce the operational work of running GPU infrastructure while leaving the training algorithm under developer control. They require more loop and reward logic than Serverless Model Customization, so teams should weigh that engineering effort against the benefit of custom methods. The post does not give unit prices, so the cost case depends on token usage and the workload’s local scoring needs.
Sources
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