Write your own training loop — without managing GPUs — with Amazon SageMaker training session APIs

Amazon SageMaker AI introduces training session APIs that let you write a custom training loop in Python on your local machine while SageMaker runs GPU-bound operations such as forward_backward(), optim_step(), and sample() on managed accelerators with your base model and LoRA adapter. The APIs are organized around ServiceClient, TrainingClient, and SamplingClient in the SageMaker Python SDK, and the post walks through a minimal GRPO loop on GSM8K (held-out accuracy rising from 56% to 75% in 10 steps) plus a Code RL recipe on competitive programming problems (held-out accuracy from 40% to 50.5%). A companion cookbook provides ready-to-run SFT and RL recipes.