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

- Company: Amazon (amazon.com)
- Announced: 2026-10-22T18:00:01+00:00
- Category: api-release
- Coverage: not counted
- Announcement: no
- Group: routine
- Source: https://aws.amazon.com/blogs/machine-learning/write-your-own-training-loop-without-managing-gpus-with-amazon-sagemaker-training-sessions/
- Record: https://forck.live/items/26794-write-your-own-training-loop-without-managing-gpus-with-amazon-sagemaker
- Subject: Bedrock / Nova
- Pricing: Usage is billed per token processed

Amazon SageMaker AI is expanding its serverless model customization with training session APIs, which let developers drive a live, GPU-backed session from their own Python process instead of handing the whole training job to a managed service. The APIs abstract away the compute rather than the algorithm, so operations like forward_backward(), optim_step() and sample() become SageMaker calls that developers compose into supervised fine-tuning, preference optimization, reinforcement learning or new alignment procedures. The service handles runtime setup, distributed execution, gradient synchronization, fault recovery, tenant isolation and GPU operations, while the model stays loaded on the GPUs across steps in a stateful session. The SageMaker Python SDK organizes the work around three clients created in sequence: a ServiceClient for platform settings and session start, a TrainingClient for training and checkpointing, and a SamplingClient for generation and scoring during rollouts and evaluation. An active session also serves an OpenAI-compatible chat completions endpoint, so existing OpenAI client code can sample from it. Amazon is releasing a training sessions cookbook with ready-to-run SFT and RL recipes, including reinforcement learning on competitive programming problems where generated code is graded against unit tests. Usage is billed per token processed, so no GPU time is charged while reward computation, data loading, logging or debugging run on the developer's machine.

## Evidence

Verbatim from https://aws.amazon.com/blogs/machine-learning/write-your-own-training-loop-without-managing-gpus-with-amazon-sagemaker-training-sessions/:

> 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. Instead of abstracting away the algorithm, they abstract away the compute.

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