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

This article explains Amazon SageMaker AI's training session APIs, which allow users to run custom training loops (like SFT or RL) on managed GPU infrastructure without provisioning or managing the underlying compute. The API abstracts away GPU allocation, distributed execution, and checkpointing while giving full control over the training algorithm through a Python SDK hierarchy (ServiceClient → TrainingClient → SamplingClient).

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Create a SageMaker training loop for RL on GSM8K math problems using GRPO. Use the ServiceClient, TrainingClient, and SamplingClient from the SageMaker Python SDK. Define a reward function that compares model-generated answers to ground truth, compute advantages, and perform one gradient step per step. Include code for: 1) Starting a session with ServiceClient, 2) Getting tokenizer and sampling client, 3) The step loop with sample(), score(), forward_backward(), optim_step(), 4) Saving checkpoints. Replace placeholders (role_arn, region, SESSION_NAME, etc.) with your actual values.

Example input

Use the placeholders in the prompt with permitted, anonymized source data.

Expected output

Follow the output format requested in the prompt and verify the result against the source.

Tools

SageMaker Python SDK, ServiceClient, TrainingClient, SamplingClient