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AWS Launches SageMaker AI Training Sessions for Custom Model Training Loops

2 reports 1 reporting sources 1 hours agoNew

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AWS made SageMaker AI training session APIs generally available, letting developers write custom Python post-training loops while the service manages GPU allocation, distributed execution, fault recovery, and checkpoint storage. Developers control loss functions and reward signals. At launch, training sessions support gpt-oss-20b, gemma-4-31B-it, Qwen3.6-27B, and Qwen3.8-27B. AWS bills for tokens processed by SageMaker AI, with billing paused while a reward function scores a batch in the developer’s own environment; developers do not need to reserve capacity. The accompanying cookbook includes supervised fine-tuning and reinforcement learning recipes. In its competitive programming example, AWS reported that GPT-OSS 20B’s held-out accuracy increased from 40 percent to 50.5 percent on 200 test problems after 50 training steps.

Generated by AI from reports · Updated 1 hours ago

Latest development10/23 02:00 (UTC+8)
AWS launches SageMaker AI training sessions for model customization without managing GPUsAWS Machine Learning Blog

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10/23
  1. AWS Machine Learning BlogPicks
    AWS launches SageMaker AI training sessions for model customization without managing GPUs

    AWS announced general availability of SageMaker AI serverless training sessions, letting developers write custom post-training loops while SageMaker AI manages GPU allocation, distributed execution, fault recovery, and checkpoint storage. Developers control loss functions and reward signals, with token billing paused while their environment scores responses. Available today, training sessions support gpt-oss-20b, gemma-4-31B-it, Qwen3.6-27B, and Qwen3.8-27B.

  2. AWS Machine Learning BlogPicks
    Amazon SageMaker training session APIs enable custom training loops on managed GPUs

    AWS introduced Amazon SageMaker AI training session APIs, letting developers write custom Python training loops while the service manages GPU infrastructure, distributed execution, fault recovery, and checkpointing. Usage is billed per token processed, excluding local reward computation time. The accompanying cookbook provides SFT and RL recipes, including a competitive programming example where GPT-OSS 20B held-out accuracy rose from 40 percent to 50.5 percent on 200 test problems after 50 training steps.

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