Amazon SageMaker training session APIs enable custom LLM training loops without GPU management
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AWS announced SageMaker training session APIs, allowing users to drive GPU-backed sessions from local Python processes for custom LLM training loops without managing GPU infrastructure. AWS handles GPU allocation, distributed execution, fault recovery, and checkpoint storage. Billing is based on processed tokens, with computation paused during reward function evaluation batches. The service does not support capacity reservations. Each session uses a user-created IAM role, supports custom KMS keys and VPC configuration, and stores user data in a regional S3 bucket without training other models.
Written with AI from the reporting · updated 10/22/2026
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New at this step ·Amazon SageMaker training session APIs let users customize LLM training loops without managing GPUs
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- AWS Machine Learning Blog · First-partyAmazon SageMaker training session APIs let users customize LLM training loops without managing GPUsOriginal source ↗
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2 developments10/23/2026, 2:10:40 AM
AWS SageMaker AI launches serverless model customization with training sessionsAWS Machine Learning Blog
10/23/2026, 2:10:40 AM
Amazon SageMaker training session APIs let users customize LLM training loops without managing GPUsAWS Machine Learning Blog
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- 1Oct 23, 00:00recorded Oct 23, 02:10single source
SageMaker AI serverless model customization training session
Read original source ↗ AWS Machine Learning BlogReplay this moment in the explorer - 2Oct 23, 02:10single sourcereport-time only
Amazon SageMaker training session APIs：无服务器 GPU 基础设施下的自定义训练循
Read original source ↗ AWS Machine Learning BlogReplay this moment in the explorer
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First-party sources · 2 reports
- AWS Machine Learning Blog · Oct 23, 02:00AWS SageMaker AI launches serverless model customization with training sessions
AWS SageMaker AI has launched general availability of serverless training sessions, enabling developers to customize open-weight models using post-training techniques like reinforcement learning without managing GPU infrastructure. Users can write custom training loops with tailored loss functions and reward signals, while SageMaker handles GPU allocation, distributed execution, and fault recovery.
Read original source ↗ - AWS Machine Learning Blog · Oct 23, 02:00Amazon SageMaker training session APIs let users customize LLM training loops without managing GPUs
Amazon SageMaker AI introduces training session APIs that let users drive GPU-backed sessions from local Python code. The service handles distributed execution, gradient synchronization, and fault recovery, while users retain control over training procedures such as custom loss functions and reinforcement learning reward functions. A new cookbook provides ready-to-run SFT and RL recipes as blueprints.
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- AWS Machine Learning BlogSelectedAWS SageMaker AI launches serverless model customization with training sessions
AWS SageMaker AI has launched general availability of serverless training sessions, enabling developers to customize open-weight models using post-training techniques like reinforcement learning without managing GPU infrastructure. Users can write custom training loops with tailored loss functions and reward signals, while SageMaker handles GPU allocation, distributed execution, and fault recovery.
- AWS Machine Learning BlogSelectedAmazon SageMaker training session APIs let users customize LLM training loops without managing GPUs
Amazon SageMaker AI introduces training session APIs that let users drive GPU-backed sessions from local Python code. The service handles distributed execution, gradient synchronization, and fault recovery, while users retain control over training procedures such as custom loss functions and reinforcement learning reward functions. A new cookbook provides ready-to-run SFT and RL recipes as blueprints.
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