AWS 推出 SageMaker 训练会话 API：自写训练循环免管 GPU
原标题：Write your own training loop — without managing GPUs — with Amazon SageMaker training session APIs
AI 摘要
AWS 在 Amazon SageMaker AI 的无服务器模型定制中推出训练会话 API，将抽象层从算法下移到计算，开发者可用 forward_backward()、optim_step()、sample() 等 API 自行编写 SFT、偏好优化或强化学习训练循环，而 GPU 分配、分布式执行、梯度同步与故障恢复由 SageMaker 托管。该服务按处理的 token 计费，并配套发布包含 SFT 与 RL 示例的 cookbook。
正文节选
Write your own training loop — without managing GPUs — with Amazon SageMaker training session APIs Fine-tuning a large language model (LLM) today usually forces a tradeoff between training loop control and operational simplicity. With fully managed services you select a base model, provide data and hyperparameters, and the service owns everything else, which works well when a standard, prepackaged recipe fits your problem. Less managed services give you more control, but you also manage more of