Write your own training loop — without managing GPUs — with Amazon SageMaker training session APIs
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Amazon SageMaker AI training session APIs let you write your own LLM training loop (SFT, RL, or custom objectives) in a Python script while the service runs the GPUs. This post introduces the programming model and three SDK clients, then walks through a GRPO loop on GSM8K and a reinforcement learning workload on competitive programming.
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 the underlying infrastructure and training frameworks. Sometimes a standard recipe isn’t enough:…
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