Developer builds Python-PyTorch LLM training engine that runs 1 million steps without OOM errors

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A software engineer has detailed the architecture of MEM v3 (Model Execution Manager), a custom orchestration engine built with Python, PyTorch, and DeepSpeed designed to sustain one million LLM training steps without out-of-memory failures. The system addresses three common pain points in large language model training: GPU memory crashes, hyperparameter instability from external AI agents, and loss of progress due to inadequate checkpointing. MEM v3 uses a Zero-Trust policy layer that intercepts and validates any hyperparameter changes suggested by external AI planners before they are applied to the training run. The engine also incorporates adaptive memory management and rotating checkpoints to maintain long-term stability across extended, GPU-intensive workloads. Its architecture follows Clean Architecture and Domain-Driven Design principles, fully decoupling orchestration logic from hardware execution.

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