继续以自动工作负荷回收方式分发培训

正如分布的AI培训规模，硬件和软件依赖的失败日益干扰培训进展，使快速、可靠的恢复至关重要。 一个停滞的训练级别可以触发NVIDIA集体通信库(NCCL)超时，一个GPU节点可以停止对调度器的响应，或者一个疏导器可以减缓整个工作。 每一次干扰都是可以控制的。..

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As distributed AI training scales, failures across hardware and software dependencies increasingly disrupt training progress, making fast, reliable recovery essential. A stalled training rank can trigger an NVIDIA Collective Communications Library (NCCL) timeout, a GPU node can stop responding to the scheduler, or a straggler can slow the entire job. Each disruption is manageable on its own…

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