Simplifying distributed ML through a unified API Writing fault-tolerant distributed programs is complex and a process that’s prone to errors. For example, consider the distributed evaluation of a deep network. The first step is to send a multi-GB model to hundreds of worker machines without overwhelming the network. Then, data readers must coordinate to ensure that all data is queued for processin
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