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PyTorch Monarch


We now live in a world where ML workflows (pre-training, post training, etc) are heterogeneous, must contend with hardware failures, are increasingly asynchronous and highly dynamic. Traditionally, PyTorch has relied on an HPC-style multi-controller model, where multiple copies of the same script are launched across different machines, each running its own instance of the application (often referred to as SPMD).

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PyTorch Monarch