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首先，The team outlines cache-enabled pipeline communication and dual-phase computation strategies that facilitate distributed training and inference. These innovations yield under 4% training overhead in pipeline parallel environments, with repository data indicating less than 2% inference latency increase on standard workloads.
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最后，We build a neural ordinary differential equation model using Equinox to represent the system dynamics with a neural network. We define a loss function and optimization procedure using Optax so that the model can learn the underlying dynamics from data. We then train the neural ODE using the differential equation solver and evaluate its performance, benchmarking the solver with JAX’s JIT compilation.
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