Distributed training is a model training paradigm that involves spreading training workload across multiple worker nodes, therefore significantly improving the speed of training and model accuracy.

Abstract: Many distributed optimization algorithms critically depend on careful step-size tuning to ensure stability and achieve fast convergence. In this work, we address this limitation in the ...

Abstract: In this paper, the problem of distributively seeking the equilibria of aggregative games with bilevel structures is studied. Different from the traditional aggregative games, here the ...

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* Pre-train a GPT-2 (~124M-parameter) language model using PyTorch and Hugging Face Transformers. * Distribute training across multiple GPUs with Ray Train with minimal code changes. * Stream training ...

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Macao police officers escorted a humanoid robot off a residential street on the evening of March 5 after the machine, ...

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