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@@ -25,7 +25,7 @@ class TrainingAverager(DecentralizedAverager):
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:param average_parameters: whether or not to average model parameters in self.step(...)
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:param average_parameters: whether or not to average model parameters in self.step(...)
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:param average_gradients: whether or not to average model gradients in self.step(...)
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:param average_gradients: whether or not to average model gradients in self.step(...)
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:param average_opt_statistics: if specified, average optimizer statistics with corresponding names in statedict
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:param average_opt_statistics: if specified, average optimizer statistics with corresponding names in statedict
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- :param scheduler: if specified, averager keeps scheduler state
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+ :param scheduler: if specified, averager stores scheduler state
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:param initialize_optimizer: if True, this will run a speculative optimizer step with
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:param initialize_optimizer: if True, this will run a speculative optimizer step with
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zero gradients to initialize all tensors. If False, please initialize the optimizer state manually.
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zero gradients to initialize all tensors. If False, please initialize the optimizer state manually.
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:param extra_tensors: if specified, these extra tensors will also be averaged and shared in load_state_from_peers.
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:param extra_tensors: if specified, these extra tensors will also be averaged and shared in load_state_from_peers.
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justheuristic