track iteration rate
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@ -186,9 +186,10 @@ class Trainer:
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#### training
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if self._profile:
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print("Update LR: %f" % (time() - _t))
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_t = time()
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_t = time()
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self.model.feed_data(train_data, self.current_step)
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gradient_norms_dict = self.model.optimize_parameters(self.current_step, return_grad_norms=will_log)
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iteration_rate = (time() - _t) / batch_size
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if self._profile:
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print("Model feed + step: %f" % (time() - _t))
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_t = time()
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@ -202,7 +203,8 @@ class Trainer:
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if will_log and self.rank <= 0:
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logs = {'step': self.current_step,
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'samples': self.total_training_data_encountered,
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'megasamples': self.total_training_data_encountered / 1000000}
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'megasamples': self.total_training_data_encountered / 1000000,
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'iteration_rate': iteration_rate}
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logs.update(current_model_logs)
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if self.dataset_debugger is not None:
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logs.update(self.dataset_debugger.get_debugging_map())
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