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@ -206,6 +206,8 @@ class Model:
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function respectively.
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callbacks (list): List of callback object. Callbacks which should be executed while training. Default: None.
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dataset_sink_mode (bool): Determines whether to pass the data through dataset channel. Default: True.
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Configure pynative mode, the training process will be performed with
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dataset not sink.
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"""
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epoch = check_int_positive(epoch)
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self._train_network.set_train()
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@ -227,8 +229,13 @@ class Model:
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cb_params.train_dataset = train_dataset
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cb_params.list_callback = list_callback
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if dataset_sink_mode and context.get_context("mode") == context.GRAPH_MODE:
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self._train_dataset_sink_process(epoch, train_dataset, list_callback, cb_params)
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if dataset_sink_mode:
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if context.get_context("mode") == context.PYNATIVE_MODE:
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logger.warning("The pynative mode cannot support dataset sink mode currently."
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"So the training process will be performed with dataset not sink.")
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self._train_process(epoch, train_dataset, list_callback, cb_params)
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else:
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self._train_dataset_sink_process(epoch, train_dataset, list_callback, cb_params)
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else:
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self._train_process(epoch, train_dataset, list_callback, cb_params)
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@ -349,7 +356,7 @@ class Model:
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"""
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Training API where the iteration is controlled by python front-end.
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Configure to pynative mode, the training will be performed with dataset non-sink mode.
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When setting pynative mode, the training process will be performed with dataset not sink.
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Note:
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CPU is not supported when dataset_sink_mode is true.
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@ -363,6 +370,8 @@ class Model:
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function respectively.
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callbacks (list): List of callback object. Callbacks which should be excuted while training. Default: None.
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dataset_sink_mode (bool): Determines whether to pass the data through dataset channel. Default: True.
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Configure pynative mode, the training process will be performed with
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dataset not sink.
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Examples:
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@ -508,7 +517,7 @@ class Model:
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self._clear_metrics()
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if dataset_sink_mode and context.get_context("mode") == context.GRAPH_MODE:
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if dataset_sink_mode:
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return self._eval_dataset_sink_process(valid_dataset, list_callback, cb_params)
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return self._eval_process(valid_dataset, list_callback, cb_params)
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