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For all modified calls to set_mixed_precision_policy(), the loss_scale argument was removed, as it cannot be passed if the nonexperimental API is used. For all such callers, the loss_scale is later used to explicitly create a LossScaleOptimizer, so removing the argument has no impact.

Switching to the non-experimental LossScaleOptimizer has no effect, as it has near identical behavior and all isinstance checks within the official models check for the non-experimental version.

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This shouldn't break any official models, since I changed all LossScaleOptimizer isinstance checks to use the nonexperimental version (the experimental LSO subclasses the nonexperimental LSO, so changing isinstance checks in this way is always safe).

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The default is True, but I plan on changing it to False soon. After that, I plan on removing the argument and never using the experimental API.

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https://www.tensorflow.org/datasets/api_docs/python/tfds/load

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[core] Update BestCheckpointExporter to support dictionary with arbitrary depth (possibly coming from multitask eval).
Also added best checkpointexporter support to multitask.

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- Create shadow weights at the beginning of training.
- Swap the weights during.
- Save best checkpoint with average weights.

The following fields need to be set in order to activate the best checkpoint exporter.
best_checkpoint_eval_metric
best_checkpoint_export_subdir
best_checkpoint_metric_comp

To serve, or to finetune the trained checkpoints on a target dataset, use checkpoints under best_checkpoint_export_subdir.

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