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This options allows the new tf.train.experimental.enable_mixed_precision_graph_rewrite() function to be used for fp16, instead of manual casts.

Replace per_device with per_replica and PerDevice with PerReplica, because the PerDevice concept was renamed and doesn't exist anymore. (#6693)

* Replace per_device with per_replica and PerDevice with PerReplica, because the PerReplica concept was renamed and doesn't exist anymore.

* Add accuracy check.

* Avoid double flag init, move data_dir to real data.

* Comment on lower accuracy target.

* bug fix

* bug fix

* Add benchmarks with the --cloning flag to Resnet and NFC.

* Renamed cloning to clone_model_in_keras_dist_strat. Dropped a few tests that aren't essential.

* Fixed up the formatting after re-naming the flag to a much longer  name.  Thanks, lint.
* Fixed the lint error in nfc_common.py

* Combined imagenet and cifar-10 benchmarks

* Comments and epochs_between_evals.

* Added tuned tests and cleaned up benchmark flags

* Fix names.

* Return results and add images/sec hook.

* updated doc strings for return values.

* 128 to 256 batch for FP16 test

* added more doc strings to fix lint.

* Add num_packs flag for MirroredStrategy's cross device ops.

* fix parens

* Fix lint errors and make all_reduce_alg more robust.

* Set default num_packs to 1

tf.test.is_gpu_available() should not be called in flags since this is
called before app.main() and the runtime has not yet been initialized.

* Remove contrib AllReduceCrossDeviceOps and update all_reduce_alg options with MirroredStrategy.

* cleanup

Reason: test failures because contrib is not available in V2

This reverts commit 325dd761.

* Added none check for output_dir

* Change double quote to single

* Introduce a short sleep before ds.prefetch in tf.data.

* Further limit dataset threads to reduce CPU contention

* Tuned dataset sleep time

* Rename dataset sleep flag; enable it only for Keras Graph mode

* Small word tweak

* Few more tweaks

Added additional information on using the `SavedModel` for prediction purposes.

* Use tf.image.resize_with_crop_or_pad

* exp_per_second and hr_at_10

* Remove contrib imports, or move them inline

* Use exposed API for FixedLenFeature

* Replace tf.logging with absl logging

* Change GFile to v2 APIs

* replace tf.logging with absl loggin in movielens

* Fixing an import bug

* Change gfile to v2 APIs in code

* Swap to keras optimizer v2

* Bug fix for optimizer

* Change tf.log to tf.keras.backend.log

* Change the loss function to keras loss

* convert another loss to keras loss

* Resolve comments and fix lint

* Add a doc string

* Fix existing tests and add new tests for DS

* Added tests for multi-replica

* Fix lint

* resolve comments

* make estimator run in tf2.0

* use compat v1 loss

* fix lint issue

* before moving test cases to the base class

* Added tests for keras cifar and keras imagenet

* fix cifar10_test

* add blank lines

* fix lint errors

* fix lint

* Resolve comments

* Modified two resnet keras tests

* Tests passed

* Remove keras_test_base

* Remove gpu from the no-dist tests

* Revert "Set input layer `batch_size` in multi-replica mode (#6578)"

This reverts commit f1a59682.

* Rename variables.

* Update resnet_run_loop.py

* Update resnet_run_loop.py

* Update resnet_run_loop.py

* Update resnet_run_loop.py

* Update resnet_run_loop.py

* Update README.md

* Update README.md

* Update README.md

* Revert "Revert " Ensure static shapes when enabling XLA in Resnet Keras model (#6508)" (#6517)"

This reverts commit cc9eef76.

* Set `batch_size` to keras.Input in non-eager mode.

Eager mode currently has OOM problem.

* Add comments for enable_eager flag.

* Always set drop_remainder=True.

* Only set drop_remainder=True for XLA.

* Make BatchTimestamp object printable.

* Removing trailing whitespace.

* Make BatchTimestamp repr a string.

* Refactored ResNet code and added additional architectures.

* Added numerical layer names instead of alphabetical.

* Change dash to underscore.

* Corrected return statement.

* Use conv_strides argument.

* Set classes=10

* Use partial to reduce code duplication.

With trivial model, it improves the data input pipeline throughput from 12.5K to 15K on a DGX1 V100 machine.

* add ds support for ncf

* remove comments for in_top_k

* avoid expanding the input layers

* resolve comments and fix lint

* Added some comments in code and fix lint

* fix lint

* add some documentation

* add tensorflow imports