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* set strip_default_attrs=True for SavedModel exports

* specify dtype in resnet export

* another dtype fix

* fix another dtype issue, and set --image_bytes_as_serving_input to default to False

with serving signature that accepts JPEG image bytes instead
of a fixed size [HxWxC] image tensor.

Passing JPEG image bytes is easier for inference/serving use
cases. The model internally resizes/crops the JPEG image to
required [HxWxC] tensor before passing it on for actual model
inference.

This change aligns with Cloud TPU/ResNet-50 model that offers a
similar interface (jpeg bytes) for inferencing here:

https://github.com/tensorflow/tpu/tree/master/models/official/resnet

NOTE: This flag is set to `True` by default for ImageNet, and is
disallowed for CIFAR (as it does not apply to CIFAR).

* Make ResNet robust to the case that epochs_between_evals does not divide train_epochs, and add an --eval_only option

* add some comments to make the control flow easier to follow

* address PR comments

* Add 5 epoch warmup

* get_lr with warm_up only for imagenet

* Add base_lr, remove fp16 unittest arg validation

* Remove validation check stopping v1 and FP16

* Add top_5 to to eval.

* labels shape to [?] from [?,1] matches unittest.

* warm start a resent with all but the dense layer and only update the final layer weights when fine tuning

* Update README for Transfer Learning

* make lint happy and variable naming error related to scaled gradients

* edit the test cases for cifar10 and imagenet to reflect the default case of no fine tuning

Removed the conditional over distributed strategies when computing metrics.
Metrics are now computed even when distributed strategies are used.

* begin branch

* finish download script

* rename download to dataset

* intermediate commit

* intermediate commit

* misc tweaks

* intermediate commit

* intermediate commit

* intermediate commit

* delint and update census test.

* add movie tests

* delint

* fix py2 issue

* address PR comments

* intermediate commit

* intermediate commit

* intermediate commit

* finish wide deep transition to vanilla movielens

* delint

* intermediate commit

* intermediate commit

* intermediate commit

* intermediate commit

* fix import

* add default ncf csv construction

* change default on download_if_missing

* shard and vectorize example serialization

* fix import

* update ncf data unittests

* delint

* delint

* more delinting

* fix wide-deep movielens serialization

* address PR comments

* add file_io tests

* investigate wide-deep test failure

* remove hard coded path and properly use flags.

* address file_io test PR comments

* missed a hash_bucked_size

* Add DistributionStrategy to transformer model

* add num_gpu flag

* Calculate per device batch size for transformer

* remove reference to flags_core

* Add synthetic data option to transformer

* fix typo

* add import back in

* Use hierarchical copy

* address PR comments

* lint

* fix spaces

* group train op together to fix single GPU error

* Fix translate bug (sorted_keys is a dict, not a list)

* Change params to a default dict (translate.py was throwing errors because params didn't have the TPU parameters.)

* Address PR comments. Removed multi gpu flag + more

* fix lint

* fix more lints

* add todo for Synthetic dataset

* Update docs

* fix comment

* align comment

* add .take() to dataset pipeline

* delint

* address PR comments

This will help us differentiate between dataset.

* Add new test ID and test env info to the benchmark run.

* Fix test.

* Fix lint

* Address review comment.

* Update benchmark logger to update the run status.

This is important for streaming upload to bigquery so that the
dashboard can ignore the 'running' benchmark at the moment since
its not finished yet.

* Move the run status into a separate table.

Also update the run status in the benchmark uploader and
BigqueryBenchmarkLogger.

* Insert instead of update for the benchmark status for file logger.

* Address review comments.

Update the logger to have benchmark context, which will update the
run status accordingly.

* Fix broken tests.

* Move the benchmark logger context to main function.

* Fix tests.

* Update the rest of the models to use the context in main.

* Delint.

* Remove one hot labels, Add drop_remainder to batch, Use parallel interleve in imagenet dataset.

* minor lint fix

* Don't try to read the files twice...

* Add explanation for cycle_length

* Move the benchmark_uploader to new location.

* Update benchmark logger to streaming upload.

* Fix lint and unit test error.

* delint.

* Update the benchmark uploader test.

Skip the import of benchmark_uploader when bigquery is not installed.

* Merge the 2 classes of benchmark uploader into 1.

* Address review comments.

* delint.

* Execute bigquery upload in a separate thread.

* Change to use python six.moves for importing.

* Address review comments and delint.

* Address review comment.

Adding comment for potential performance impact for model on CPU.

* Fix random failure on py3.

* Fix the order of flag saver to avoid the randomness.

The test is broken when the benchmark_logger_type is set first, and
validated when the benchmark_log_dir is not set yet.

* forbid resnet v1 fp16

* address PR comments

* rename --version flag and fix tests to correctly specify version rather than verbosity

* rename version to resnet_version throughout

* fix bugs

* delint

* missed layer_test

* fix indent

* Add dataset info and hyper parameter logging for benchmark.

* Address review comments.

* Address the view comment for data schema name.

* Fix test cases.

* Lint fix.

* Revert 823da318. This restores distribution strategies for resnet.

This commit is not a direct revert due to significant merge conflict
resolution.

* fix flags test

* npc is no longer used in resnet

* squash of modular absl usage commits

* delint

* address PR comments

* change hooks to comma separated list, as absl behavior for space separated lists is not as expected

1. trainning hooks and train/eval function does not need to be
declared several times.
2. change to use tf.logging.info instead of print
3. Show the current index and total count of training cycle, which
give user some hint about where they are in the whole process.

* Update the benchmark logger to have default logging.

1. Create global instance of benchmark logger, which default log to
tf.logging.info
2. Allow user to config the logging location.
3. Fix nits in code and comment.

* Fix lint and test error.

* Address review comments.

* Remove the duplicated print statement.

This reverts commit 32aa6563.

* begin transfer from contrib fork

more changes to resnet_run_loop

use AUTOTUNE in prefetch

first pass at resnet with functional distribution strategies

fix syntax error

delint

aesthetic tweaks

delint and fix typos

rip multi_gpu flag out of resnet entirely. Subject to saved model load verification

update cifar10 and imagenet tests to reflect that the model function no longer need to know about multi_gpu

fix imagenet test

start addressing PR comments

more PR response work

* misc tweaks

* add a comment

* final pr tweaks

* fix parsers

* Adding tests

* Adding tests

* Repackaging

* Adding logging

* Linting

* Add fp16 support to resnet.

* address PR comments

* add dtype checking to model definition

* delint

* more PR comments

* few more tweaks

* update resnet checkpoints

* Updating name of logging package to avoid overwriting Python builtin logging.

* Updating name of logging package to avoid overwriting Python builtin logging.