GitLab

* 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.

* Adding export_dir and model saving for Resnet

* Moving to utils for tests

* Adding batch_size

* Adding multi-gpu export warning

* Responding to CR

* Py3 compliance

* Add benchmark upload util to bigquery.

Also update the benchmark logger and bigquery schema for the
errors found during the integration test.

* Fix lint error.

* Update test to clear all the env vars during test.

This was causing error since the Kokoro test has TF_PKG=tf-nightly
injected during test.

* Update lintrc to ignore google related package.

* Another attempt to fix lint import error.

* Address the review comment.

* Fix lint error.

* Another fix for lint.

* Update test comment for env var clean up.

* Update reset model for benchmark logging.

To enable benchmark logging, just add "--hooks LoggingMetricHook"

* Benchmark logger fix for resnet.

1. Update default at_end to False for metric logger to avoid
checkpoint error.
2. Update resnet run to log final evaluation result.

* Update log output for final eval_result.

* Typo fix.

* Unset the default value for benchmark_log_dir.

Usually the benchmark should be logged to different directly for
each run. Having a default value will hide the choice from user.

* Bug fix for benchmark logger initialization.

* Fix lint error.

* Address the review comment.

1. Update the logger to cover evaluation result.
2. Move the flag to performance parser.

* Undo the change for arg_parser.

* Glint everything

* Adding rcfile and pylinting

* Extra newline

* Few last lints

Use util functions hooks_helper and parser in mnist and wide_deep, and rename epochs_between_eval (from epochs_per_eval) (#3650)

This commit adds a basic end to end test for resnet cifar10 and imagenet models to check for syntax errors outside of the core neural net code.