GitLab

* Support pure eager execution in ResNet50

* Use smaller batch size

* Add enable_get_next_as_optional flag.

* Set enable_get_next_as_optional to strategy.

* Add comments to explain the flag.

* Remove trailing whitespace.

* Remove trailing space.

* Set the --clone_model_in_keras_dist_strat to None.  Remove the separate no_cloning benchmarks and add a couple of cloning ones.  Fixes the learning rate schedule to cache its ops per graph.

* Added 'tfdata_exp' version of all benchmarks which set
FLAGS.tf_data_experimental_slack = True. Renamed
`data_prefetch_with_slack` to `data_delay_prefetch` (haoyu's change)
to make the names more distinct.

* Add flag to resnet input pipeline and surface through
keras_imagenet_main.py

* Do not report metrics in performance benchmarks

* Rename flag

* Add learning rate tensor. This makes training slower

* Improve LearningRateSchedule with better efficiency

* Fix lint error

* Replace constant definition with existing one

* Enable CuDNN BatchNorm spatial persistent by default; Remove 2nd zero padding layer

* Apply scale=False and fused=True consistently to BatchNorm layers

* Undo remove padding layer

* Replace zero padding with padding attribute in max pooling for better performance

* Resolve comments

* Revert "Replace zero padding with padding attribute in max pooling for better performance"

This reverts commit ad49db057c800ecac008eec1057005bd2c08ac73.

* 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

* 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

* 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.

* 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

* Add profiler callback for Keras models

* Update build stats to identify time callback by type

* Add warning message when both TensorBoard and profiler callbacks are used

This should give better performance in tweaked benchmarks.

Reason: break 1-gpu nightly test.

This reverts commit 371645fc.

* Update resnet_model.py

* Ensure static shapes when enabling XLA.

* Define `drop_remainder` as a variable.

* Handles per_replica_batch_size in non-XLA mode

* Remove trailing whitespace.

* initial commit

* bug fix

* Move build_stats from common to keras main, because it is only applicable in keras

* remove tailing blank line

* add test for synth data

* add kwargs to init

* add kwargs to function invokation

* correctly pass kwargs

* debug

* debug

* debug

* fix super init

* bug fix

* fix local_flags

* fix import

* bug fix

* fix log_steps flag

* bug fix

* bug fix: add missing return value

* resolve double-defined flags

* lint fix

* move log_steps flag to benchmarK flag

* fix lint

* lint fix

* lint fix

* try flag core default values

* bug fix

* bug fix

* bug fix

* debug

* debug

* remove debug prints

* rename benchmark methods

* flag bug fix for synth benchmark

* Added XLA test with a monkey-patched op to avoid OOM

* Added doc strings in Keras benchmarks to avoid Lint error

* Add flag to enable XLA in Keras models

* Fix lint errors (some of them are old errors)

* Add pure synthetic data to keras resnet mode.

* Add imports.

* Address comments.

* update comment

* Undo set up synthetic data for real data path.

* update comment

* Address comment

* Remove trailing whiltespaces.

* s/make_data_set_iterator/make_dataset_iterator/

This reverts commit 57e07520.

This reverts commit d6b2b83c.

* Add resnet56 short tests. (#6101)

* Add resnet56 short tests.
- created base benchmark module
- renamed accuracy test class to contain the word Accuracy
which will result in a need to update all the jobs
and a loss of history but is worth it.
- short tests are mostly copied from shining with oss refactor

* Address feedback.

* Move flag_methods to init
- Address setting default flags repeatedly.

* Rename accuracy tests.

* Lint errors resolved.

* fix model_dir set to flags.data_dir.

* fixed not fulling pulling out flag_methods.

* Use core mirrored strategy in official models (#6126)

* Imagenet short tests (#6132)

* Add short imagenet tests (taken from seemuch)
- also rename to match go forward naming

* fix method name

* Update doc strings.

* Fixe gpu number.

* points default data_dir to child folder. (#6131)

Failed test is python2  and was a kokoro failure

* Imagenet short tests (#6136)

* Add short imagenet tests (taken from seemuch)
- also rename to match go forward naming

* fix method name

* Update doc strings.

* Fixe gpu number.

* Add fill_objects

* fixed calling wrong class in super.

* fix lint issue.

* Flag (#6121)

* Fix the turn_off_ds flag problem

* add param names to all args

* Export benchmark stats using tf.test.Benchmark.report_benchmark() (#6103)

* Export benchmark stats using tf.test.Benchmark.report_benchmark()

* Fix python style using pyformat

* Typos. (#6120)

* log verbosity=2 logs every epoch no progress bars (#6142)

* tf_upgrade_v2 on resnet and utils folder.

* tf_upgrade_v2 on resnet and utils folder.

* Single timestamp list and return average_exp_per_second.

* Add paren to subtract before division.

* Add timestamp history for each batch in training

* Resolve github comments

* Change the batch start recording logic

* Lint fix