GitLab

* 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

* Fix trivial model to work properly with fp16

* Add comment on manual casting

Previously we had one dense layer in trivial model. The weight was [224*224*3, num_classes].
Using two dense layers, the weights are [224*224*3, 1] and [1, num_classes].

* Do not report metrics in performance benchmarks

* Rename flag

* Modified tweaked tests to use tensor learning rate

* 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.

* 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

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

* 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.

* 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.

* 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.

* 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

* Added main function to the two keras resent benchmarks

* remove asserts

Forgot to add the `dtype='fp16'` flag in previous CL...

* 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

* Add benchmarks for dynamic loss scaling.

I add one convergence benchmark and five performance benchmarks.

* Fix lint error

This should give better performance in tweaked benchmarks.

Reason: break 1-gpu nightly test.

This reverts commit 371645fc.

Don't pass `batch_size` to keras.layers.Input in DS multi-replica case. There is currently a bug in Keras side which will cause a batch size incompatible error.

* 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.

* Add tweaked test for fp16 nonXLA

* Add dummy test for dataset warmup