GitLab

This combination does not yet work. Fail early with an explicit message instead of throwing error later on.

* Ncf perf changes 1)exclude metric layer from CTL train step 2)dataset optimization to fix size of the sample_weights, preventing a costly broadcast during loss calculation for multi-gpu case

* reduce iterations from 20 to 12.

* add fp16 dynamic batch accuracy check.

* fix existing lint issue.

* Fix unit tests failures.

* 96% of TF 2.0 tests on GPU are passing.

* Currently all passing GPU and CPU TF 2.0

* Address code comments.

* use tf 2.0 cast.

* Comment about working on TF 2.0 CPU

* Uses contrib turn off for TF 2.0.

* Fix wide_deep and add keras_common_tests.

* use context to get num_gpus.

* Switch to tf.keras.metrics

* borrowing a tf1.x optimization which converts gradients from sparse to dense for better perf

* cleanup after code review

* XLA FP32 and first test

* More XLA benchmarks FP32.

* Add eager to NCF and refactor resnet.

* fix v2_0 calls and more flag refactor.

* Remove extra flag args.

* 90 epoch default

* add return

* remove xla not used by estimator.

* Remove duplicate run_eagerly.

* fix flag defaults.

* Remove fp16_implementation flag option.

* Remove stop early on mlperf test.

* remove unneeded args.

* load flags from keras mains.

* adding a new perf test for ncf, and changing some names

* Added change to make ncf use the data from the gcp bucket, and removed the need to re-download data >1day old. Reorganized the perf-zero tests

* Add CTL benchmark

* Divide train loss by number of train steps

* increase num epochs to 10

* add benchmark for early stopping with CTL

* remove whitespace

* Fix various lint errors

* Fix logging format

* Add flag to use custom training loop for keras NCF model.

* Add error check to NCF model for custom training loop + tf1.0.

* Add a custom training loop for NCF model with TF2.0

* Fix long line in ncf_keras_main.py

* Remove dataset repeat when using custom training loop.