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PiperOrigin-RevId: 362075728

PiperOrigin-RevId: 342770296

PiperOrigin-RevId: 342770296

PiperOrigin-RevId: 331359058

PiperOrigin-RevId: 331359058

PiperOrigin-RevId: 326286926

PiperOrigin-RevId: 326286926

PiperOrigin-RevId: 321890251

PiperOrigin-RevId: 321890251

PiperOrigin-RevId: 310658964

PiperOrigin-RevId: 306994199

PiperOrigin-RevId: 306453144

PiperOrigin-RevId: 299160422

Remove not maintained code path.

PiperOrigin-RevId: 285869559

PiperOrigin-RevId: 282650416

To test, I did 50 fp32 runs and 50 fp16 runs. I used the following command:

python ncf_keras_main.py --dataset=ml-20m --num_gpus=1 --train_epochs=10 --clean --batch_size=99000 --learning_rate=0.00382059 --beta1=0.783529 --beta2=0.909003 --epsilon=1.45439e-7 --layers=256,256,128,64 --num_factors=64 --hr_threshold=0.635 --ml_perf --nouse_synthetic_data --data_dir ~/ncf_data_dir_python3 --model_dir ~/tmp_model_dir --keras_use_ctl

For the fp16 runs, I added --dtype=fp16. The average hit-rate for both fp16 and fp32 was 0.6365. I also did 50 runs with the mixed precision graph rewrite, and the average hit-rate was 0.6363. The difference is likely due to noise.

PiperOrigin-RevId: 275059871

Move movielens to recommendation

PiperOrigin-RevId: 269680664

--stop_threshold, --num_gpu, --hooks, --export_dir, and --distribution_strategy have been unexposed from models which do not use them

PiperOrigin-RevId: 268032080

--clean, --train_epochs, and --epochs_between_evals have been unexposed from models which do not use them

PiperOrigin-RevId: 267065651

262988559  by A. Unique TensorFlower<gardener@tensorflow.org>:

    Enable NCF TF 2.0 model to run on TPUStrategy.

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262971756  by A. Unique TensorFlower<gardener@tensorflow.org>:

    Internal change

262967691  by hongkuny<hongkuny@google.com>:

    Internal

--

PiperOrigin-RevId: 262988559

260228553  by priyag<priyag@google.com>:

    Enable transformer and NCF official model tests. Also fix some minor issues so that all tests pass with TF 1 + enable_v2_behavior.

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260043210  by A. Unique TensorFlower<gardener@tensorflow.org>:

    Add logic to train NCF model using offline generated data.

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259778607  by priyag<priyag@google.com>:

    Internal change

259656389  by hongkuny<hongkuny@google.com>:

    Internal change

PiperOrigin-RevId: 260228553

* Add force_run_distributed tests.

* Added enable_eager

* r/force_run_distributed/force_v2_in_keras_compile

* Adding force_v2 tests and FLAGs.

* Rename method to avoid conflict.

* Add cpu force_v2 tests.

* fix lint, wrap line.

* change to force_v2_in_keras_compile

* Update method name.

* Lower mlperf target to 0.736.

* Merged commit includes the following changes:
259442882  by hongkuny<hongkuny@google.com>:

    Internal

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259377621  by A. Unique TensorFlower<gardener@tensorflow.org>:

    Fix NCF serialization/de-serialization logic in NCF input pipeline to use tf.FixedLenFeature instead of raw string/binary decoding.

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259373183  by A. Unique TensorFlower<gardener@tensorflow.org>:

    Create binary to generate NCF training/evaluation dataset offline.

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259026454  by isaprykin<isaprykin@google.com>:

    Internal change

258871624  by hongkuny<hongkuny@google.com>:

    Internal change

257285772  by haoyuzhang<haoyuzhang@google.com>:

    Internal change

256202287  by A. Unique TensorFlower<gardener@tensorflow.org>:

    Internal change.

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254069984  by hongkuny<hongkuny@google.com>:
    Automated rollback of changelist 254060732.

254060732  by yifeif<yifeif@google.com>:
    Automated rollback of changelist 254027750.

254027750  by hongkuny<hongkuny@google.com>:

    Internal change

253118910  by hongkuny<hongkuny@google.com>:

    Internal change

251906769  by hongkuny<hongkuny@google.com>:

    Internal change

251303452  by haoyuzhang<haoyuzhang@google.com>:

    Internal change

PiperOrigin-RevId: 259442882

* Update ncf_keras_main.py

* 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.

* 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.

Add early stopping logic to ncf keras when desired threshold is met. Also change the default batch size to match the tuned hyperparams

* 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

* Remove contrib imports, or move them inline

* Use exposed API for FixedLenFeature

* Replace tf.logging with absl logging

* Change GFile to v2 APIs

* replace tf.logging with absl loggin in movielens

* Fixing an import bug

* Change gfile to v2 APIs in code

* Swap to keras optimizer v2

* Bug fix for optimizer

* Change tf.log to tf.keras.backend.log

* Change the loss function to keras loss

* convert another loss to keras loss

* Resolve comments and fix lint

* Add a doc string

* Fix existing tests and add new tests for DS

* Added tests for multi-replica

* Fix lint

* resolve comments

* make estimator run in tf2.0

* use compat v1 loss

* fix lint issue

* tmp commit

* tmp commit

* first attempt (without eval)

* Bug fixes

* bug fixes

* training done

* Loss NAN, no eval

* Loss weight problem solved

* resolve the NAN loss problem

* Problem solved. Clean up needed

* Added a todo

* Remove debug prints

* Extract get_optimizer to ncf_common

* Move metrics computation back to neumf; use DS.scope api

* Extract DS.scope code to utils

* lint fixes

* Move obtaining DS above producer.start to avoid race condition

* move pt 1

* move pt 2

* Update the run script

* Wrap keras_model related code into functions

* Update the doc for softmax_logitfy and change the method name

* Resolve PR comments

* working version with: eager, DS, batch and no masks

* Remove git conflict indicator

* move reshape to neumf_model

* working version, not converge

* converged

* fix a test

* more lint fix

* more lint fix

* more lint fixes

* more lint fix

* Removed unused imports

* fix test

* dummy commit for kicking of checks

* fix lint issue

* dummy input to kick off checks

* dummy input to kick off checks

* add collective to dist strat

* addressed review comments

* add a doc string

Add bisection based producer for increased scalability, enable fully deterministic data production, and use the materialized and bisection producer to check each other (via expected output md5's)