GitLab

* 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

* 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

* from NCF_input import NCFDataset for line 181

The type __NCFDataset__ is used in the type declaration on line 81 but it is never imported.

[flake8](http://flake8.pycqa.org) testing of https://github.com/tensorflow/models on Python 3.7.1

$ __flake8 . --count --select=E9,F63,F72,F82 --show-source --statistics__
```
./official/recommendation/data_preprocessing.py:180:3: F821 undefined name 'NCFDataset'
  # type: (str, str, dict, typing.Optional[str], bool, typing.Optional[str]) -> (NCFDataset, typing.Callable)
  ^
1    F821 undefined name 'NCFDataset'
1
```
__E901,E999,F821,F822,F823__ are the "_showstopper_" [flake8](http://flake8.pycqa.org) issues that can halt the runtime with a SyntaxError, NameError, etc. These 5 are different from most other flake8 issues which are merely "style violations" -- useful for readability but they do not effect runtime safety.
* F821: undefined name `name`
* F822: undefined name `name` in `__all__`
* F823: local variable name referenced before assignment
* E901: SyntaxError or IndentationError
* E999: SyntaxError -- failed to compile a file into an Abstract Syntax Tree

* int, int, data_pipeline.BaseDataConstructor

https://mypy.readthedocs.io/en/latest/cheat_sheet.html

[flake8](http://flake8.pycqa.org) testing of https://github.com/tensorflow/models on Python 3.7.1

$ __flake8 . --count --select=E9,F63,F72,F82 --show-source --statistics__
```
./official/recommendation/data_pipeline.py:346:41: F821 undefined name 'string'
               epoch_dir=None           # type: string
                                        ^
```

* Add support for TPUEstimator to data processing pipeline and add the ability to store epochs in user specified location.

* Fix ncf test for keras

* add a todo for batch_size and eval_batch_size for ncf keras

* lint fix

* fix typos

* Lint fix

* fix lint

* resolve pr comment

* resolve pr comment

* fix resnet breakage and add keras end-to-end tests

* delint

* address PR comments

* 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

Explicitly allow for script execution from any directory.  Make env vars visible in python script. (#6105)

This reverts commit 63f5827d.

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)

2nd half of rough replacement pass

fix dataset map functions

reduce bias in sample selection

cache pandas work on a daily basis

cleanup and fix batch check for multi gpu

multi device fix

fix treatment of eval data padding

print data producer

replace epoch overlap with padding and masking

move type and shape info into the producer class and update run.sh with larger batch size hyperparams

remove xla for multi GPU

more cleanup

remove model runner altogether

bug fixes

address subtle pipeline hang and improve producer __repr__

fix crash

fix assert

use popen_helper to create pools

add StreamingFilesDataset and abstract data storage to a separate class

bug fix

fix wait bug and add manual stack trace print

more bug fixes and refactor valid point mask to work with TPU sharding

misc bug fixes and adjust dtypes

address crash from decoding bools

fix remaining dtypes and change record writer pattern since it does not append

fix synthetic data

use TPUStrategy instead of TPUEstimator

minor tweaks around moving to TPUStrategy

cleanup some old code

delint and simplify permutation generation

remove low level tf layer definition, use single table with slice for keras, and misc fixes

missed minor point on removing tf layer definition

fix several bugs from recombinging layer definitions

delint and add docstrings

Update ncf_test.py. Section for identical inputs and different outputs was removed.

update data test to run against the new producer class

This tag should match EVAL_HP_NUM_NEG.

I've noticed sometimes the async process's pool processes do not die when ncf_main.py ends and kills the async process. This commit fixes the issue.

* Keras-ify TPU embedding lookup

* delint

* pull get_variable() out of keras lambda

* delint

* move get_variable under variable scope