GitLab

We've deprecated the "tower" terminology in DistributionStrategy, so the "cross_tower_ops" argument is now "cross_device_ops", matching the current name of "AllReduceCrossDeviceOps".

`num_replicas` is being replaced by `num_replicas_in_sync` and something that returns the number of steps running concurrently.

--ml_perf now just changes the model to make it MLPerf compliant. --output_ml_perf_compliance_logging adds the MLPerf compliance logs.

* move version check to a function

* delint

* tweak pip check

* delint

To match new terminology in DistributionStrategy.

* first pass at __getattr__ abuse logger

* first pass at adding tags to NCF

* minor formatting updates

* fix tag name

* convert metrics to python floats

* getting closer...

* direct mlperf logs to a file

* small tweaks and add stitching

* update tags

* fix tag and add a sudo call

* tweak format of run.sh

* delint

* use distribution strategies for evaluation

* address PR comments

* delint and fix test

* adjust flag validation for xla

* add prefix to distinguish log stitching

* fix index bug

* fix clear cache for root user

* dockerize cache drop

* TIL some regex magic

Since we plan on deleting this method, it is only used in distribution_utils_test.py.

if-else to if only

Spaces after equal signs as well

Spaces after commas

* perform a codecs check and remove unicode \ufeff if utf-8 is not present

* delint

* intermediate commit

* ncf now working

* reorder pipeline

* allow batched decode for file backed dataset

* fix bug

* more tweaks

* parallize false negative generation

* shared pool hack

* workers ignore sigint

* intermediate commit

* simplify buffer backed dataset creation to fixed length record approach only. (more cleanup needed)

* more tweaks

* simplify pipeline

* fix misplaced cleanup() calls. (validation works\!)

* more tweaks

* sixify memoryview usage

* more sixification

* fix bug

* add future imports

* break up training input pipeline

* more pipeline tuning

* first pass at moving negative generation to async

* refactor async pipeline to use files instead of ipc

* refactor async pipeline

* move expansion and concatenation from reduce worker to generation workers

* abandon complete async due to interactions with the tensorflow threadpool

* cleanup

* remove per...

* Add shorter timeout for GCP util.

* Add comment for change reason and unit for timeout.

* begin branch

* finish download script

* rename download to dataset

* intermediate commit

* intermediate commit

* misc tweaks

* intermediate commit

* intermediate commit

* intermediate commit

* delint and update census test.

* add movie tests

* delint

* fix py2 issue

* address PR comments

* intermediate commit

* intermediate commit

* intermediate commit

* finish wide deep transition to vanilla movielens

* delint

* intermediate commit

* intermediate commit

* intermediate commit

* intermediate commit

* fix import

* add default ncf csv construction

* change default on download_if_missing

* shard and vectorize example serialization

* fix import

* update ncf data unittests

* delint

* delint

* more delinting

* fix wide-deep movielens serialization

* address PR comments

* add file_io tests

* investigate wide-deep test failure

* remove hard coded path and properly use flags.

* address file_io test PR comments

* missed a hash_bucked_size

* remove unused imports and lint

* fix schedule.py

* address PR comments

* Add checklist for official models. Remove file access from flag validator (causing issues with BUILD)

* spelling

* address PR comments

* Add DistributionStrategy to transformer model

* add num_gpu flag

* Calculate per device batch size for transformer

* remove reference to flags_core

* Add synthetic data option to transformer

* fix typo

* add import back in

* Use hierarchical copy

* address PR comments

* lint

* fix spaces

* group train op together to fix single GPU error

* Fix translate bug (sorted_keys is a dict, not a list)

* Change params to a default dict (translate.py was throwing errors because params didn't have the TPU parameters.)

* Address PR comments. Removed multi gpu flag + more

* fix lint

* fix more lints

* add todo for Synthetic dataset

* Update docs

* Improve the BenchmarkFileLogger performance.

Open and preserve the file handler of the metric log file during
init, which reduce the overhead of open/close the file for each
log_metric call.

* Address review comment.

* add tests for matmul embedding and schedule manager, as well as some minor cleanup

* delint

* address PR comments

This will prevent the unit test to read the local config of the GCP API, which does not necessary to exist for the test environment.

* port changes from previous branch now that transformer util changes are in master

fix incorrect count

correct (hopefully) treatment of batch_size

set eval_metrics to a dummy function for now

add some comments

start bringing metrics to transformer TPU

resolve logits shape

metrics are now working except for tf.py_func metrics

increase batch_size for tpu, and create summary host call

fix host call

reduce tpu default batch size

further tune batch sizes

add minibatch loss to summary

handle case of single_iteration_train_steps > number points in an epoch

begin to incorporate hooks

add sleep workarounds

disable hooks altogether

generalize host call function and move to newly created tpu utils module

remove all traces of params as an object

switch from  to

address some PR comments, and change the number of data points.

minor tweaks

add tpu dry run for testing, and use matmul for TPU embedding

infeed/outfeed queue issue is fixed. Sleeps are no longer necessary

add some documentation.

cleanup and address PR comments

delint

add accelerator __init__

fix embedding

missed PR comment

address PR comments

fix validator bug

rewrite cloud storage validator, and add oauth dependency to requirements.txt

* delint

* Add new test ID and test env info to the benchmark run.

* Fix test.

* Fix lint

* Address review comment.

* Update benchmark logger to update the run status.

This is important for streaming upload to bigquery so that the
dashboard can ignore the 'running' benchmark at the moment since
its not finished yet.

* Move the run status into a separate table.

Also update the run status in the benchmark uploader and
BigqueryBenchmarkLogger.

* Insert instead of update for the benchmark status for file logger.

* Address review comments.

Update the logger to have benchmark context, which will update the
run status accordingly.

* Fix broken tests.

* Move the benchmark logger context to main function.

* Fix tests.

* Update the rest of the models to use the context in main.

* Delint.

* Fix hooks_test

* Add more comments

* Fix lints

* Using BenchmarkLogger

* Using BenchmarkLogger

* Fixing tests

* Linting fixes.

* Adding comments

* Moving mock logger

* Moving mock logger

* Glinting

* Responding to CR

* Reverting assertEmpty