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Because we run warmup tests in all real data benchmarks, XLA bugs will cause non-XLA tests to fail as well.

After the change (https://github.com/tensorflow/models/pull/6846/files#diff-965780bf33f2aeca41a33f8eba197c79) I receive the following error:

File "./models/official/mnist/mnist_tpu.py", line 202, in <module>
    absl_app.run()
TypeError: run() missing 1 required positional argument: 'main'

I added main as an argument and it seems to be working fine now.

* Add mlperf like test.

* Final comments.

* docstring wording tweak.

* non-tweaked version

* Add step 1 instructions for MaskGAN.

* Fix various lint errors

* Fix logging format

250447559  by Zhichao Lu:

    Update expected files format for Instance Segmentation challenge:
    - add fields ImageWidth, ImageHeight and store the values per prediction
    - as mask, store only encoded image and assume its size is ImageWidth x ImageHeight

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250402780  by rathodv:

    Fix failing Mask R-CNN TPU convergence test.

    Cast second stage prediction tensors from bfloat16 to float32 to prevent errors in third target assignment (Mask Prediction) - Concat with different types bfloat16 and bfloat32 isn't allowed.

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250300240  by Zhichao Lu:

    Addion Open Images Challenge 2019 object detection and instance segmentation
    support into Estimator framework.

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249944839  by rathodv:

    Modify exporter.py to add multiclass score nodes in exported inference graphs.

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249935201  by rathodv:

    Modify postprocess methods to preserve multiclass scores after non max suppression.

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249878079  by Zhich...

250779087  by A. Unique TensorFlower<gardener@tensorflow.org>:

    Reduce BERT Perfzero benchmark test training steps.

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PiperOrigin-RevId: 250779087

* Support pure eager execution in ResNet50

* Use smaller batch size

250713045  by hongkuny<hongkuny@google.com>:

    TPU util

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PiperOrigin-RevId: 250713045

250606180  by A. Unique TensorFlower<gardener@tensorflow.org>:

    Fix BERT benchamrk test errors.

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250589623  by A. Unique TensorFlower<gardener@tensorflow.org>:

    Change BERT benchmark test pretrained checkpoint url.

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250587892  by A. Unique TensorFlower<gardener@tensorflow.org>:

    Fix error in BERT custom training loop checkpoint restoration.

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250577163  by A. Unique TensorFlower<gardener@tensorflow.org>:

    Add logic to inject callback that measures performance in BERT custom training
    loop.

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250529526  by hongkuny<hongkuny@google.com>:

    Internal clean up

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250428976  by hongkuny<hongkuny@google.com>:

    Internal change

250415383  by A. Unique TensorFlower<gardener@tensorflow.org>:

    Add min/max value to BERT classifier benchmark test.

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250376246  by A. Unique TensorFlower<gardener@tensorflow.org>:

    Add benchmark performance test to run BERT on multiple numbers of GPUs.

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PiperOrigin-RevId: 250606180

* Put all python dependencies into one line.

This makes it easier to copy, paste & install all dependencies at once. In addition many users have custom setups (virtualenv, conda, .etc). Having it in one line easily allows to grap the dependencies.

* Remove 'sudo' from all pip install commands and adjust troubleshooting section.

* Make max_length and static_batch configurable.

* Fix line length.

* Fix incorrect parameters in building eval input.

* Improve comments for readability.

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

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

* Add static batch benchmarks to estimator 

So we can distinguish how much static vs dynamic batch matter.

* change max_length for static_batch tests

* Add flag for max length

* 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.

this is not going to help with current tf.data semantics. so removing it.

* Fixes that make transformer run.

* Remove debug print statements.

* Changed the permissions to 644.

* Fix the rest of the permissions.

* enable static batch in all benchmarks

* Restrict dist strat hack to training mode

For now we will do predict/eval without dist strat, so remove that hack in non training cases.

* Use `inputs` instead of `x` as arg name for call

Keras has different behavior based on whether the inputs are called `inputs` or not. Using `inputs` gives expected behaviors.

* Avoid extra map fn on input in dist strat case

* Update how we handle custom metrics

This new approach works with and without dist strat. The previous one didn't work with dist strat. We need to fix that but this is reasonable in meantime (b/133724664).

* Update benchmarks

* typo in metrics code

* Revert metrics change

Didn't actually work in distributed case..

250347237  by A. Unique TensorFlower<gardener@tensorflow.org>:

    Fix linting errors in BERT benchmark test.

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250326131  by A. Unique TensorFlower<gardener@tensorflow.org>:

    Internal change

250315593  by A. Unique TensorFlower<gardener@tensorflow.org>:

    Internal change

250303528  by haoyuzhang<haoyuzhang@google.com>:

    Add method docstring to fix lint error.

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PiperOrigin-RevId: 250347237

* Run different numbers of steps on different platforms

* Add new tests for delayed performance measurement

This shuffling should help in getting shuffling each epoch.

The ".mat" files loaded in the dataset are byte files. Python 3.7 requires them to be loaded using "rb".

250009207  by A. Unique TensorFlower<gardener@tensorflow.org>:

    Add feature in BERT to write training metrics to a summary file.

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PiperOrigin-RevId: 250009207

249896208  by hongkuny<hongkuny@google.com>:

    Adds __init__.py

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PiperOrigin-RevId: 249896208