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PiperOrigin-RevId: 270122397

PiperOrigin-RevId: 270079001

PiperOrigin-RevId: 269835917

PiperOrigin-RevId: 268836696

PiperOrigin-RevId: 268703439

The old infer_float32_policies policy will be removed from TensorFlow soon.

To test convergence, I ran the Resnet50KerasAccuracy.benchmark_8_gpu_fp16 benchmark. I got an accuracy of 0.76037 and an exp_per_second of 6908.

PiperOrigin-RevId: 266191126

PiperOrigin-RevId: 265940771

PiperOrigin-RevId: 264895439

PiperOrigin-RevId: 264511778

using existing flag --fp16_implementation in official/utils/flags/_performance.py to enable automatic mixed precision

Moving Keras ResNet models to `official/vision/image_classification` and benchmarks to `official/benchmark`.

PiperOrigin-RevId: 264268533

* 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

* 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.

* Move the benchmark_uploader to new location.

* Update benchmark logger to streaming upload.

* Fix lint and unit test error.

* delint.

* Update the benchmark uploader test.

Skip the import of benchmark_uploader when bigquery is not installed.

* Merge the 2 classes of benchmark uploader into 1.

* Address review comments.

* delint.

* Execute bigquery upload in a separate thread.

* Change to use python six.moves for importing.

* Address review comments and delint.

* Address review comment.

Adding comment for potential performance impact for model on CPU.

* Fix random failure on py3.

* Fix the order of flag saver to avoid the randomness.

The test is broken when the benchmark_logger_type is set first, and
validated when the benchmark_log_dir is not set yet.

* Add dataset info and hyper parameter logging for benchmark.

* Address review comments.

* Address the view comment for data schema name.

* Fix test cases.

* Lint fix.

* Add benchmark upload util to bigquery.

Also update the benchmark logger and bigquery schema for the
errors found during the integration test.

* Fix lint error.

* Update test to clear all the env vars during test.

This was causing error since the Kokoro test has TF_PKG=tf-nightly
injected during test.

* Update lintrc to ignore google related package.

* Another attempt to fix lint import error.

* Address the review comment.

* Fix lint error.

* Another fix for lint.

* Update test comment for env var clean up.

1. Update logger to convert the value type to float to please JSON.
2. Update logger test to call parent test tearDown.

1. Added Tensorflow version information.
2. Added environment variables.
3. Fix typo for hyperparameters.
4. Added cloud related information.

The current schema contains the entity information about model
and train data metadata, as well as machine config. Future change
will contain benchmark metric.

The json schema can be used to create bigquery table. A sample
table can be found in
https://bigquery.cloud.google.com/table/tf-benchmark-dashboard:test_benchmark.benchmark_run.