- Add notes for next release here.
- Dump Python stack when Segmentation fault occurs during remote environment.
- Update API documentation.
- Remove upper bound dependency on
tensorflow_datasets
. - Fix dataset auto shard policy.
- Fix machine_config for DistributingCloudTuner.
- Additional updates in samples and documentation.
- Upgrad the default Cloud Build machine to
N1_HIGHCPU_8
- Support for launching concurrent tuning jobs.
- Code samples for running concurrent tuning jobs.
- Project setup instruction notebooks.
- Updated privacy policy notice for telemetry.
- Small bug fixes in
CloudTuner
.
- Telemetry additions
- Address test failures/flakiness
- Py 3.5 support removed.
- Small bug fixes.
- Added Kaggle integration.
cloud_fit
now moved to a sub-module underTuner
- HParams plugin integration with DistributingCloudTuner
- Added integration tests
- Small bug fixes.
cloud_fit
uses pickle instead of cloudpickle.- Better integration tests checking for job status.
- Small bug fixes.
- New module CloudTuner - Implementation of a library for hyperparameter tuning that is built into the KerasTuner and creates a seamless integration with Cloud AI Platform Optimizer as a backend to get suggestions of hyperparameters and run trials.
- New application Monitoring - TensorFlow extension that exports its metrics to Stackdriver backend, allowing users to monitor the training and inference jobs in real time.
- New experimental project cloud_fit - an experimental module that enables training keras models on Cloud AI Platform Training by serializing the model, and datasets for remote execution.
- Small bug fixes.
- Restructuring of source code for new projects
- Multi-file code example
- Integration test example
- Small bug fixes
- New API remote() to detect if currently in a remote cloud env.
- CI using Github Action.
- Updated README.
- Some minor bug fixes.
- Support for single node Keras tuner workflow.
- Support for TPU training.
- Fixed docker build decode errors.
- Default to Py3 for TF docker images.
- New colab notebook example.
- New Auto Keras example.
- Improved ReadMedocs.
- Improved error messages.
- Support for passing colab notebook as entry_point.
- Support for cloud docker build and colab workflow.
- Support for log streaming in colab
- Detailed README with setup instructions and examples.
- Support for running
run
API from within a python script which contains a Keras model.
- Initial release with support for running a python script on GCP.
- Examples for basic workflows in Keras.