* Pass keyword arguments onto Keras compile, allowing more flexibility * Add Gullfaks A as new asset * Add "infinity" imputer * Add pushing of "latest" tag for docker images, making it easier to always test latest build of master * Optimize ML server post data processing, speeding it up * Add pytest-benchmark
0.25.0
- Change default keras activation functions to tanh (346) - Server: Log timings and return as header (345) - Added PERA (Peregrino) as new asset - Allow TimeseriesDataset to take and output target tags (327) - Support sklearn.multioutput.MultiOutputRegressor (321) - Add output activation function for feedforward NN as a parameter (352)
0.24.0
* More robust and scalable watchman - using K8S updates * Fix a bug that made the automatic client fail on IROC projects if train_start was not UTC * Add multithreaded download of NCS data from datalake * Support dry-run mode on the ncs_provider load_series
0.23.0
- Support building models without scoring/cross val (326) - Fix issue where the serializer drops parameters to keras (333) - Refactor ML Server into modular model views (288) - Rename auto encoders .transform() -> .predict() (288) - Upgrade scikit-learn ~=0.21
Note: This depends on a compatible version of gordo-infrastructure, the soon-to-be-released 0.24.0
0.22.0
- Replace generate_window with keras functionality, speeding up `predict` and improving `fit` (299) - Ensure model-config in builder is fully expanded (313) - Fix setup.py license and supported python versions (314)
0.21.0
Breaking change: - Add `name` as a mandatory input to model building, and insert the name into metadata (291)
Other: - Upgrade several test-dependencies (309) - Add major and minor versions to model key calculation, so now models are rebuilt with new versions of gordo-components (312)