Changelog¶
Current release candidate¶
0.6.2 (2020-06-18)¶
- Minor bugfixes
0.6.1 (2020-06-15)¶
- Deleted accidental debug ‘print’ call :/
0.6.0 (2020-06-12)¶
- Prebuilt flask server images for faster image build
- More and better methods in Ebonite client
- Pipelines - chain Models methods into one Model-like objects
- Refactioring of image and instance API
- Rework of pandas DatasetType: now with column types, even non-primitive (e.g. datetimes)
- Helper functions for stanalone docker build/run
- Minor bugfixes and features
0.5.2 (2020-05-16)¶
- Fixed dependency inspection to include wrapper dependencies
- Fixed s3 repo to fail with subdirectories
- More flexible way to add parameters for instance running (e.g. docker run arguments)
- Added new type of Requirement to represent unix packages - for example, libgomp for xgboost
- Minor tweaks
0.5.1 (2020-04-16)¶
- Minor fixes and examples update
0.5.0 (2020-04-10)¶
- Built Docker images and running Docker containers along with their metadata are now persisted in metadata repository
- Added possibility to track running status of Docker container via Ebonite client
- Implemented support for pushing built images to remote Docker registry
- Improved testing of metadata repositories and Ebonite client and fixed discovered bugs in them
- Fixed bug with failed transactions not being rolled back
- Fixed bug with serialization of complex models some component of which could not be pickled
- Decomposed model IO from model wrappers
- bytes are now used for binary datasets instead of file-like objects
- Eliminated build_model_flask_docker in favor of Server-driven abstraction
- Sped up PickleModelIO by avoiding ModelAnalyzer calls for non-model objects
- Sped up Model.create by calling model methods with given input data just once
- Dataset types and model wrappers expose their runtime requirements
0.4.0 (2020-02-17)¶
- Implemented asyncio-based server via aiohttp library
- Implemented support for Tensorflow 2.x models
- Changed default type of base python docker image to “slim”
- Added ‘description’ and ‘params’ fields to Model. ‘description’ is a text field and ‘params’ is a dict with arbitrary keys
- Fixed bug with building docker image with different python version that the Model was created with
0.3.5 (2020-01-31)¶
- Fixed critical bug with wrapper_meta
0.3.4 (2020-01-31)¶
- Fixed bug with deleting models from tasks
- Support working with model meta without requiring installation of all model dependencies
- Added region argument for s3 repository
- Support for delete_model in Ebonite client
- Support for force flag in delete_model which deletes model even if artifacts could not be deleted
0.3.3 (2020-01-10)¶
- Eliminated tensorflow warnings. Added more tests for providers/loaders. Fixed bugs in multi-model provider/builder.
- Improved documentation
- Eliminate useless “which docker” check which fails on Windows hosts
- Perform redirect from / to Swagger API docs in Flask server
- Support for predict_proba method in ML model
- Do not fix first dimension size for numpy arrays and torch tensors
- Support for Pytorch JIT (TorchScript) models
- Bump tensorflow from 1.14.0 to 1.15.0
- Added more tests
0.3.2 (2019-12-04)¶
- Multi-model interface bug fixes
0.3.1 (2019-12-04)¶
- Minor bug fixes
0.3.0 (2019-11-27)¶
- Added support for LightGBM models
- Added support for XGBoost models
- Added support for PyTorch models
- Added support for CatBoost models
- Added uwsgi server for flask containers
0.2.1 (2019-11-19)¶
- Minor bug fixes
0.2.0 (2019-11-14)¶
- First release on PyPI.