Changelog¶

Current release candidate¶

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

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

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

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

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

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