The world's cleanest AutoML library ✨ - Do hyperparameter tuning with the right pipeline abstractions to write clean deep learning production pipelines. Let your pipeline steps have hyperparameter spaces. Design steps in your pipeline like components. Compatible with Scikit-Learn, TensorFlow, and most other libraries, frameworks and MLOps environments.
General improvements and bugfixes:
FlattenForEach
that wasn't up to date._ids
, data_inputs
, and expected_outputs
more often in DataContainer
instead of shorthand ids
, di
, and eo
, which were auto-filling values too often when looping over things in flow classes and output handlers.InputAndOutputTransformerMixin
to IdsAndInputAndOutputTransformerMixin
and derived classes to also process IDs more often in triplets rather than duo tuples of di and eo.DataContainer
.DataContainer
(DACT
) is now possible for easier print debugging at a glimpse.__str__
and __repr__
functionalities to context to show its services and parents in detail upon printing._TruncableMixin
that is common to _TruncableSteps
and _TruncableService
._TruncableServiceWithBodyMixin
for .body
and .joiner
that is easier. Also fix FlattenForEach
.AutoML
train/val phase..mutate(...)
function again in the services and steps..will_mutate_to(...)
function again in the services and steps.copy()
to _copy()
in the services and ExecutionContext
to bypass the fact that the copy method was already defined in some python core data structures. This renaming avoids conflicting these functionalities of the core python libs and of Neuraxle when defining services that inherits from core data structures at the same time._repr
to make step strings less bloated when debugging: removed steps names and steps hyperparams when names are redundant with class names and hyperparams empty. Also sometimes the str will be a compact one-liner when the children of a truncable step are of length 1.New major version number since new changes added in 0.7.1 and 0.7.2 and the following changes of 0.8.0 that makes debugging and usage of parallelism much easier:
Minor release to add AutoML Report classes used to generate statistics:
Minor version release that allows for usage of SQLAlchemy ORM for hyperparameter repositories:
Major changes to the AutoML module are done in this version to improve its capabilities considerably:
SequentialQueuedPipeline
and ParallelQueuedFeatureUnion
such that use_threading
changed touse_processes
to be more clear.Update service assertion interface, extended build to python 3.8, a few minor problems fixed.
Update H1, H2, H3 in intro page