Package: basenet-api
BaseNet: A simpler way to build AI models.Basenet API Package - 1.9.3:  (2.0.0 Is coming!!)This package implements an API over Keras and Tensorflow to build Deep Learning models easily without losing the
framework flexibility. BaseNet API tries to implement almost everything from a few lines of code.Disclaimer: This API is under development. This means that there isn't a stable release yet.
However, some features are stable and can be used. Check the tutorials for more info.
Please, check the roadmap below to see the future updates.Any feature request is wellcome and appreciated; and has a high probability to be implemented.Any issue is wellcome and appreciated; and it will often be inspected.AboutAuthor: A.Palomo-Alonso (a.palomo@uah.es)
Universidad de Alcalá.
Escuela Politécnica Superior.
Departamento de Teoría De la Señal y Comunicaciones (TDSC).
ISDEFE Chair of Research.FeaturesMACHINE LEARNINGFeature 01:Database train, validation and test automatic and random segmentation for DeepLearning.Feature 02:Solving optimization problems for MetaHeuristic models.EFFICIENCYFeature 03:Real multiprocessing training process (CPU usage optimization).Feature 04:Automatic and custom GPU usage and assignment.SIMPLICITYFeature 05:Easy-to-use API.Feature 06:API documentation.Feature 07:JuPyter Notebooks tutorials included.Feature 08:Python Packaging and PyPi indexing.MONITORIZATIONFeature 09:Real-time logging.Feature 10:Dashboards included.Feature 11:Model printing and easy debugging.CONNECTIVITYFeature 12:Model merging and multiple model inputs.Feature 13:Computational cluster linking.Feature 14:The different parts of the API are designed to interact.Feature 15:It allows to create dynamic databases for data ingestion and math problems that require synthetic data.RELIABILITYFeature 16:Depends on huge and reliable frameworks: KERAS, TENSORFLOW, RAY.Feature 17:Code updating and active support.Cons:FLEXIBILITYAn API must look for a balance between simplicity and flexibility. In this case, we bet on simplicity; but the API
is still highly flexible.The API is designed for high level research and design. But it is not optimal for low-level research.DEPENDENCEThis API is built over highly reliable frameworks, however, the API depends on those frameworks to run the models.DEPLOYMENTThe API can not deploy models by itself yet in this current version. But I am planning!Roadmap:Discipline coverage:Database building.Supervised learning.MetaHeuristic optimization.Unsupervised learning. (Planning in 3.0 release)Reinforcement learning. (Planning in 4.0 release)Computer Vision. (Planning in 5.0 release)Natural Language Processing. (Will be in development from 3.0 to 6.0)Feature roadmap:Feeder databases.Computational clustering.Monitoring.BaseNetDeployment:Deploy your model in your infrastructure with high connectivity and scalability.With preprocess, model, postprocess and front-end sector.Automatic workload balance (probably withRayorSparkclusters).Dynamic training after deployment. The model will still be learning from input data if desired.Reinforcement learning:This will be hard to add in deployment.Custom environments.Connectivity with BaseNetCompiler: It will work with RL policy and DL!State-of-the-art RL.Computer Vision:It is already possible to be implemented in DeepLearning API; but can be improved.Database visualization.Database size optimization.Computer vision utils.Natural Language Processing:Hard to tell here, this discipline is very ad-hoc, and it is hard to pack it in an API.Transformers, LSTM and Word Vectorization will be included.Preprocessing will be a must.Probably will useHuggingFacepackage.Will be in constant development.What's new?< 0.1.0BaseNetModel included.BaseNetDatabase included.BaseNetCompiler included.Inheritance from CorNetAPI project.Multi-processing fitting.Tensorboard launching.0.2.0BaseNetResults included (working).Now the model is callable.Switched print to logging.Project documentation.1.0.0 - 1.0.3Python packaging1.0.x: Upload bug solving.1.1.0Functional package.PyPi indexing.1.2.0:Loss results included in the BaseNetResults while multiprocessing.GPU auto set up to avoid TensorFlow memory errors.MethodBaseNetCompiler.set_up_devices()configures the GPUs according to the free RAM to be used in the API.1.3.0Included WindowDiff to the project scope.1.4.0Solved python packaging problems.Included force stop callback in theBaseNetModel.fit_stop()method.1.5.0BaseNetDatabase now has the attributesBaseNetDatabase.sizeandBaseNetDatabase.distribution.Solved forced stopping bugs with multiprocessing in the methodBaseNetDatabase.fit_stop().BaseNetModel._threshold()private method now takes a set of outputs instead only one.
This was only for optimization.Solved wrongBaseNetModel.recover().Auto recover implemented, nowBaseNetModel.recover()is a private method:BaseNetModel._recover().
Now the used does not need to recover it.The model recovers by itself. -- Hans Niemann 2022.NOTE: RECOVER IS NECESARY WHEN THE MODEL IS EARLY STOPPED; CONSIDER RECOVERING ALWAYS THE MODEL.1.5.1 - 1.5.3Solved a bug whereBaseNetDatabasemodified the incoming list of instances in the database; avoiding checkpoints
for large database generators.Exception handler fornvmllibrary if NVIDIA Drivers are not installed`in the machine.1.5.4Added someBaseNetDatabaseutils: merge and split databases.AddedBaseNetDatabaseequality check.Added aBaseNetDatabase._reversion(),BaseNetCompiler._reversion()andBaseNetModel.__version__. Which
rebuilds the Classes to the current version of the API.1.6.0Start to develop the second branch of the API:BaseNetHeuristicStart to create JuPyter Notebook tutorials of the API.IncludedBaseNetDatabasebinarization and normalization of databases.Included inBaseNetDatabaseto readTensorFlowandPandasdatabases.Some rework was done for bug-fixing and providing more logging information.1.7.0 - 1.9.1ReworkedBaseNetDatabasefor minor bug fixing. (1.7.0)Added theBaseNetFeederClass to generate dynamicBaseNetDatabases. (1.7.0)Jupyter tutorials. (1.8.0)Added theBaseNetDeploymentClass to deploy scalable Machine Learning models (in alpha, do not use until 3.0).
(1.9.0)AddedBaseNetHeuristicalgorithms that implements MetaHeuristic methods such as PSO. (1.8.0)1.9.2AddedBaseNetLMSE, solving linear problems with matrix multiplication.1.9.3AddedCassandraDatabase.Basic and fast usageThere are Jupyter Notebooks with usage tutorials! Refer to themHERE. You should run your notebook creating a
virtual environment in this README path.Cite asPlease, cite this library as:@misc{basenetapi,
  title={BaseNet: A simpler way to build AI models.},
  author={A. Palomo-Alonso},
  booktitle={PhD in TIC: Machine Learning and NLP.},
  year={2022}
}