This release contains functionality about saving and loading machine learning models as TileDB arrays for machine learning frameworks Tensorflow Keras, PyTorch and Scikit-Learn. Moreover, contains functionality for reading data from TileDB arrays natively to Tensorflow Data API and PyTorch Dataloader API (for dense and sparse arrays) to train machine learning models, using Python Generators.
Specifically, the current release contains the following.
-Save/Load Tensorflow Keras machine learning models as TileDB arrays.
-Save/Load PyTorch machine learning models as TileDB arrays.
-Save/Load Scikit-Learn machine learning models as TileDB arrays.
-Read data from dense TileDB arrays directly into Tensorflow Data API.
-Read data from sparse TileDB arrays directly into Tensorflow Data API.
-Read data from dense TileDB arrays directly into PyTorch Dataloader API.
-Read data from sparse TileDB arrays directly into PyTorch Dataloader API.
-Example notebooks for saving machine learning models as TileDB arrays locally.
-Example notebooks for saving machine learning models as TileDB arrays on TileDB-Cloud.
-Example notebooks for training Tensorflow Keras machine learning models reading data from dense TileDB arrays.
-Example notebooks for training Tensorflow Keras machine learning models reading data from sparse TileDB arrays.
-Example notebooks for training PyTorch machine learning models reading data from dense TileDB arrays.
-Example notebooks for training PyTorch machine learning models reading data from sparse TileDB arrays.