Tiledb-ml

This release includes quite a few updates.

Major Changes

* [API change] Consolidate the dense and sparse TileDB Dataset classes for Tensorflow and PyTorch
* Tensorflow: Merge `TensorflowTileDBDenseDataset` and `TensorflowTileDBSparseDataset` into `TensorflowTileDBDataset`
* PyTorch: Merge `PyTorchTileDBDenseDataset` and `PyTorchTileDBSparseDataset` into `PyTorchTileDBDataset`
* [API change] Remove `__len__` method from `*TileDBDataset`instances
* [Enhancement] Support data loading from (dense x, sparse y) arrays
* [Enhancement] Read only the necessary dimensions and attributes
* [Enhancement] Extensive `tiledb.ml.readers` refactoring
* [Enhancement] Test and CI tooling revamp
* [Enhancement] Fix deprecation warning for attrs with var=True and fixed len dtype
* [Bug fix] Take into account all requested attributes for sparse datasets

What's Changed

* Add dense and sparse checks in Tensoflow and PyTorch data APIs by georgeSkoumas in https://github.com/TileDB-Inc/TileDB-ML/pull/91
* Fix broken links on README by georgeSkoumas in https://github.com/TileDB-Inc/TileDB-ML/pull/92
* Readme update by georgeSkoumas in https://github.com/TileDB-Inc/TileDB-ML/pull/93
* Test tweaks by gsakkis in https://github.com/TileDB-Inc/TileDB-ML/pull/94
* Tooling & test improvements by gsakkis in https://github.com/TileDB-Inc/TileDB-ML/pull/95
* Fix deprecation warning for attrs with var=True and fixed len dtype by gsakkis in https://github.com/TileDB-Inc/TileDB-ML/pull/96
* Move internal utils modules by gsakkis in https://github.com/TileDB-Inc/TileDB-ML/pull/97
* Fix CI Test Coverage Badge action and run only on master by gsakkis in https://github.com/TileDB-Inc/TileDB-ML/pull/98
* Refactor tensorflow datasets by gsakkis in https://github.com/TileDB-Inc/TileDB-ML/pull/99
* Fix max workers to 2 by ktsitsi in https://github.com/TileDB-Inc/TileDB-ML/pull/100
* Refactor tensorflow generators by gsakkis in https://github.com/TileDB-Inc/TileDB-ML/pull/101
* Refactor Pytorch datasets by gsakkis in https://github.com/TileDB-Inc/TileDB-ML/pull/102
* Fix: leave the cardinality of TensorflowTileDBDataset unknown by gsakkis in https://github.com/TileDB-Inc/TileDB-ML/pull/103
* Refactor common logic of Tensorflow and Pytorch data loaders by gsakkis in https://github.com/TileDB-Inc/TileDB-ML/pull/104
* Update reader notebook examples by gsakkis in https://github.com/TileDB-Inc/TileDB-ML/pull/105
* Drop `PyTorchTileDBDataset.__len__` by gsakkis in https://github.com/TileDB-Inc/TileDB-ML/pull/106
* Re-run all model examples by georgeSkoumas in https://github.com/TileDB-Inc/TileDB-ML/pull/107
* Data loading for (dense, x sparse y) by gsakkis in https://github.com/TileDB-Inc/TileDB-ML/pull/109
* Fix BaseSparseBatch.set_buffer_offset by gsakkis in https://github.com/TileDB-Inc/TileDB-ML/pull/111
* Refactor data loader tests v2 by gsakkis in https://github.com/TileDB-Inc/TileDB-ML/pull/110
* Refactor data loader tests by gsakkis in https://github.com/TileDB-Inc/TileDB-ML/pull/108
* BaseSparseBatch fix: take into account all requested attributes by gsakkis in https://github.com/TileDB-Inc/TileDB-ML/pull/112
* Read only the necessary dimensions and attributes by gsakkis in https://github.com/TileDB-Inc/TileDB-ML/pull/113


**Full Changelog**: https://github.com/TileDB-Inc/TileDB-ML/compare/v0.2.6...v0.3.0

This release contains the following changes.

1. Always add timestamp when saving a model as a TileDB array for all supported frameworks, i.e., Tensorflow-Keras, PyTorch and Scikit-Learn.
2. Tensorflow Keras sparse reader slicing in CSR format.
3. PyTorch sparse reader slicing in CSR format.
4. Add buffer for large batch reads to Tensorflow Keras Readers.
5. Add buffer for large batch reads to PyTorch Readers.
6. Batch shuffling and within batch shuffling for Tensorflow Keras readers.
7. Batch shuffling and within batch shuffling for PyTorch readers.
8. Parallel reads for X and Y for Tensorflow Keras readers.
9. Parallel reads for X and Y for PyTorch readers.
10. Example directory restructure.
11. All example notebooks were accordingly updated.
12. Docs were updated.

What's Changed
* [Enhancement] Multiple Attribute Readers for PyTorch by ktsitsi in https://github.com/TileDB-Inc/TileDB-ML/pull/77
* [Enhancement] Tighten type annotations & type check with mypy git hook & GH action by gsakkis in https://github.com/TileDB-Inc/TileDB-ML/pull/78
* [Enhancement] Parallel batch reads for Pytorch by ktsitsi in https://github.com/TileDB-Inc/TileDB-ML/pull/79
* [Examples] Serverless End-To-End example PyTorch by georgeSkoumas in https://github.com/TileDB-Inc/TileDB-ML/pull/80
* [Enhancement] Parallel batch reads for TF by ktsitsi in https://github.com/TileDB-Inc/TileDB-ML/pull/81
* [Enhancement] PyTorch Batch and Within Batch Shuffling by georgeSkoumas in https://github.com/TileDB-Inc/TileDB-ML/pull/82
* [Enhancement] Tensorflow Batch and Within Batch Shuffling by georgeSkoumas in https://github.com/TileDB-Inc/TileDB-ML/pull/83
* [Enhancement] Pytorch Buffer Generator by ktsitsi in https://github.com/TileDB-Inc/TileDB-ML/pull/84
* [Enhancement] Tensorflow Buffer Generator by ktsitsi in https://github.com/TileDB-Inc/TileDB-ML/pull/85
* [Examples] Revisit All Reader Example Notebooks by georgeSkoumas in https://github.com/TileDB-Inc/TileDB-ML/pull/86
* [Bug] Fixing batching error using CSR format for Pytorch by ktsitsi in https://github.com/TileDB-Inc/TileDB-ML/pull/87
* [Bug] Fixing batching error using CSR format for TF by ktsitsi in https://github.com/TileDB-Inc/TileDB-ML/pull/88
* [Fix] Update broken docs.tiledb.com links by gsakkis in https://github.com/TileDB-Inc/TileDB-ML/pull/90
* [Fix/Enhancement] Adding default argument current time timestamp in open 'w' mode by ktsitsi in https://github.com/TileDB-Inc/TileDB-ML/pull/89

This release concerns a bug fix in TileDB version dependency.

This release concerns the following:

1. Mutliple attribute readers for Dense and Sparse TileDB Arrays for the Tensorflow Data API.
2. Tensorflow-Keras Subclassed models can now be saved as TileDB Arrays.
3. Minor updates and bug fixes.

This release is a bug fix in the model metadata part. We used to JSON serialise model metadata before store them in a Model TileDB array. This has been removed and metadata are stored in the form the users pass them.

This is a bug fix. We should remove machine learning framework imports from model base class.