Integration with TensorFlow and PyTorch

[gabrielspadon]

We should integrate AISdb with TensorFlow and PyTorch by creating a custom, high-speed data loader. The goal is to simplify the process of setting up a database, importing the data loader, and training sequence-to-sequence models with the provided data. By integrating AISdb with TensorFlow and PyTorch, users will be able to directly utilize the machine-learning libraries within the AISdb environment. This will greatly facilitate the development and training of models, particularly those that require sequence-to-sequence learning. To develop the custom data loader, I suggest exploring the data-loader functionalities already provided by TensorFlow (https://www.tensorflow.org/api_docs/python/tf/data/Dataset) and PyTorch (https://pytorch.org/docs/stable/data.html). These libraries already offer robust and efficient data loading and preprocessing capabilities that can be easily tailored to general needs and specifications. This can be solved either by embedding these data-loading functions into AISdb using wrappers, or it can be done through tutorials on GitBook that will show the users how to join both libraries in an efficient and correct way.

[JayKumarr]

@gabrielspadon can we create a new project Aisdb-ML which uses AISdb-client?

[gabrielspadon]

No, we can do a tutorial on GitBook that shows how to do the integration instead.