Initial project research

[andrewdalpino]

Rubix ML brings powerful machine learning algorithms, data transformers, validation, and production tools to the PHP language without the need for additional libraries or extensions. In addition, users can significantly speed up training and inference by up to 12X on some projects by installing the optional Tensor extension. However, for large-scale deep learning and big data projects, we need even more computing power. This research is to prototype a deep learning library built on top of TensorFlow using the C interface and delivered as a PHP extension so that we can train deep learning models on high-performance systems with millions of samples.

Additional motivations ...

1. Rubix ML does not handle non-tabular data such as images or video natively (they need to be flattened/vectorized), Rubix DL will support n-d array operations natively.
2. We've reached an inflection point on the diminishing returns curve of manual differentiation where further development would start to become burdened by the added friction. TensorFlow provides autodifferentiation which removes this burden completely.
3. This opens a path to running models on fast ASICs (GPU support will eventually land in Tensor)

Things to consider for research ...

1. Will the models be interchangeable with other inference engines?
2. What is the performance compared to a similar neural net built in Rubix ML with Tensor?
3. How to store C data alongside PHP objects (see https://www.zend.com/embedding-c-data-into-php-objects)?