Chapter 6 - Open Source

This phase of the project involved lesser work and definitely a breather compared to the previous 5 phases. I focused on another aspect of open source development. Open Source is not only meant for sharing code but allowing others to contribute as well. To make my package contributor friendly, I sat to follow the Google C++ style guidlines and the R guidlines by Hadley Wikham. This also provided better readibility for new users, making it easier for them to get started. Infact the package already has another contributor now.

I was also delighted to discover that the HomeBrew PR has been merged. Now a homebrew formula for installing libtensorflow exists, making it easier for macOS users (Earlier the install name needed to be changed using otool). So thats good news for macOS users.

Having implemented a basic version of tf.Variable, I am currently trying out the experimental gradients provided by the Tensorflow C API.

For the ops, I am planning to write a R code generator to parse the protobuf text file residing in the Tensorflow repository. If implemented, all ops ranging from Adam to Add should have a usable and efficient wrapper implementation to use with rtensorflow.

Another major hinderance I will need to solve in the next phase, is accessing the array-like pointer of the input Rcpp vector while creating new tensors. Right now, a native C++ array is initialized with the Rcpp vector contents, but this might possibly slow down computation, especially around placeholders.