I bet that you have already seen in movies the IT guy hacking a system by writing commands inside a black window and thought "How cool is that!". Well, in reality, things are not that easy to hack but we do have some basic commands that can help interact with the computer, which is called command-line interface (CLI). The command-line interface is a program on your computer that allows you to create and delete files, run programs, and navigate through folders and files. On a Mac and Linux Systems, it's called Terminal, and on Windows, it's Command Prompt. CLI is not just a fancy method to interact with your computer.

In an effort to increase standardization across the PyTorch ecosystem Facebook AI in a recent blog post told that they would be leveraging Facebook's open-source Hydra framework to handle configs, and also offer an integration with PyTorch Lightning. This post is about Hydra. If you are reading this post then I assume you are familiar with what are config files, why are they useful, and how they increase reproducibility. And you also know what a nightmare is argparse. In general, with config files you can pass all the hyperparameters to your model, you can define all the global constants, define dataset splits, and … without touching the core code of your project.

Check out the related post: Loading a TensorFlow graph with the C API. Even though the full C API for TensorFlow is not yet available, we can still use it load TensorFlow graphs and evaluate them from other languages. This is incredibly useful for embedding pre-trained models in other applications. Embedding is one of the most interesting use cases for TensorFlow as it cannot be accomplished as easily with Theano. Note that while all of the examples here will use Node.js the steps are nearly identical in any language with C FFI support (e.g.