Microsoft's Neural Network Intelligence (NNI): An Extensible Toolkit for Automated Machine Learning (AutoML)
Microsoft's Neural Network Intelligence (NNI) is an all-inclusive open-source toolkit designed to enhance and automate the process of Machine Learning (ML) and Deep Learning (DL). This toolkit aims to assist both researchers and developers in their journey to develop, fine-tune, and test their neural networks.
Project Overview:
The goal of the Neural Network Intelligence (NNI) project is to provide a versatile, easy-to-use platform to automate and manage the entire development process involved in Machine Learning (ML) and Deep Learning (DL) projects. It addresses the needs of the growing community of ML and DL developers, by providing a curated toolkit for model training, hyperparameter tuning, neural architecture search, and model compression. The target audience for this project spans from ML and DL researchers to AI developers and enthusiasts.
Project Features:
One of the key features of NNI is an array of efficient tuning algorithms, supporting hyper-parameter optimization and neural architecture search. It also provides an easy-to-use interface for managing training services and allows users to define and modify search space for hyperparameters. Moreover, NNI's model compression provides a collection of functions to facilitate model pruning and quantization. Some compelling use cases include automating the design of learning models, tuning hyperparameters, or training models across multiple servers.
Technology Stack:
The project primarily employs Python and TypeScript as its primary programming languages. Python is a popular choice because of its readability, flexibility, and comprehensive support for scientific computing. TypeScript, a statically typed extension of JavaScript, offers enhanced development efficiency and maintainability for server-side programming. Notable libraries used in this project include TensorFlow, PyTorch, MXNet, Keras, and Caffe2, which are renowned for their efficiency in handling machine learning tasks.
Project Structure and Architecture:
NNI is a well-structured project with dedicated modules for multiple tasks. It includes modules like Experiment, Trial, Tuner, Assessor and Advisor, each responsible for a specific aspect of an AutoML task. For example, the Tuner module is responsible for generating a set of optimal hyperparameters for the model.
