This project is a deep learning framework designed for constructing, training, and deploying neural networks across diverse hardware environments. It functions as a high-performance tensor computation library that provides both imperative and symbolic programming interfaces, allowing developers to balance flexible, step-by-step model building with the efficiency of compiled computation graphs.
The framework distinguishes itself through a hybrid execution engine that integrates declarative graph compilation with imperative runtime logic. It supports scalable, distributed training across multiple compute nodes and devices, utilizing a shared key-value store and sophisticated synchronization strategies to manage parameters and gradient updates. The system is built on a language-agnostic native core, ensuring consistent performance and behavior when accessed through its various language bindings.
Beyond core training and inference, the project includes comprehensive tools for managing data pipelines, including utilities for streaming, resizing, and prefetching datasets from local or cloud storage. It also provides extensive monitoring, profiling, and visualization capabilities to track performance metrics, inspect intermediate outputs, and identify bottlenecks during the development process.
The software is designed for production-grade deployment, offering support for model serialization, mobile optimization, and secure execution environments. It includes specialized memory planning and hardware-specific tuning to maximize throughput and minimize resource usage across CPUs and graphics cards.
