Paddle is a deep learning framework designed for building, training, and deploying neural networks. It provides a platform for constructing models using tensor-based computations and supports both dynamic and static execution graphs to facilitate research and production workflows.
The platform functions as a distributed machine learning system, enabling the scaling of training workloads across multiple nodes and hardware clusters. It includes a comprehensive toolkit for model deployment and optimization, allowing users to convert external model formats, compress trained models for resource-constrained hardware, and perform cross-framework migrations to maintain compatibility with current architecture standards.
Beyond core training and deployment, the framework offers tools for neural network architecture design, including the ability to define custom layers and visualize complex model structures. It incorporates performance-oriented features such as mixed precision arithmetic, automated parameter tuning, and graph-level optimizations to maximize computational throughput and ensure stable convergence during large-scale training.
