baidu/paddle

Features

• Distributed Deep Learning - Scales the training of large-scale machine learning models across multiple GPUs and compute nodes.
• Machine Learning Training - Provides a high-performance framework for building and training large-scale deep learning models on single machines or distributed clusters.
• Distributed Deep Learning Frameworks - Serves as a unified platform for the distributed training and deployment of large-scale deep learning models.
• Distributed Training - Optimizes model performance across multiple GPUs using automated data and model parallelization strategies.
• Dynamic Graph Builders - Implements dynamic computational graphs to support flexible model structures and automatic differentiation.
• Hardware Acceleration Stacks - Includes a standardized software stack to support diverse hardware accelerators through a common API layer.
• Model Performance Optimizations - Utilizes ahead-of-time compilation to optimize computational graphs for high execution speed in generative and scientific models.
• Model Training and Inference Engines - Provides a unified interface for both training machine learning models and executing them for prediction tasks.
• Hardware Abstraction Layers - Provides a hardware abstraction layer to normalize heterogeneous hardware backends through a common interface.
• Model Inference Runtimes - Transforms trained deep learning models into production-ready formats for efficient cross-platform execution.
• Cross-Platform ML Deployment - Ensures deep learning models run consistently and efficiently across diverse operating systems and hardware.
• Machine Learning Workflow Libraries - Manages the end-to-end pipeline from training to inference using a single framework to streamline development.
• Model Exporters - Converts trained models into standardized formats for execution across diverse hardware and software environments.
• Model Compilation - Transforms trained models into optimized binaries to increase inference speed and reduce runtime overhead.
• Model Deployment Toolkits - Provides toolkits for transforming trained models into production-ready formats for industrial environments.
• Tensor Parallelism - Automates data and model distribution by partitioning large tensors across multiple processing devices.
• Unified Execution Primitives - Shares operational primitives and memory management between training and deployment to eliminate code duplication.
• Deep Learning Frameworks - Parallel distributed deep learning platform.

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