# baidu/paddle

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23,959 stars · 6,000 forks · C++ · Apache-2.0

## Links

- GitHub: https://github.com/baidu/paddle
- Homepage: http://www.paddlepaddle.org/
- awesome-repositories: https://awesome-repositories.com/repository/baidu-paddle.md

## Description

Paddle is a deep learning framework designed for building, training, and deploying large-scale machine learning models. It incorporates a distributed training engine for optimizing performance across multiple chips and a model inference engine for transforming trained models into production-ready formats for cross-platform execution.

The platform features a heterogeneous hardware abstraction and a standardized software stack that allows models to run across diverse hardware architectures through a common interface. It also includes a scientific computing library capable of solving complex differential equations using high-order automatic differentiation and complex number operations.

The framework covers automated distributed training and model execution optimization, utilizing tensor partitioning and ahead-of-time compilation. It further provides tools for cross-platform model export and production deployment to manage industrial machine learning workflows.

## Tags

### Artificial Intelligence & ML

- [Distributed Deep Learning](https://awesome-repositories.com/f/artificial-intelligence-ml/distributed-deep-learning.md) — Scales the training of large-scale machine learning models across multiple GPUs and compute nodes.
- [Machine Learning Training](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/machine-learning-training.md) — Provides a high-performance framework for building and training large-scale deep learning models on single machines or distributed clusters. ([source](https://github.com/baidu/paddle#readme))
- [Distributed Deep Learning Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/distributed-deep-learning-frameworks.md) — Serves as a unified platform for the distributed training and deployment of large-scale deep learning models.
- [Distributed Training](https://awesome-repositories.com/f/artificial-intelligence-ml/distributed-training-frameworks/distributed-training.md) — Optimizes model performance across multiple GPUs using automated data and model parallelization strategies.
- [Dynamic Graph Builders](https://awesome-repositories.com/f/artificial-intelligence-ml/gradient-computation/dynamic-graph-builders.md) — Implements dynamic computational graphs to support flexible model structures and automatic differentiation.
- [Hardware Acceleration Stacks](https://awesome-repositories.com/f/artificial-intelligence-ml/hardware-acceleration-stacks.md) — Includes a standardized software stack to support diverse hardware accelerators through a common API layer.
- [Model Performance Optimizations](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/frameworks/training-systems/model-performance-optimizations.md) — Utilizes ahead-of-time compilation to optimize computational graphs for high execution speed in generative and scientific models. ([source](https://github.com/baidu/paddle#readme))
- [Model Training and Inference Engines](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-management/model-training-and-inference-engines.md) — Provides a unified interface for both training machine learning models and executing them for prediction tasks. ([source](https://github.com/baidu/paddle#readme))
- [Hardware Abstraction Layers](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-optimization-and-inference/hardware-and-acceleration/hardware-abstraction-layers.md) — Provides a hardware abstraction layer to normalize heterogeneous hardware backends through a common interface.
- [Model Inference Runtimes](https://awesome-repositories.com/f/artificial-intelligence-ml/model-inference-runtimes.md) — Transforms trained deep learning models into production-ready formats for efficient cross-platform execution.
- [Cross-Platform ML Deployment](https://awesome-repositories.com/f/artificial-intelligence-ml/cross-platform-ml-deployment.md) — Ensures deep learning models run consistently and efficiently across diverse operating systems and hardware.
- [Machine Learning Workflow Libraries](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning-workflow-libraries.md) — Manages the end-to-end pipeline from training to inference using a single framework to streamline development.
- [Model Exporters](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-deployment-and-serving/serialization-and-export-formats/model-exporters.md) — Converts trained models into standardized formats for execution across diverse hardware and software environments. ([source](https://github.com/baidu/paddle#readme))
- [Model Compilation](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-optimization-and-inference/serving-and-runtime/inference-optimization-utilities/model-compilation.md) — Transforms trained models into optimized binaries to increase inference speed and reduce runtime overhead.
- [Model Deployment Toolkits](https://awesome-repositories.com/f/artificial-intelligence-ml/model-optimization/inference-deployment/model-deployment-toolkits.md) — Provides toolkits for transforming trained models into production-ready formats for industrial environments. ([source](https://github.com/baidu/paddle#readme))
- [Tensor Parallelism](https://awesome-repositories.com/f/artificial-intelligence-ml/tensor-parallelism.md) — Automates data and model distribution by partitioning large tensors across multiple processing devices.
- [Unified Execution Primitives](https://awesome-repositories.com/f/artificial-intelligence-ml/training-pipelines/unified-execution-primitives.md) — Shares operational primitives and memory management between training and deployment to eliminate code duplication.

### Part of an Awesome List

- [Deep Learning Frameworks](https://awesome-repositories.com/f/awesome-lists/ai/deep-learning-frameworks.md) — Parallel distributed deep learning platform.