{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/3"}],"enrichment":{"capability":"PaddlePaddle is a deep learning framework for building, training, and deploying neural networks across multiple platforms and devices.","skillfed_tags":["distributed-training","model-inference","edge-deployment"],"use_cases":["Train large-scale neural networks on distributed clusters using the framework's distributed training infrastructure.","Deploy trained models to edge devices and mobile platforms using inference acceleration tools.","Prototype and experiment with deep learning models using pre-built mainstream models from the framework's library.","Build computer vision applications leveraging Pillow integration and pre-trained vision models.","Develop natural language processing systems using the framework's NLP model collections and utilities."],"what_it_does":"PaddlePaddle is an open-source deep learning framework designed for both research and industrial deployment. It supports declarative and imperative programming styles, allowing developers to build neural networks flexibly while maintaining high runtime performance. The framework includes pre-built models, training utilities, and inference acceleration tools, with particular focus on distributed training at scale and cross-platform deployment from cloud servers to edge devices.\n\nThe package depends on numpy for tensor operations, protobuf for serialization, Pillow for image handling, httpx, opt_einsum, networkx, typing_extensions, safetensors, and setuptools. It is actively maintained and classified as production-stable. Installation is straightforward via pip, though platform-specific wheels are required and GPU support needs a separate variant. The framework is licensed under Apache-2.0, making it suitable for both open and commercial projects.","worth_installing":"Yes, if you are building or deploying deep learning models and want an actively maintained framework with strong industrial backing and cross-platform support. The permissive Apache-2.0 license, stable production status, and zero known vulnerabilities support adoption. Medium install friction is typical for ML frameworks and not a barrier. Best suited for teams already familiar with deep learning frameworks or those targeting the framework's specific strengths in distributed training and edge deployment."},"id":"paddlepaddle","links":{"html":"https://skillfed.io/packages/paddlepaddle","md":"https://skillfed.io/packages/paddlepaddle.md","pypi":"https://pypi.org/project/paddlepaddle/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-03-24","license_spdx":null,"license_treatment":"permissive","name":"paddlepaddle","python_support":"unspecified","summary":"Parallel Distributed Deep Learning"},"popularity":{"monthly_downloads":2415720,"position":3075,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"3.3.1"}