Frameworks

Building AI applications may involve machine learning libraries, training frameworks or tools connecting models and data. This category brings together development resources at different layers of a project.

All AI tools

31 listings

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飞桨PaddlePaddleBaidu's open-source deep learning framework TensorFlowGoogle's machine learning framework PyTorchThe research-first deep learning framework KerasFriendly high-level neural network API Caffecaffe.berkeleyvision.org Apache MXNetScalable deep learning framework MindSporeHuawei's AI framework for Ascend Scikit-learnClassic machine learning library NumPyFoundation of numerical Python LangChainFramework for LLM applications LlamaIndexConnect private data to LLMs HaystackProduction RAG and search framework Lightning AlPyTorch scaffold and cloud platform NLTKClassic NLP toolkit for Python JAXjax.readthedocs.io DL4Jdeeplearning4j.konduit.ai Vercel AI SDKsdk.vercel.ai 文心智能体平台agents.baidu.com 昇思Mindsporemindspore.cn MLXml-explore.github.io Label Studiolabelstud.io Leaptryleap.ai 芝士饼knowledgepie.cloud.alipay.com Wordwarewordware.ai Cozecoze.com 言犀智能体平台yanxi.jd.com 无问芯穹cloud.infini-ai.com 魔乐社区modelers.c 百宝箱Tboxtbox.alipay.com NoCodenocode.cn Skyworkskywork.ai

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How to choose a tool

Decide whether you are training a model or building an application, then check language, hardware, dependencies and deployment needs. Review maintenance, documentation and licenses, and use a small prototype to assess integration effort.