Open Source Linux Deep Learning Frameworks

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Deep Learning Frameworks for Linux

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Deep Learning Frameworks Linux 4 - Beta Clear Filters

Browse free open source Deep Learning Frameworks and projects for Linux below. Use the toggles on the left to filter open source Deep Learning Frameworks by OS, license, language, programming language, and project status.

  • Auth0 for AI Agents now in GA Icon
    Auth0 for AI Agents now in GA

    Ready to implement AI with confidence (without sacrificing security)?

    Connect your AI agents to apps and data more securely, give users control over the actions AI agents can perform and the data they can access, and enable human confirmation for critical agent actions.
    Start building today
  • Run applications fast and securely in a fully managed environment Icon
    Run applications fast and securely in a fully managed environment

    Cloud Run is a fully-managed compute platform that lets you run your code in a container directly on top of scalable infrastructure.

    Run frontend and backend services, batch jobs, deploy websites and applications, and queue processing workloads without the need to manage infrastructure.
    Try for free
  • 1
    AI-Agent-Host

    AI-Agent-Host

    The AI Agent Host is a module-based development environment.

    The AI Agent Host integrates several advanced technologies and offers a unique combination of features for the development of language model-driven applications. The AI Agent Host is a module-based environment designed to facilitate rapid experimentation and testing. It includes a docker-compose configuration with QuestDB, Grafana, Code-Server and Nginx. The AI Agent Host provides a seamless interface for managing and querying data, visualizing results, and coding in real-time. The AI Agent Host is built specifically for LangChain, a framework dedicated to developing applications powered by language models. LangChain recognizes that the most powerful and distinctive applications go beyond simply utilizing a language model and strive to be data-aware and agentic. Being data-aware involves connecting a language model to other sources of data, enabling a comprehensive understanding and analysis of information.
    Downloads: 0 This Week
    Last Update:
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