2 projects for "network monitoring tool" with 2 filters applied:

  • Ship Agents Faster Icon
    Ship Agents Faster

    Transform your applications and workflows into powerful agentic systems at global scale.

    Gemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
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    MongoDB Atlas runs apps anywhere

    Deploy in 115+ regions with the modern database for every enterprise.

    MongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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  • 1
    SwiftNIO

    SwiftNIO

    Network application framework for protocol servers and clients

    SwiftNIO is a cross-platform asynchronous event-driven network application framework for rapid development of maintainable high performance protocol servers & clients. Low-level protocol implementations are often a collection of ChannelHandlers that implement a protocol but still require the user to have a good understanding of SwiftNIO. Often, low-level protocol implementations will then be wrapped in high-level libraries with a nicer, more user-friendly API. High-level implementations are...
    Downloads: 0 This Week
    Last Update:
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  • 2
    MMdnn

    MMdnn

    Tools to help users inter-operate among deep learning frameworks

    ...E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML. MMdnn is a comprehensive and cross-framework tool to convert, visualize and diagnose deep learning (DL) models. The "MM" stands for model management, and "dnn" is the acronym of deep neural network. We implement a universal converter to convert DL models between frameworks, which means you can train a model with one framework and deploy it with another. During the model conversion, we generate some code snippets to simplify later retraining or inference. ...
    Downloads: 0 This Week
    Last Update:
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