3 projects for "edge computing" with 2 filters applied:

  • MongoDB Atlas runs apps anywhere Icon
    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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  • Custom VMs From 1 to 96 vCPUs With 99.95% Uptime Icon
    Custom VMs From 1 to 96 vCPUs With 99.95% Uptime

    General-purpose, compute-optimized, or GPU/TPU-accelerated. Built to your exact specs.

    Live migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
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  • 1
    sebastian/diff

    sebastian/diff

    Diff implementation

    ...Internally, the library splits input into hunks and manages edge cases such as whitespace-only changes and end-of-line variations. Its abstractions make it straightforward to plug custom output styles or colorizers without reimplementing diff logic. Because it’s the diff engine under PHPUnit, it has been exercised across countless projects and edge cases.
    Downloads: 0 This Week
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  • 2
    Unikraft

    Unikraft

    A next-generation cloud native kernel designed to unlock performance

    Unikraft powers the next generation of cloud-native, containerless applications by enabling you to radically customize and build custom OS/kernels; unlocking best-in-class performance, security primitives, and efficiency savings. Unikraft optimizes resource utilization, leading to smaller footprints (meaning higher server saturation) and improved efficiency in resource-constrained environments. Unikraft is an open-source project driven by a vibrant community of over 100 developers, fostering...
    Downloads: 0 This Week
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  • 3
    PyCNN

    PyCNN

    Image Processing with Cellular Neural Networks in Python

    Image Processing with Cellular Neural Networks in Python. Cellular Neural Networks (CNN) are a parallel computing paradigm that was first proposed in 1988. Cellular neural networks are similar to neural networks, with the difference that communication is allowed only between neighboring units. Image Processing is one of its applications. CNN processors were designed to perform image processing; specifically, the original application of CNN processors was to perform real-time ultra-high...
    Downloads: 0 This Week
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