Showing 3 open source projects for "project code for cloud computing"

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    Fully Managed MySQL, PostgreSQL, and SQL Server

    Automatic backups, patching, replication, and failover. Focus on your app, not your database.

    Cloud SQL handles your database ops end to end, so you can focus on your app.
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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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    HolmesGPT

    HolmesGPT

    CNCF Sandbox Project

    ...Rather than requiring engineers to manually correlate large volumes of monitoring data, HolmesGPT automatically synthesizes evidence and presents explanations in natural language. The project is developed by Robusta and has been accepted as a Cloud Native Computing Foundation Sandbox project, highlighting its relevance to the cloud-native ecosystem. It is designed to operate as an automated troubleshooting assistant that can analyze incidents continuously and support on-call engineers during outages.
    Downloads: 4 This Week
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  • 2
    E2B Cookbook

    E2B Cookbook

    Examples of using E2B

    E2B Cookbook is an open-source collection of example projects, guides, and reference implementations demonstrating how to build applications using the E2B platform. The repository acts as a practical learning resource for developers who want to integrate AI agents with secure cloud execution environments that allow large language models to run code and interact with tools. The examples illustrate how developers can build AI workflows capable of performing tasks such as data analysis, code execution, and application generation inside isolated sandbox environments. E2B itself provides secure Linux-based sandboxes that enable AI systems to safely run generated code and interact with real computing resources without compromising the host environment. ...
    Downloads: 0 This Week
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  • 3
    Mixtral offloading

    Mixtral offloading

    Run Mixtral-8x7B models in Colab or consumer desktops

    ...This approach takes advantage of the sparse activation properties of mixture-of-experts architectures, where only a subset of expert networks are used for each token during generation. By selectively loading and caching the required experts, the system avoids keeping the entire model in GPU memory at once. The repository includes notebooks and code examples that demonstrate how to run large language models on consumer hardware such as personal GPUs or cloud notebook environments.
    Downloads: 0 This Week
    Last Update:
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