Showing 4 open source projects for "developer"

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    MongoDB Atlas runs apps anywhere

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  • 1
    RamaLama

    RamaLama

    Simplifies the local serving of AI models from any source

    RamaLama is an open-source developer tool that simplifies working with and serving AI models locally or in production by leveraging container technologies like Docker, Podman, and OCI registries, allowing AI inference workflows to be treated like standard container deployments. It abstracts away much of the complexity of configuring AI runtimes, dependencies, and hardware optimizations by detecting available GPUs (or falling back to CPU) and automatically pulling a container image pre-configured for the detected hardware environment. ...
    Downloads: 0 This Week
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  • 2
    Xorbits Inference

    Xorbits Inference

    Replace OpenAI GPT with another LLM in your app

    ...With Xorbits Inference, you can effortlessly deploy and serve your or state-of-the-art built-in models using just a single command. Whether you are a researcher, developer, or data scientist, Xorbits Inference empowers you to unleash the full potential of cutting-edge AI models.
    Downloads: 0 This Week
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  • 3
    Sockeye

    Sockeye

    Sequence-to-sequence framework, focused on Neural Machine Translation

    ...If you are interested in collaborating or have any questions, please submit a pull request or issue. You can also send questions to sockeye-dev-at-amazon-dot-com. Developers may be interested in our developer guidelines. Starting with version 3.0.0, Sockeye is also based on PyTorch. We maintain backwards compatibility with MXNet models of version 2.3.x with 3.0.x. If MXNet 2.x is installed, Sockeye can run both with PyTorch or MXNet. All models trained with 2.3.x (using MXNet) can be converted to models running with PyTorch using the converter CLI (sockeye.mx_to_pt).
    Downloads: 0 This Week
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  • 4
    BudgetML

    BudgetML

    Deploy a ML inference service on a budget in 10 lines of code

    ...Deploying from scratch involves learning too many different concepts like SSL certificate generation, Docker, REST, Uvicorn/Gunicorn, backend servers etc., that are simply not within the scope of a typical data scientist. BudgetML is our answer to this challenge. It is supposed to be fast, easy, and developer-friendly. It is by no means meant to be used in a full-fledged production-ready setup. It is simply a means to get a server up and running as fast as possible with the lowest costs possible.
    Downloads: 0 This Week
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