Showing 2 open source projects for "high performance computing"

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  • 1
    Dask

    Dask

    Parallel computing with task scheduling

    Dask is a Python library for parallel and distributed computing, designed to scale analytics workloads from single machines to large clusters. It integrates with familiar tools like NumPy, Pandas, and scikit-learn while enabling execution across cores or nodes with minimal code changes. Dask excels at handling large datasets that don’t fit into memory and is widely used in data science, machine learning, and big data pipelines.
    Downloads: 2 This Week
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  • 2
    Nuclio

    Nuclio

    High-Performance Serverless event and data processing platform

    Nuclio is an open source and managed serverless platform used to minimize development and maintenance overhead and automate the deployment of data-science-based applications. Real-time performance running up to 400,000 function invocations per second. Portable across low laptops, edge, on-prem and multi-cloud deployments. The first serverless platform supporting GPUs for optimized utilization and sharing. Automated deployment to production in a few clicks from Jupyter notebook. Deploy one of...
    Downloads: 2 This Week
    Last Update:
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