Showing 4 open source projects for "stores"

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    ArduinoJson

    ArduinoJson

    JSON library for Arduino and embedded C++. Simple and efficient

    ...ArduinoJson supports two coding styles, with implicit or explicit casts. ArduinoJson deduplicates strings in the JSON document. When you have several identical keys or values, the JsonDocument only stores one of each. In practice, this feature reduces memory consumption by 20-30%.
    Downloads: 3 This Week
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  • 2
    Faiss

    Faiss

    Library for efficient similarity search and clustering dense vectors

    Faiss is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. Faiss is written in C++ with complete wrappers for Python/numpy. Some of the most useful algorithms are implemented on the GPU. It is developed by Facebook AI Research. Faiss contains several methods for similarity search. It...
    Downloads: 5 This Week
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  • 3
    Rapid YAML

    Rapid YAML

    Library to parse and emit YAML, and do it fast

    ...The data tree is a flat index-based structure stored in a single array. Serialization happens only at your direct request, after parsing / before emitting. Internally, the data tree representation stores only string views and has no knowledge of types, but of course, every node can have a YAML type tag. ryml makes it easy and fast to read and modify the data tree.
    Downloads: 1 This Week
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  • 4
    TensorStore

    TensorStore

    Library for reading and writing large multi-dimensional arrays

    TensorStore is a high-performance library for reading and writing N-dimensional arrays that live in many different storage systems, from local files to cloud object stores. It separates the logical view (shape, dtype, chunking) from the physical layout so the same code can target Zarr, N5, TIFF pyramids, or custom backends. Rich indexing, slicing, and broadcasting operations make it feel like a familiar array API, while asynchronous I/O pipelines stream chunks efficiently in parallel. Transactional semantics allow atomic updates and consistent snapshots, which is essential for large, shared datasets used by ML and scientific workflows. ...
    Downloads: 0 This Week
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