{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/13"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/5"}],"enrichment":{"capability":"Serializes and deserializes NumPy arrays and Python complex types using msgpack's efficient binary format, with optional monkey-patching to make msgpack globally NumPy-aware.","skillfed_tags":["serialization","numpy-integration"],"use_cases":["Serialize NumPy arrays to disk or network in a compact binary format while preserving dtype information.","Make msgpack-based RPC or message-passing systems NumPy-aware without modifying existing msgpack call sites.","Exchange numerical data between Python processes or services using msgpack as the transport layer.","Store machine learning model weights or intermediate tensors in a format more efficient than JSON or pickle.","Integrate NumPy serialization into existing msgpack-based data pipelines without rewriting encoder/decoder logic."],"what_it_does":"msgpack-numpy bridges NumPy and msgpack by adding type-aware serialization for NumPy arrays and Python complex numbers. It works by providing custom encoder and decoder functions that preserve dtype information during msgpack serialization, or by monkey-patching msgpack globally so all serialization calls become NumPy-aware automatically.\n\nThe package is designed for scenarios where you need efficient, compact binary serialization of numerical data while preserving type information across serialization boundaries. It trades some storage overhead (for type metadata) against the convenience of not having to manually reconstruct dtypes after deserialization. The main constraint is that deserialized arrays are read-only, and arrays with object dtype ('O') fall back to pickle, which defeats much of msgpack's efficiency advantage.","worth_installing":"Yes, if you already use msgpack and need NumPy array serialization with type preservation. No, if you are starting a new project\u2014the dormant maintenance (last release 2023-10-02, no recent commits) and low GitHub stars (2) suggest limited community support and risk of compatibility drift with future NumPy or msgpack releases. Consider pickle or Protocol Buffers for new work unless msgpack integration is a hard requirement."},"id":"msgpack-numpy-opentensor","links":{"html":"https://skillfed.io/packages/msgpack-numpy-opentensor","md":"https://skillfed.io/packages/msgpack-numpy-opentensor.md","pypi":"https://pypi.org/project/msgpack-numpy-opentensor/"},"maintenance":{"status":"dormant"},"meta":{"latest_release":"2023-10-02","license_spdx":null,"license_treatment":"permissive","name":"msgpack-numpy-opentensor","python_support":"unspecified","summary":"Numpy data serialization using msgpack"},"popularity":{"monthly_downloads":215354,"position":9402,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.5.0"}