{"categories":[{"label":"Compression","url":"https://skillfed.io/packages/category/system-archiving-compression"}],"enrichment":{"capability":"Lossily compresses floating-point NumPy arrays into byte strings with user-specified accuracy, designed for storing training data and models in machine learning workflows.","skillfed_tags":["data-compression","machine-learning","numpy-integration"],"use_cases":["Store large training datasets on disk with reduced footprint while maintaining acceptable numerical precision for model training.","Compress neural network model weights for efficient distribution and deployment in bandwidth-constrained environments.","Archive historical experimental results and intermediate model checkpoints with controlled accuracy loss.","Reduce memory usage when loading large arrays into RAM during batch processing or inference pipelines.","Serialize floating-point data for transmission over networks where bandwidth is a bottleneck."],"what_it_does":"lilcom is a lossy compression utility that encodes NumPy floating-point arrays into compact byte strings, trading accuracy for storage efficiency. It uses a regression-based algorithm that models each element relative to its neighbors, then quantizes and compresses the residuals. The core implementation is in C++, and the compression ratio is controlled by a tick_power parameter that sets the quantization step size; the default tick_power of -8 yields a maximum error of 1/512 per element. It is intended for machine learning workflows where storing large volumes of training data or model weights benefits from reduced disk footprint and memory usage.\n\nThe package requires numpy as its only runtime dependency and supports Python 3.6 and later. Installation via pip uses prebuilt wheels for modern Python versions (3.10\u20133.14) on common platforms, but building from source requires a C++ compiler. The repository is maintained but aging, with no recent feature development; it remains suitable for stable, production use in contexts where the compression trade-offs are acceptable.","worth_installing":"Yes, if you need lossy compression of NumPy arrays in machine learning contexts and can accept the accuracy trade-offs. The package is stable, has no known vulnerabilities, and carries a permissive MIT license. Install friction is moderate due to C++ compilation, but prebuilt wheels are available for modern Python versions. The aging maintenance status is not a blocker for stable use, but expect no active feature development."},"id":"lilcom","links":{"html":"https://skillfed.io/packages/lilcom","md":"https://skillfed.io/packages/lilcom.md","pypi":"https://pypi.org/project/lilcom/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2026-01-15","license_spdx":null,"license_treatment":"permissive","name":"lilcom","python_support":"supports_current","summary":"Lossy-compression utility for sequence data in NumPy"},"popularity":{"monthly_downloads":706753,"position":5270,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.8.2"}