{"categories":[{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/3"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/2"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/6"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/3"}],"enrichment":{"capability":"Provides dict-like configuration data structures with dot-notation access, type safety, and lazy evaluation for ML experiment and model configuration.","skillfed_tags":["ml-config","experiment-management","type-safe-dicts"],"use_cases":["Store and manage experiment hyperparameters with type safety to prevent configuration errors.","Create immutable, hashable configuration snapshots for reproducible ML experiments using FrozenConfigDict.","Define dependent configuration fields using lazy evaluation so changing a base value automatically updates derived values.","Serialize and deserialize experiment configs to YAML for easy version control and sharing across teams.","Prevent typos in nested config access by using dot notation with locking to catch misspelled field names early."],"what_it_does":"ML Collections provides ConfigDict and FrozenConfigDict\u2014specialized dictionary classes designed for ML workflows. ConfigDict lets you access nested configuration values using dot notation (cfg.learning_rate) instead of bracket syntax, with built-in type checking to catch spelling mistakes and type mismatches. FrozenConfigDict is an immutable, hashable variant useful for passing configs as function arguments or storing them in sets. Both support lazy evaluation through FieldReferences, allowing one field to depend on another and recompute automatically when the source changes.\n\nThe library depends on absl-py and PyYAML to handle logging and YAML serialization. It's widely used in ML research and production to centralize experiment hyperparameters, model settings, and training configurations in a single, strongly-typed object that's easier to reason about than plain dictionaries.","worth_installing":"Yes. ML Collections fills a genuine gap in Python ML tooling by providing type-safe, dot-accessible configuration objects with lazy evaluation. Low install friction, active maintenance, permissive license, and no known vulnerabilities make it a straightforward choice for any ML project that needs structured configuration management beyond plain dicts."},"id":"ml-collections","links":{"html":"https://skillfed.io/packages/ml-collections","md":"https://skillfed.io/packages/ml-collections.md","pypi":"https://pypi.org/project/ml-collections/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2025-04-17","license_spdx":null,"license_treatment":"permissive","name":"ml-collections","python_support":"supports_current","summary":"ML Collections is a library of Python collections designed for ML usecases."},"popularity":{"monthly_downloads":2197822,"position":3218,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"1.1.0"}