google/ml_collections

Health v2 · maintenance only

Adoption not part of the score

1036 stars · 49 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

ML Collections is a Python library providing dict-like data structures (ConfigDict and FrozenConfigDict) for managing ML experiment and model configurations. It offers dot-based nested access, type safety, immutability, lazy computation, and YAML-readable printing.

Use cases

• manage hyperparameter configs for ML experiments
• define model configuration files with dot access
• create immutable frozen configs for reproducible runs
• avoid typos in nested config dictionaries
• share experiment configs as keyword arguments

When to choose

• you need structured, type-safe configuration for ML experiments
• you want immutable, hashable config objects
• you work in Python ML research pipelines (e.g., JAX/Flax ecosystems)

When to avoid

• you need a general-purpose config format like YAML or TOML files alone
• your project is not Python-based
• you need runtime-editable configs with no locking semantics

Facets

library · maturity stable

configuration-management developer-tools machine-learning developer-tools deep-learning python configdict experiment-configuration hyperparameters dot-access type-safety

Member repositories

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markdown · JSON · MCP: product_card(name="google/ml_collections")