facebookresearch / recipes

Recipes are a standard, well supported set of blueprints for machine learning engineers to rapidly train models using the latest research techniques without significant engineering overhead.Specifically, recipes aims to provide- Consistent access to pre-trained SOTA models ready for production- Reference implementations for SOTA research reproducibility, and infrastructure to guarantee correctness, efficiency, and interoperability.

Summary

	TOML IN
		Main Code: 3,732 LOC (122 files) = PY (90%) + YAML (9%) + TOML (<1%) + IN (<1%) Secondary code: Test: 1,339 LOC (29); Generated: 0 LOC (0); Build & Deploy: 0 LOC (0); Other: 311 LOC (6);
		Duplication: 11%
		File Size: 0% long (>1000 LOC), 81% short (<= 200 LOC)
		Unit Size: 0% long (>100 LOC), 87% short (<= 10 LOC)
		Conditional Complexity: 0% complex (McCabe index > 50), 96% simple (McCabe index <= 5)
		Logical Component Decomposition: primary (14 components)
		1 month old 0% of code older than 365 days 0% of code not updated in the past 365 days
		0% of code updated more than 50 times Also see temporal dependencies for files frequently changed in same commits.
		Goals: Keep the system simple and easy to change (4)
	Features of interest: TODOs 5 files

Commits Trend

Latest commit date: 2022-01-12

16 commits (30 days) 6 contributors (30 days)

Commits 13 33 Contributors 4 13 2022 2021

show commits trend per language

Commits	2022 2021 10 20 py 4 yaml toml toml in in
Contributors	2022 2021 4 10 py 4 yaml toml toml in in