arogozhnikov/einops

Health v2 · maintenance only

Adoption not part of the score

9615 stars · 402 forks observed · 2026-10-07

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

einops is a Python library providing readable, framework-agnostic tensor operations via mini-language functions like rearrange, reduce, and repeat. It works across numpy, PyTorch, JAX, TensorFlow, MLX, and other array backends.

Use cases

• reshape and transpose tensors with readable expressions
• write deep learning code that works across pytorch, jax, and tensorflow
• replace verbose reshape, permute, and squeeze calls
• reduce and aggregate tensor dimensions declaratively
• pack and unpack variable-length tensor lists
• implement custom layers like EinMix for neural networks

When to choose

• you work with high-dimensional tensors in deep learning and want readable, less error-prone code
• you need the same tensor manipulation code to run on multiple frameworks
• you want to simplify stacking, reshaping, transposition, and reduction logic

When to avoid

• you only do simple 2D array math where numpy alone suffices
• you need maximum raw performance with zero abstraction overhead
• your team is unwilling to learn the einops mini-language syntax

Facets

library · maturity stable

machine-learning deep-learning math developer-tools deep-learning machine-learning data-science python cross-platform tensor-manipulation einsum pytorch jax numpy tensor-operations

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