---
id: torch-einops-utils
version: "0.1.19"
license: MIT License Copyright (c) 2026 Phil Wang Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in… (full text in the JSON record)
license_treatment: permissive
maintenance: active
---

# torch-einops-utils — Personal utility functions

License: permissive · Maintenance: active · Downloads: 619.3K/mo

## What it is and what it does

torch-einops-utils is a lightweight utility library that wraps and extends einops and torch to simplify tensor operations in machine learning workflows. It depends on einops and torch as its only runtime dependencies, making it a thin convenience layer rather than a heavy framework. The package targets developers working with PyTorch who want faster, more readable tensor manipulation code.

Released on 2026-01-09 and actively maintained (most recent release 2026-08-11), the package is in Beta status and requires Python 3.9 or later. It carries no known security vulnerabilities and uses a permissive MIT License. With roughly 619277 monthly downloads and a position in the top 15000 PyPI packages, it has found modest adoption in the ML community.

Use it for:

- Simplify tensor reshaping and dimension manipulation in PyTorch models using einops-based helpers.
- Accelerate prototyping of neural network layers by reducing boilerplate tensor operation code.
- Standardize tensor operations across a team's ML codebase with shared utility functions.
- Integrate einops patterns into existing PyTorch projects with minimal overhead.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Provides utility functions for PyTorch and einops to accelerate tensor manipulation in ML workflows.

Yes, if you use PyTorch and einops regularly. Low install friction, active maintenance, permissive license, and no known vulnerabilities make it a safe addition. The Beta status and modest documentation suggest it's best suited for developers comfortable exploring a smaller utility library rather than a mature framework. Check the repository for specific functions that match your tensor manipulation needs.

## Install

pip install torch-einops-utils
uv add torch-einops-utils
poetry add torch-einops-utils

## Installing torch-einops-utils

Before you install: Low install friction with a pure Python wheel. Active maintenance—released 3 days ago with a recent commit on 2026-08-11. Repository has 52 stars and is not archived.

License in practice: MIT License permits free use, modification, and distribution with minimal restrictions, making it suitable for both open-source and commercial projects.

Quickstart:

pip install torch-einops-utils

import torch
from torch_einops_utils import ...

# Use utility functions with torch and einops

Requires Python 3.9 or later; torch and einops must be installed as runtime dependencies.

Verify before relying:

- What specific utility functions are included and their scope of applicability to different ML tasks.
- Whether the package is stable enough for production use given its Beta development status.

## Package facts

- License: MIT License Copyright (c) 2026 Phil Wang Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in… (full text in the JSON record) (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 619.3K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

pytorch tensor utilities, einops helpers, torch einops wrapper, ml tensor operations, pytorch convenience functions, einops pytorch integration, tensor manipulation utils, pytorch, tensor-ops, ml-utilities

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