Practical overview
When it fits
Use Einops when tensor transformations recur in Python code and explicit axis names would make reshaping, reductions or model-layer transformations easier to read.
Who it is for
- Python developers working with tensors or arrays in machine-learning and scientific-computing projects.
- Deep-learning practitioners who want tensor dimension changes to be explicit in model code.
- Developers using supported frameworks including PyTorch, JAX, TensorFlow, NumPy and MLX.
Documented uses
- Reorder or reshape tensor axes with a pattern that describes their meanings.
- Reduce tensor dimensions, including averaging grouped spatial regions.
- Repeat data along a new axis.
- Reversibly pack and unpack tensors, including tensors with different dimensionality.
- Write multi-framework einsum operations with multi-letter axis names.
Requirements & limits
The repository lists the MIT license and documents installation with pip.
- Einops is a Python library integrated into a project, not a hosted model or AI service.
- The repository identifies support for NumPy, PyTorch, JAX, MLX and other frameworks; available layer modules vary by framework.
- Core operation patterns name axes, while einsum supports multi-letter axis names.
- The README documents a single-package pip installation command.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Install einops and rearrange a tensor
Install the Python library and express a tensor dimension rearrangement with einops.
Before you start
- Python 3.10 or later.
- pip available in the Python environment.
Steps
Install the library with the command below.
pip install einopsImport
rearrangewithfrom einops import rearrange.Apply a pattern such as
rearrange(input_tensor, 't b c -> b c t')to rearrange the tensor axes.
Expected outcome
einops is installed, and the example rearranges a tensor from t-b-c axis order to b-c-t.
Guide limitations
- The pattern must describe the input and desired output axes; the example does not provide an input tensor or verify a result.
- This covers installation and a basic rearrangement, not framework-specific setup.
Add a flattening layer to a PyTorch model
Use einops `Rearrange` in a PyTorch sequential model to flatten feature dimensions before a linear layer.
Before you start
- Python 3.10 or later.
- PyTorch and einops available in the Python environment.
- A model input compatible with the documented convolution and linear layer dimensions.
Steps
Import
Sequential,Conv2d,MaxPool2d,Linear, andReLUfromtorch.nn.Import
Rearrangefromeinops.layers.torch.In the sequential model, place
Rearrange('b c h w -> b (c h w)')after the convolution and pooling layers and before the linear layer, following the documented example.
Expected outcome
The model definition includes an einops layer that flattens channel and spatial dimensions without a separate forward-method flatten operation.
Guide limitations
- The example uses an ellipsis for preceding model content and assumes particular tensor dimensions for its linear layer.
- The README says operations and layers can be compiled with
torch.compile, but this recipe does not cover compilation or verify model execution.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 14 October 2026
- Original website title
- arogozhnikov/einops · Official homepage title · retained in its source language