pykeops

library2.3pypypiunverified

PyKeOps is a Python package providing bindings to the KeOps library, which enables efficient computation of kernel operations on CPUs and GPUs with automatic differentiation and without memory overflows. It is widely used in geometry processing, shape analysis, and machine learning for large-scale distance and kernel computations. Current version is 2.3, with active development on GitHub.

ai-ml data

pip install pykeops

github ↗package ↗homepage ↗

INSTALL

IMPORT

SIG · PYKEOPS

pykeops

ai-mlpythonv2.3

Install

6.1s avg

Import

—

Disk

94MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v2.3 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.95 runs

installs and imports cleanly · install 0.0s · import 0.000s · 95.2MB

glibc

py 3.10–3.95 runs

installs and imports cleanly · install 6.1s · import 0.000s · 91MB

94MB installed

● package 94MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

LazyTensor

✓ from pykeops import LazyTensor

✗ from pykeops.torch import LazyTensor

Compute pairwise Gaussian kernel sum using LazyTensors for memory-efficient reduction.

import torch from pykeops.torch import LazyTensor # Create sample data x = torch.randn(1000, 3).cuda() y = torch.randn(2000, 3).cuda() # Define LazyTensors X = LazyTensor(x[:, None, :]) # (1000, 1, 3) Y = LazyTensor(y[None, :, :]) # (1, 2000, 3) # Compute pairwise distances squared D = ((X - Y) ** 2).sum(2) # Symbolic reduction # Compute softmin (log-sum-exp) with Gaussian kernel K = (-D).exp() result = K.sum(1) # (1000,) actually returns a LazyTensor, evaluate with .detach() print(result.detach())

Debug

Known issues

breakingPyKeOps 2.0 introduced a completely reworked compilation engine. Old code using from pykeops.torch import generic_logsumexp or similar may not work. Update to LazyTensor API.

fix

Migrate to LazyTensor-based API: replace generic reductions with symbolic expressions.

affects: <2.0

gotchaLazyTensor operations must be explicitly evaluated (e.g., .eval(), .detach(), or .sum() returns a LazyTensor, not a torch.Tensor). Forgetting evaluation leads to symbolic objects in the result.

fix

Call .eval() on the final LazyTensor or use reduction operations that return tensors (some like .sum() still return LazyTensor).

affects: all

deprecatedThe old API using pykeops.torch.generic_sum, generic_logsumexp, etc. is deprecated in 2.x and may be removed. Use LazyTensor or Genred instead.

fix

Use LazyTensor or Genred for custom formulas.

affects: >=2.0

Upgrade

Version history

2.3latest on PyPI · released Apr 17, 2025

Audit

Dependencies

torchrequiredPyKeOps integrates with PyTorch for tensor operations and autograd.

Agent activity

12 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/getkeops/keops ↗

packagehttps://pypi.org/project/pykeops/ ↗

homepagehttp://www.kernel-operations.io/ ↗