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TOROS N2 - lightweight approximate Nearest Neighbor library which runs faster even with large datasets
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README.rst

N2

https://travis-ci.org/kakao/n2.svg?branch=master https://img.shields.io/pypi/v/n2.svg?style=flat

N2 - approximate Nearest Neighbor

import numpy as np
from n2 import HnswIndex

N, dim = 10240, 20
samples = np.arange(N * dim).reshape(N, dim)

index = HnswIndex(dim)
for sample in samples:
    index.add_data(sample)
index.build(m=5, n_threads=4)
print(index.search_by_id(0, 10))
# [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

Install

To install:

sudo pip install n2

For more detail, see the installation for instruction on how to build N2 from source.

Introduce

N2 is an approximate nearest neighborhoods algorithm library written in C++ (including Python/Go bindings). N2 provides a much faster search speed than other implementations when modeling large dataset. Also, N2 supports multi-core CPUs for index building.

Background

There are great approximate nearest neighborhoods libraries such as annoy and nmslib, but they did not fully meet the requirments to handle Kakao’s dataset. Therefore, we decided to implement a library that improves usability and performs better based on nmslib. And finally, we release N2 to the world.

Features

  • Efficient implementations. N2 is a lightweight library which runs faster even with large datasets.
  • Support multi-core CPUs for index building.
  • Support a mmap feature by default for handling large index files efficiently.
  • Support Python/Go bindings.

Performance

If you want to read about detail benchmark explanation. See the benchmark for more experiments.

Index build times

image0

Search speed

image1

Memory usage

image2

Bindings

The following guides explain how to use N2 with basic examples and API.

References

License

This software is licensed under the Apache 2 license, quoted below.

Copyright 2017 Kakao Corp. http://www.kakaocorp.com

Licensed under the Apache License, Version 2.0 (the “License”); you may not use this project except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0.

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

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