Skip to main content

DiskANN Python extension module

Project description

diskannpy

DiskANN Paper DiskANN Paper DiskANN Paper DiskANN Main PyPI version Downloads shield License: MIT

Installation

Packages published to PyPI will always be built using the latest numpy major.minor release (at this time, 1.25).

Conda distributions for versions 1.19-1.25 will be completed as a future effort. In the meantime, feel free to clone this repository and build it yourself.

Local Build Instructions

Please see the Project README for system dependencies and requirements.

After ensuring you've followed the directions to build the project library and executables, you will be ready to also build diskannpy with these additional instructions.

Changing Numpy Version

In the root folder of DiskANN, there is a file pyproject.toml. You will need to edit the version of numpy in both the [build-system.requires] section, as well as the [project.dependencies] section. The version numbers must match.

Linux

python3.11 -m venv venv # versions from python3.9 and up should work
source venv/bin/activate
pip install build
python -m build

Windows

py -3.11 -m venv venv # versions from python3.9 and up should work
venv\Scripts\Activate.ps1
pip install build
python -m build

The built wheel will be placed in the dist directory in your DiskANN root. Install it using pip install dist/<wheel name>.whl

Citations

Please cite this software in your work as:

@misc{diskann-github,
   author = {Simhadri, Harsha Vardhan and Krishnaswamy, Ravishankar and Srinivasa, Gopal and Subramanya, Suhas Jayaram and Antonijevic, Andrija and Pryce, Dax and Kaczynski, David and Williams, Shane and Gollapudi, Siddarth and Sivashankar, Varun and Karia, Neel and Singh, Aditi and Jaiswal, Shikhar and Mahapatro, Neelam and Adams, Philip and Tower, Bryan and Patel, Yash}},
   title = {{DiskANN: Graph-structured Indices for Scalable, Fast, Fresh and Filtered Approximate Nearest Neighbor Search}},
   url = {https://github.com/Microsoft/DiskANN},
   version = {0.6.1},
   year = {2023}
}

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

diskannpy-0.6.1-cp311-cp311-win_amd64.whl (4.5 MB view details)

Uploaded CPython 3.11 Windows x86-64

diskannpy-0.6.1-cp311-cp311-manylinux_2_28_x86_64.whl (91.3 MB view details)

Uploaded CPython 3.11 manylinux: glibc 2.28+ x86-64

diskannpy-0.6.1-cp310-cp310-win_amd64.whl (4.5 MB view details)

Uploaded CPython 3.10 Windows x86-64

diskannpy-0.6.1-cp310-cp310-manylinux_2_28_x86_64.whl (91.3 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.28+ x86-64

diskannpy-0.6.1-cp39-cp39-win_amd64.whl (4.5 MB view details)

Uploaded CPython 3.9 Windows x86-64

diskannpy-0.6.1-cp39-cp39-manylinux_2_28_x86_64.whl (91.3 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.28+ x86-64

File details

Details for the file diskannpy-0.6.1-cp311-cp311-win_amd64.whl.

File metadata

File hashes

Hashes for diskannpy-0.6.1-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 0be2c5edf0ce17ea8eaea286832c1f54901da46ea8d11965ce859a0850b17293
MD5 a8d85c0557c7b67a8b8d219131120e1e
BLAKE2b-256 8bd86d468e12d83ac1a3b8f92e57948e8cc1ea3e3d2f69992ca8161ec95199e5

See more details on using hashes here.

File details

Details for the file diskannpy-0.6.1-cp311-cp311-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for diskannpy-0.6.1-cp311-cp311-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 a48b9e8d8b5a0d875099cd88f41709caf554b8167b24d6216d0b458c6857a905
MD5 f26d7ad35c4c1b4801f75d26018cc5bf
BLAKE2b-256 5c3e99c7e12a5a5224aea6dacf304d8c3631660fd49978231daf2999ab528173

See more details on using hashes here.

File details

Details for the file diskannpy-0.6.1-cp310-cp310-win_amd64.whl.

File metadata

File hashes

Hashes for diskannpy-0.6.1-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 daf4d405d9faa041bf9cd1e9ae136d67f769ae28eb5c282f87a605760aa85d2f
MD5 1814eb28b69c23e1f1dca35c84f0ca8c
BLAKE2b-256 ae96529151fd551dbdebbfc1a721cb2e915d05089ed5866745718247e67c129f

See more details on using hashes here.

File details

Details for the file diskannpy-0.6.1-cp310-cp310-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for diskannpy-0.6.1-cp310-cp310-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 04a1e126cb042a110f915fb93a91411adae00f14a6f3a05624b97587a7223c75
MD5 a66eb84832cebf501ab3194ff7fa8b83
BLAKE2b-256 0528a1d66258010acdaea17d2f2728e3ea333dd1c4187f944bfb2f10cf2aabaf

See more details on using hashes here.

File details

Details for the file diskannpy-0.6.1-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: diskannpy-0.6.1-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 4.5 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.4

File hashes

Hashes for diskannpy-0.6.1-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 bfdb5793cf600aea4d72504092177cc66bee13f11ddca6a0aedbe84ca76d149a
MD5 6336326c57fd2cee577d2278b03cb423
BLAKE2b-256 8fa2fa434ea71cf5b9e7ff0764c2d4dd45b02e570df6a09c3e0092f1ec8e9e8d

See more details on using hashes here.

File details

Details for the file diskannpy-0.6.1-cp39-cp39-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for diskannpy-0.6.1-cp39-cp39-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 fc507deea95d41f20cf63d3e9157079736d9faf688e6d86331253b4c48a8b70e
MD5 e6594ed87dc2129b985804ed665f9d5d
BLAKE2b-256 e716face3d5319a92057928dc86f46ffcb0853bb76e6cebdbfb714ce94225a33

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page