{"id":27240572,"url":"https://github.com/jonmarty/pycuda-kmeans","last_synced_at":"2026-04-25T12:36:33.594Z","repository":{"id":76011006,"uuid":"174723205","full_name":"jonmarty/PyCuda-KMeans","owner":"jonmarty","description":"A parallelized PyCuda implementation of the KMeans clustering algorithm.","archived":false,"fork":false,"pushed_at":"2019-03-18T14:16:06.000Z","size":6,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"master","last_synced_at":"2025-04-10T19:47:36.680Z","etag":null,"topics":["cuda","kmeans","pycuda"],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/jonmarty.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2019-03-09T17:13:00.000Z","updated_at":"2021-08-13T03:06:10.000Z","dependencies_parsed_at":null,"dependency_job_id":"754c9ad3-6fbb-4bce-9b23-f9d86f17ec31","html_url":"https://github.com/jonmarty/PyCuda-KMeans","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/jonmarty/PyCuda-KMeans","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jonmarty%2FPyCuda-KMeans","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jonmarty%2FPyCuda-KMeans/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jonmarty%2FPyCuda-KMeans/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jonmarty%2FPyCuda-KMeans/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jonmarty","download_url":"https://codeload.github.com/jonmarty/PyCuda-KMeans/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jonmarty%2FPyCuda-KMeans/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32262801,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-25T09:15:33.318Z","status":"ssl_error","status_checked_at":"2026-04-25T09:15:31.997Z","response_time":59,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["cuda","kmeans","pycuda"],"created_at":"2025-04-10T19:19:26.663Z","updated_at":"2026-04-25T12:36:33.572Z","avatar_url":"https://github.com/jonmarty.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# PyCuda-KMeans\nA parallelized PyCuda implementation of the KMeans clustering algorithm.\n\n## Setup\nFirst, install the CUDA Toolkit on your computer. Downloads and instructions can be found at [https://developer.nvidia.com/cuda-downloads].\n\nThen, install the pycuda library with\n  \n  pip install pycuda\n  \nYou will also need numpy, if you don't have it, you can install it with\n  \n  pip install numpy\n  \nVerify that the pycuda library is working properly using\n  \n  python PyCudaCheck.py\n  \nThis piece of code generates the array {1 2 3 ... 398 399 400}, which is sent to the gpu. The array is retrieved from the gpu and printed. Then a function is applied to the array that doubles all the values, and the array is again retrieved from the gpu and printed, the result should be {2 4 6 ... 796 798 800}.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjonmarty%2Fpycuda-kmeans","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjonmarty%2Fpycuda-kmeans","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjonmarty%2Fpycuda-kmeans/lists"}