{"id":13687869,"url":"https://github.com/dfdazac/wassdistance","last_synced_at":"2025-05-01T15:33:25.254Z","repository":{"id":34433796,"uuid":"172699252","full_name":"dfdazac/wassdistance","owner":"dfdazac","description":"Approximating Wasserstein distances with PyTorch","archived":false,"fork":false,"pushed_at":"2023-04-15T10:10:02.000Z","size":390,"stargazers_count":452,"open_issues_count":3,"forks_count":56,"subscribers_count":4,"default_branch":"master","last_synced_at":"2024-08-03T15:06:52.164Z","etag":null,"topics":["deep-learning","machine-learning","optimal-transport","pytorch"],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/dfdazac.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2019-02-26T11:32:58.000Z","updated_at":"2024-07-24T02:52:49.000Z","dependencies_parsed_at":"2022-07-13T19:10:03.850Z","dependency_job_id":null,"html_url":"https://github.com/dfdazac/wassdistance","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dfdazac%2Fwassdistance","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dfdazac%2Fwassdistance/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dfdazac%2Fwassdistance/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dfdazac%2Fwassdistance/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/dfdazac","download_url":"https://codeload.github.com/dfdazac/wassdistance/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":224266371,"owners_count":17283124,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","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":["deep-learning","machine-learning","optimal-transport","pytorch"],"created_at":"2024-08-02T15:01:02.300Z","updated_at":"2025-05-01T15:33:25.248Z","avatar_url":"https://github.com/dfdazac.png","language":"Jupyter Notebook","funding_links":[],"categories":["Jupyter Notebook"],"sub_categories":[],"readme":"# Approximating Wasserstein distances with PyTorch\n\nRepository for the blog post on [Wasserstein distances](https://dfdazac.github.io/sinkhorn.html).\n\n***Update (July, 2019):*** I'm glad to see many people have found this post useful. Its main purpose is to introduce and illustrate the problem. To apply these ideas to large datasets and train on GPU, I highly recommend the \u003ca href=\"http://www.kernel-operations.io/geomloss/index.html\" target=\"_blank\"\u003eGeomLoss\u003c/a\u003e library, which is optimized for this.\n\n**Instructions**\n\nCreate a conda environment with all the requirements (edit `environment.yml` if you want to change the name of the environment):\n\n```sh\nconda env create -f environment.yml\n```\n\nActivate the environment\n\n```sh\nsource activate pytorch\n```\n\nOpen the notebook to reproduce the results:\n\n\n```sh\njupyter notebook\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdfdazac%2Fwassdistance","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdfdazac%2Fwassdistance","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdfdazac%2Fwassdistance/lists"}