{"id":15654949,"url":"https://github.com/milescranmer/gnn_resource_allocation","last_synced_at":"2025-05-01T13:11:31.920Z","repository":{"id":86181236,"uuid":"365403860","full_name":"MilesCranmer/gnn_resource_allocation","owner":"MilesCranmer","description":"Code for our paper on doing resource allocation with graph neural networks","archived":false,"fork":false,"pushed_at":"2021-06-24T09:03:54.000Z","size":8510,"stargazers_count":31,"open_issues_count":1,"forks_count":2,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-05-01T12:48:11.061Z","etag":null,"topics":["astronomy","astrophysics","cosmology","deep-learning","graph-neural-networks","inference","machine-learning","neural-network","pytorch","resource-allocation"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/MilesCranmer.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.txt","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}},"created_at":"2021-05-08T02:49:53.000Z","updated_at":"2025-04-24T01:56:05.000Z","dependencies_parsed_at":"2023-05-05T07:32:22.311Z","dependency_job_id":null,"html_url":"https://github.com/MilesCranmer/gnn_resource_allocation","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/MilesCranmer%2Fgnn_resource_allocation","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MilesCranmer%2Fgnn_resource_allocation/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MilesCranmer%2Fgnn_resource_allocation/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MilesCranmer%2Fgnn_resource_allocation/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/MilesCranmer","download_url":"https://codeload.github.com/MilesCranmer/gnn_resource_allocation/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":251879117,"owners_count":21658690,"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":["astronomy","astrophysics","cosmology","deep-learning","graph-neural-networks","inference","machine-learning","neural-network","pytorch","resource-allocation"],"created_at":"2024-10-03T12:55:13.583Z","updated_at":"2025-05-01T13:11:31.896Z","avatar_url":"https://github.com/MilesCranmer.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Unsupervised Resource Allocation with Graph Neural Networks\n\n![](https://github.com/MilesCranmer/gnn_resource_allocation/blob/master/schematic.svg)\n\nCheck out the paper [here](https://arxiv.org/abs/2106.09761).\n\nPyTorch code for the forward model of our algorithm can be found in this repository in the file `model.py`. To train the model, execute `train.py`.\n\nData required to train this model can be found [here](https://app.globus.org/file-manager?origin_id=75a68b36-a6c0-11eb-92d8-6b08dd67ff48\u0026origin_path=%2F)\n\nRequirements for our codebase can be found in `environment.yml`. Note that one needs to use the following custom astropy:\n```\npip install git+https://github.com/MilesCranmer/astropy\n```\n(it has some of the Cosmology calculations vectorized).\n\nIf you are using `conda`, and have CUDA version 11.0 and cuDNN version 8.0, you can create a duplicate of our env, using:\n```bash\n./create_env.sh gnn_allocation\n```\nwhich will create a new environment called `gnn_allocation`. This uses PyTorch 1.7.1, though it is likely to work for other versions if you decide to modify `create_env.sh` and `environment.yml`. You can also use an implementation without CUDA using the `environment_nocuda.yml` file.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmilescranmer%2Fgnn_resource_allocation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmilescranmer%2Fgnn_resource_allocation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmilescranmer%2Fgnn_resource_allocation/lists"}