{"id":26286926,"url":"https://github.com/LBANN/lbann","last_synced_at":"2025-03-14T21:01:15.824Z","repository":{"id":37548414,"uuid":"58576874","full_name":"LBANN/lbann","owner":"LBANN","description":"Livermore Big Artificial Neural Network Toolkit","archived":false,"fork":false,"pushed_at":"2024-10-18T00:53:43.000Z","size":32676,"stargazers_count":228,"open_issues_count":206,"forks_count":79,"subscribers_count":25,"default_branch":"develop","last_synced_at":"2025-02-12T12:03:13.858Z","etag":null,"topics":["artificial-intelligence","cpp","hpc","machine-learning","neural-network","performance","radiuss"],"latest_commit_sha":null,"homepage":"http://software.llnl.gov/lbann/","language":"C++","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/LBANN.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":".github/CODEOWNERS","security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2016-05-11T20:04:20.000Z","updated_at":"2025-02-01T18:21:14.000Z","dependencies_parsed_at":"2023-10-15T04:11:04.749Z","dependency_job_id":"d9211585-2396-4163-8e47-1079828f6339","html_url":"https://github.com/LBANN/lbann","commit_stats":null,"previous_names":["lbann/lbann","llnl/lbann"],"tags_count":25,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LBANN%2Flbann","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LBANN%2Flbann/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LBANN%2Flbann/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LBANN%2Flbann/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/LBANN","download_url":"https://codeload.github.com/LBANN/lbann/tar.gz/refs/heads/develop","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243646617,"owners_count":20324585,"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":["artificial-intelligence","cpp","hpc","machine-learning","neural-network","performance","radiuss"],"created_at":"2025-03-14T21:00:40.230Z","updated_at":"2025-03-14T21:01:15.765Z","avatar_url":"https://github.com/LBANN.png","language":"C++","readme":"# LBANN: Livermore Big Artificial Neural Network Toolkit\n\nThe Livermore Big Artificial Neural Network toolkit (LBANN) is an\nopen-source, HPC-centric, deep learning training framework that is\noptimized to compose multiple levels of parallelism.\n\nLBANN provides model-parallel acceleration through domain\ndecomposition to optimize for strong scaling of network training.  It\nalso allows for composition of model-parallelism with both data\nparallelism and ensemble training methods for training large neural\nnetworks with massive amounts of data.  LBANN is able to advantage of\ntightly-coupled accelerators, low-latency high-bandwidth networking,\nand high-bandwidth parallel file systems.\n\nLBANN supports state-of-the-art training algorithms such as\nunsupervised, self-supervised, and adversarial (GAN) training methods\nin addition to traditional supervised learning.  It also supports\nrecurrent neural networks via back propagation through time (BPTT)\ntraining, transfer learning, and multi-model and ensemble training\nmethods.\n\n\n## Building LBANN\nThe preferred method for LBANN users to install LBANN is to use\n[Spack](https://github.com/llnl/spack). After some system\nconfiguration, this should be as straightforward as\n\n```bash\nspack install lbann\n```\n\nMore detailed instructions for building and installing LBANN are\navailable at the [main LBANN\ndocumentation](https://lbann.readthedocs.io/en/latest/index.html).\n\n## Running LBANN\nThe basic template for running LBANN is\n\n```bash\n\u003cmpi-launcher\u003e \u003cmpi-options\u003e \\\n    lbann \u003clbann-options\u003e \\\n    --model=model.prototext \\\n    --optimizer=opt.prototext \\\n    --reader=data_reader.prototext\n```\n\nWhen using GPGPU accelerators, users should be aware that LBANN is\noptimized for the case in which one assigns one GPU per MPI\n*rank*. This should be borne in mind when choosing the parameters for\nthe MPI launcher.\n\nMore details about running LBANN are documented\n[here](https://lbann.readthedocs.io/en/latest/running_lbann.html).\n\n## Publications\n\nA list of publications, presentations and posters are shown\n[here](https://lbann.readthedocs.io/en/latest/publications.html).\n\n## Reporting issues\nIssues, questions, and bugs can be raised on the [Github issue\ntracker](https://github.com/llnl/lbann/issues).\n","funding_links":[],"categories":["C++"],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FLBANN%2Flbann","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FLBANN%2Flbann","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FLBANN%2Flbann/lists"}