{"id":15922133,"url":"https://github.com/jrhea/anomlee","last_synced_at":"2025-06-29T13:35:38.530Z","repository":{"id":37614899,"uuid":"226228553","full_name":"jrhea/anomlee","owner":"jrhea","description":"ANOMLEE: A Neat-O ML EE (pronounced a·nom·a·ly)","archived":false,"fork":false,"pushed_at":"2022-12-08T03:19:18.000Z","size":68,"stargazers_count":8,"open_issues_count":8,"forks_count":1,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-04-04T22:11:15.757Z","etag":null,"topics":["c","ethereum","machine-learning","python","rust","wasm"],"latest_commit_sha":null,"homepage":"anomlee.vercel.app","language":"Jupyter Notebook","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/jrhea.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":"2019-12-06T02:22:08.000Z","updated_at":"2023-03-09T10:58:16.000Z","dependencies_parsed_at":"2023-01-25T03:45:54.702Z","dependency_job_id":null,"html_url":"https://github.com/jrhea/anomlee","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/jrhea/anomlee","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jrhea%2Fanomlee","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jrhea%2Fanomlee/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jrhea%2Fanomlee/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jrhea%2Fanomlee/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jrhea","download_url":"https://codeload.github.com/jrhea/anomlee/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jrhea%2Fanomlee/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":260268623,"owners_count":22983601,"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":["c","ethereum","machine-learning","python","rust","wasm"],"created_at":"2024-10-06T20:05:21.141Z","updated_at":"2025-06-17T00:35:00.694Z","avatar_url":"https://github.com/jrhea.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"## ANOMLEE: A Neat-O Machine Learning Execution Environment\n\n:muscle: by Quilt's [ewasm-rt](https://github.com/quilt/ewasm-rt)\n\nThis repo demonstrates how a trained ML model could run inside an Ethereum 2 EE.\n\n## Prerequisits\n\nInstall LLVM\n\n```bash\nbrew install llvm\necho 'export PATH=\"/usr/local/opt/llvm/bin:$PATH\"' \u003e\u003e ~/.bash_profile\n```\n\nInstall the WebAssembly Binary Toolkit\n\n```bash\nbrew install wabt\n```\n\nInstall Python Environment \u0026 Dependencies\n\n```\npython3 -m venv env\nsource ./env/bin/activate\npip3 install -r requirements.txt\n```\n\n## Build \n\n```bash\nmake all\n```\n\n## Random Forest Classifier (iris model)\n\nThis model that is trained to determine which of the following types of iris flowers:\n\n- setosa\n- versicolor\n- virginica\n\n based on the following design variables:\n\n- sepal length (cm)\t\n- sepal width (cm)\t\n- petal length (cm)\t\n- petal width (cm)\n\n**Benchmarks:**\n- **Python:** 103307 microseconds\n- **eWasm:** 551.849 microseconds\n- **C:** 1 microsecond\n\n**Binary size:**\n- 826 bytes\n\n## Handwritten Digit Classifier (digit model)\n\nThis model uses a sequential neural network model to classify handwritten digits from the MINST database\n\n\u003e TODO: finish\n\n## Run Benchmarks (all models)\n\n```bash\n$ make benchmark\n########## Python Benchmark: ###########\n\nExecution time: 103307 microseconds.\n\n\n 0    setosa\nName: species, dtype: category\nCategories (3, object): [setosa, versicolor, virginica] \n\n########## eWasm Benchmark: ###########\n    Finished release [optimized] target(s) in 0.03s\n     Running target/release/deps/anomlee-0de6000a8c14c88e\n\nrunning 1 test\n\nExecution Time: 551.849µs\n\ntest tests::test ... ok\n\ntest result: ok. 1 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out\n\n########## C Benchmark: ##########\n\nExecution time: 1 microseconds\n\n\nProbabilities: \n1.000000 0.000000 0.000000 \n\nModel Predicts: \nsetosa    \n\n```\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjrhea%2Fanomlee","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjrhea%2Fanomlee","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjrhea%2Fanomlee/lists"}