{"id":24489996,"url":"https://github.com/rcghpge/pymo","last_synced_at":"2025-03-15T01:23:49.597Z","repository":{"id":273221733,"uuid":"919029129","full_name":"rcghpge/pymo","owner":"rcghpge","description":"A framework in Mojo for AI/ML/DL applications and other domains.","archived":false,"fork":false,"pushed_at":"2025-02-14T09:39:11.000Z","size":5699,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-14T10:32:46.491Z","etag":null,"topics":["deep-learning","deep-neural-networks","machine-learning","machine-learning-models","magic-cli","max-gpu","modular","mojo","mojo-language","numpy","pandas","pymo","python","python-language","scikit-learn"],"latest_commit_sha":null,"homepage":"","language":"Mojo","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/rcghpge.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":".github/FUNDING.yml","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},"funding":{"github":"rcghpge","patreon":null,"open_collective":null,"ko_fi":null,"tidelift":null,"community_bridge":null,"liberapay":null,"issuehunt":null,"lfx_crowdfunding":null,"polar":null,"buy_me_a_coffee":null,"thanks_dev":null,"custom":null}},"created_at":"2025-01-19T14:30:50.000Z","updated_at":"2025-02-14T09:39:15.000Z","dependencies_parsed_at":"2025-02-04T09:27:34.594Z","dependency_job_id":"a731df24-e7fb-44e5-a2ad-36915e751f68","html_url":"https://github.com/rcghpge/pymo","commit_stats":null,"previous_names":["rcghpge/pymo"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rcghpge%2Fpymo","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rcghpge%2Fpymo/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rcghpge%2Fpymo/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rcghpge%2Fpymo/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/rcghpge","download_url":"https://codeload.github.com/rcghpge/pymo/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243669215,"owners_count":20328219,"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","deep-neural-networks","machine-learning","machine-learning-models","magic-cli","max-gpu","modular","mojo","mojo-language","numpy","pandas","pymo","python","python-language","scikit-learn"],"created_at":"2025-01-21T17:16:45.686Z","updated_at":"2025-03-15T01:23:49.590Z","avatar_url":"https://github.com/rcghpge.png","language":"Mojo","funding_links":["https://github.com/sponsors/rcghpge"],"categories":[],"sub_categories":[],"readme":"# PyMo\n\nA framework in Mojo for AI/ML/DL applications and other domains.\n\n## An extended framework for Mojo:\n- The [Mojo🔥](https://www.modular.com/mojo)programming language\n- Mojo is built by Modular Inc. and the Mojo community.\n\n## Overview\nPyMo is a framework designed to leverage the capabilities of the Mojo programming language. The goal is to \nprovide a seamless transition for Python developers into the realm of AI, Machine Learning, Deep Learning \n(AI/ML/DL), and other domains - with the performance enhancements offered by Mojo. Since Mojo is designed as a \nsystems programming language, its capabilities go beyond AI/ML/DL. This extends to PyMo as well - it can\nbe leveraged in other programming languages and domains. See `docs` for more information.\n\n\n## Project Structure\n```\n├── LICENSE\n├── README.md\n├── __inti__.🔥\n├── data\n├── docs\n├── examples\n├── hello.mojo\n├── magic.lock\n├── mojoproject.toml\n├── pymo\n├── sandbox\n├── .github\n├── test\n└── test_pymo.mojo\n```\n\n## Installation \u0026 Setup\nTo install and set up PyMo (on Linux), follow these steps:\n\n**Install Magic Build System \u0026 Package Manager**\n- If your machine does not have Modular's Magic software, you may need to install it on your local machine.\n- See Magic documentation for installation instructions: [Magic docs](https://docs.modular.com/magic/).\n\n**Clone the repository**\n   ```bash\n   git clone git@github.com:rcghpge/pymo.git\n   cd pymo\n   ```\n**Initialize PyMo environment with Magic**\n- Add dependencies (optional)\n   ```bash\n   # Initialize PyMo\n   magic shell\n\n   # Add additional dependencies\n   magic add xgboost\n   ```\n**Run Test(s)**\n   ```bash\n   # Workflow/Development environment\n   magic run test\n   mojo test\n\n   # Test individual Mojo source code at the file level\n   mojo -I . hello.mojo  \n\n   # Initialize debugging session\n   mojo test --debug\n\n   # Initialize REPL environment\n   mojo repl\n   ```\n**Initialize a Jupyter Notebook Environment (Optional)**\n   ```bash\n   magic run jupyter notebook\n   ```\n**Format Project's Code (Optional)**\n   ```bash\n   magic run format\n   ```\n**Build \u0026 Ship Package(s) and Module(s) in Mojo (Optional)**\n   ```bash\n   magic run build\n   ```\n## Features\n### Interoperability with Python:\nPyMo showcases Mojo's potential to become a superset of Python, focusing on enhancing Python's AI/ML/DL capabilities\nwhile providing a glimpse into Mojo's language and domain-agnostic potential.\n\n### Integration with Popular Libraries:\nHere's how PyMo interacts with some well-known Python libraries and frameworks:\n- **scikit-learn:** For machine learning models.\n- **pandas:** For data manipulation and analysis.\n- **numpy:** For numerical computing.\n- **seaborn:** For statistical data visualization.\n- **matplotlib:** For plotting visualizations.\n- **pytest** Testing framework for Python.\n- and more.\n\n### Getting Started\nCheck out `examples/` and `test/` directories for sample code demonstrating how to use PyMo for building out packages and modules in Mojo and AI/ML/DL methods. The `test/` directory is for port testing ecosystems and the `sandbox/` directory is for ecosystem development eg libraries, Mojo, packages, modules etc.\n\n**MAX**\n- check out [MAX](https://www.modular.com/max) - an AI inference platform built by Modular\n- PyMo can be leveraged in this framework.\n\n**Python**\n- If you are new to programming, check out [PyPi](https://pypi.org) - The Python Package Index. A repository of software for the Python programming language and also see [Python](https://docs.python.org/3/) documentation. Python is an awesome programming language to get started with in programming.\n\n**Machine Learning University**\n- [MLU-Explain](https://mlu-explain.github.io) - Visual explanations of core machine learning concepts\n\n## Contributing\nContributions are welcome! Here's how you can contribute:\n\n- If you are interested to build out this project. Feel free to contact me. See contact below.\n- Join the discussion. See repository's Discussions board.\n- Report bugs or request features by opening an issue.\n- Fix bugs or implement features by opening a pull request.\n- Please ensure your code adheres to the project's and Mojo's coding standards before submission.\n\n## License\n\nThe PyMo project is primarily licensed under the [MIT License](./LICENSE).\n\nAdditionally, PyMo also incorporates components, each governed by its respective license:\n\n- **LLVM**: Components utilized from LLVM are licensed under the Apache License v2.0 with LLVM Exceptions. See the LLVM [License](https://llvm.org/LICENSE.txt).\n\n- **MAX and Mojo**: Usage and distribution are licensed under the [MAX \u0026 Mojo Community License](https://www.modular.com/legal/max-mojo-license).\n\n## Acknowledgements\n- The Mojo and Python community.\n- The open source community.\n\n## Contact\nFor any inquiries or further information, you can find my email in contacts [here](https://robertcocker.com).\n\n---\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frcghpge%2Fpymo","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frcghpge%2Fpymo","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frcghpge%2Fpymo/lists"}