{"id":18766050,"url":"https://github.com/akhilpandey95/gnnnas","last_synced_at":"2025-10-08T02:33:35.535Z","repository":{"id":201406809,"uuid":"492877641","full_name":"akhilpandey95/gnnNAS","owner":"akhilpandey95","description":"Work as part of ANL summer 2022 research with emphasis on utilizing symbolic programming to perform NAS on graph neural 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align=\"center\"\u003e\n  \u003cbr\u003e\n  \u003cimg src=\"https://github.com/akhilpandey95/gnnNAS/blob/main/images/gnnnas-logo.png?raw=true\"  width=\"400\" height=\"400\" alt=\"gnnnas logo\"/\u003e\n  \u003cbr\u003e\n\u003c/h1\u003e\n\n\u003ch2 align=\"center\"\u003eLibrary to write symbolic programs to generate expressive message passing neural networks\u003c/h2\u003e\n\n\u003ch1 align=\"center\"\u003e\u003ca href=\"https://akhilpandey95.github.io/gnnNAS/\"\u003ekgforge Documentation\u003c/h1\u003e\n\n\u003cp align=\"center\"\u003e\n\n   \u003ca alt=\"Tests\" href=\"https://github.com/akhilpandey95/gnnNAS/actions/workflows/publish-to-pypi.yml/badge.svg\"\u003e\n      \u003cimg src=\"https://github.com/akhilpandey95/gnnNAS/actions/workflows/publish-to-pypi.yml/badge.svg\"\u003e\n  \u003c/a\u003e\n  \u003cimg alt=\"Code Style\" src=\"https://img.shields.io/badge/  code%20style-black-000000.svg\" /\u003e\n  \u003ca href=\"https://www.python.org/downloads/release/python-390/\"\u003e\n  \u003c/a\u003e\n  \u003ca href=\"https://pypi.org/project/gnnNAS\" target=\"_blank\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/python-3.10-blue.svg\" alt=\"Supported Python versions\"\u003e\n  \u003c/a\u003e\n  \u003ca href=\"https://img.shields.io/pypi/dw/gnnnas\" target=\"_blank\"\u003e\n    \u003cimg src=\"https://img.shields.io/pypi/dw/gnnnas\" alt=\"Downloads per week\"\u003e\n  \u003c/a\u003e\n  \u003ca href=\"https://img.shields.io/badge/License-MIT-yellow.svg\" target=\"_blank\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/License-MIT-yellow.svg\" alt=\"License\"\u003e\n  \u003c/a\u003e\n  \u003ca href=\"https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square\" target=\"_blank\"\u003e\n    \u003cimg src=\"https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square\" alt=\"Contribute\"\u003e\n  \u003c/a\u003e\n\n\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\n  \u003ca href=\"#what-is-it\"\u003eWhat is it?\u003c/a\u003e •\n  \u003ca href=\"#features\"\u003eFeatures\u003c/a\u003e •\n  \u003ca href=\"#installation\"\u003eInstallation\u003c/a\u003e •\n  \u003ca href=\"#usage\"\u003eUsage\u003c/a\u003e  •\n  \u003ca href=\"#contributing\"\u003eContributing\u003c/a\u003e\n\u003c/p\u003e\n\n## What is it?\n`kgforge` is a library which automates the generation of knowledge graphs from scholarly text.\n\n## Features:\n  - **TODO**: TODO: Description.\n\n## Installation:\n\n### Poetry\n\n```bash\npoetry add kgforge\n```\n\n### Pip\n\n```bash\npip install kgforge\n```\n\nSetup your local environment:\n\nAny necessary environment variables description:\n\n\n```shell\nexport SAMPLE_ENV_VARIABLE=${VALUE}\n```\n\n## Usage\n\nNow that `kgforge` is installed, you're ready to start using it!\n\nIt's time to point you to the official [Documentation Website](https://akhilpandey95.github.io/gnnNAS/) for more information on how to use `kgforge`\n\n\n## Contributing\nIf you'd like to contribute, be sure to check out our [contributing guide](./CONTRIBUTING.md)! If you'd like to work on any outstanding items, check out the `roadmap` section in the docs and get started :smiley:\n\nThanks goes to these incredible people.\n\n\u003ca href=\"https://github.com/akhilpandey95/gnnNAS/graphs/contributors\"\u003e\n  \u003cimg style=\"border-radius: 50%\" src=\"https://contrib.rocks/image?repo=akhilpandey95/gnnNAS\" /\u003e\n\u003c/a\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fakhilpandey95%2Fgnnnas","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fakhilpandey95%2Fgnnnas","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fakhilpandey95%2Fgnnnas/lists"}