{"data":{"slug":"ml6team-fondant","name":"fondant","tagline":"Production-ready data processing made easy and shareable","github_url":"https://github.com/ml6team/fondant","owner":"ml6team","repo":"fondant","owner_avatar_url":"https://avatars.githubusercontent.com/u/36703417?v=4","primary_language":"Python","stars":359,"forks":28,"topics":["data-processing","fine-tuning","foundation-models","machine-learning","pipeline","python"],"archived":false,"github_pushed_at":"2026-02-20T07:28:05+00:00","maintenance_label":"Slowing","stars_delta_30d":1,"url":"https://www.graphcanon.com/tools/ml6team-fondant","markdown_url":"https://www.graphcanon.com/tools/ml6team-fondant.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/ml6team-fondant","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=ml6team-fondant","description":"Production-ready data processing made easy and shareable","homepage_url":"https://fondant.ai/en/stable/","license":"Apache-2.0","open_issues":57,"watchers":6,"ai_summary":"A Python-based tool for creating pipelines to process data efficiently, designed with sharing in mind, facilitating workflows involving fine-tuning and foundation models.","readme_excerpt":"## 💨 Getting Started\n\nFondant allows you to easily define workflows comprised of both reusable and custom components. The following example uses the reusable `load_from_hf_hub component` to load a dataset from the Hugging Face Hub and process it using a custom component that will resize the images resulting in a new dataset.\n\n\n```pipeline.py\nimport pyarrow as pa\n\nfrom fondant.dataset import Dataset\n\n---\n\n## ⚒️ Installation\n\nFirst, run the basic Fondant installation:\n\n```\npip install fondant\n```\n\nFondant also includes extra dependencies for specific runners, storage integrations and publishing \ncomponents to registries. The dependencies for the local runner (docker) is included by default.\n\nFor more detailed installation options, check the [**installation page**](https://fondant.ai/en/latest/guides/installation/)on our documentation.","github_created_at":"2023-03-02T08:55:48+00:00","created_at":"2026-07-11T11:42:04.519999+00:00","updated_at":"2026-08-24T12:01:24.923173+00:00","categories":[{"slug":"data-retrieval","name":"Data & Retrieval","url":"https://www.graphcanon.com/categories/data-retrieval","markdown_url":"https://www.graphcanon.com/categories/data-retrieval.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/data-retrieval"},{"slug":"model-training","name":"Model Training","url":"https://www.graphcanon.com/categories/model-training","markdown_url":"https://www.graphcanon.com/categories/model-training.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/model-training"}],"tags":[{"slug":"data-processing","name":"data-processing"},{"slug":"fine-tuning","name":"fine-tuning"},{"slug":"foundation-models","name":"foundation-models"},{"slug":"machine-learning","name":"machine-learning"},{"slug":"pipeline","name":"pipeline"},{"slug":"python","name":"python"}],"trust":{"provenance":{"is_fork":false,"github_id":608551889,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-24T12:01:24.084Z","maintenance":{"label":"Slowing","score":36,"methodology":"github_public_v1","releases_90d":0,"days_since_push":185,"last_release_at":"2024-04-22T10:56:22Z","stars_delta_30d":1,"open_issues_delta_30d":0},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T11:42:05.757Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-24T12:01:24.625Z"},"has_cli":{"value":true,"source":"pyproject.toml:[project.scripts]","observed_at":"2026-08-24T12:01:24.625Z"},"languages":{"value":["python"],"source":"github.language+pyproject.toml","observed_at":"2026-08-24T12:01:24.625Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-08-24T12:01:24.625Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["When you require a tool that simplifies the creation of machine-learning data pipelines and supports community sharing.","Suitable if your workflow includes fine-tuning existing large foundation models."],"when_not_to_use":["Avoid using Fondant if you prefer tools without Python-centric integration or seek non-sharing-friendly development environments.","Not recommended for workflows that do not involve machine learning data processing or large model training."],"source":"enrich:decision_facts","observed_at":"2026-07-15T09:27:22.569Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"Fondant is designed for Python users needing to create efficient data pipelines for processing, fine-tuning ML models, sharing these workflows."}]}}