{"id":13574240,"url":"https://github.com/pykeio/ort","last_synced_at":"2025-10-19T06:27:50.021Z","repository":{"id":63822496,"uuid":"570985989","full_name":"pykeio/ort","owner":"pykeio","description":"Fast ML inference \u0026 training for ONNX models in Rust","archived":false,"fork":false,"pushed_at":"2025-04-16T12:48:27.000Z","size":5093,"stargazers_count":1254,"open_issues_count":2,"forks_count":124,"subscribers_count":12,"default_branch":"main","last_synced_at":"2025-04-17T01:27:16.013Z","etag":null,"topics":["ai","ai-training","fine-tuning","inference","machine-learning","onnx","onnxruntime","rust"],"latest_commit_sha":null,"homepage":"https://ort.pyke.io/","language":"Rust","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/pykeio.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":".github/FUNDING.yml","license":"LICENSE-APACHE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null},"funding":{"open_collective":"pyke-osai"}},"created_at":"2022-11-26T19:26:15.000Z","updated_at":"2025-04-16T12:48:32.000Z","dependencies_parsed_at":"2022-11-26T22:53:13.119Z","dependency_job_id":"d8a9ffd9-f952-4909-ba86-c62f584bd4ab","html_url":"https://github.com/pykeio/ort","commit_stats":{"total_commits":484,"total_committers":38,"mean_commits":"12.736842105263158","dds":0.1074380165289256,"last_synced_commit":"4dd5ff73a0ccf95e01e2fa627ac7cf27192927d4"},"previous_names":[],"tags_count":30,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pykeio%2Fort","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pykeio%2Fort/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pykeio%2Fort/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pykeio%2Fort/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/pykeio","download_url":"https://codeload.github.com/pykeio/ort/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":249990402,"owners_count":21357067,"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":["ai","ai-training","fine-tuning","inference","machine-learning","onnx","onnxruntime","rust"],"created_at":"2024-08-01T15:00:48.572Z","updated_at":"2025-10-19T06:27:50.016Z","avatar_url":"https://github.com/pykeio.png","language":"Rust","funding_links":["https://opencollective.com/pyke-osai"],"categories":["Rust","Table of Contents","Machine Learning","Lighter and Deployment Frameworks","Neural Networks","Model Inference"],"sub_categories":["AI - Machine Learning"],"readme":"\u003cdiv align=center\u003e\n    \u003cimg src=\"https://parcel.pyke.io/v2/cdn/assetdelivery/ortrsv2/docs/trend-banner.png\" width=\"350px\"\u003e\n    \u003chr /\u003e\n    \u003ca href=\"https://app.codecov.io/gh/pykeio/ort\" target=\"_blank\"\u003e\u003cimg alt=\"Coverage Results\" src=\"https://img.shields.io/codecov/c/gh/pykeio/ort?style=for-the-badge\"\u003e\u003c/a\u003e \u003ca href=\"https://crates.io/crates/ort\" target=\"_blank\"\u003e\u003cimg alt=\"Crates.io\" src=\"https://img.shields.io/crates/d/ort?style=for-the-badge\"\u003e\u003c/a\u003e \u003ca href=\"https://opencollective.com/pyke-osai\" target=\"_blank\"\u003e\u003cimg alt=\"Open Collective backers and sponsors\" src=\"https://img.shields.io/opencollective/all/pyke-osai?style=for-the-badge\u0026label=sponsors\"\u003e\u003c/a\u003e\n    \u003cbr /\u003e\n    \u003ca href=\"https://crates.io/crates/ort\" target=\"_blank\"\u003e\u003cimg alt=\"Crates.io\" src=\"https://img.shields.io/crates/v/ort?style=for-the-badge\u0026label=ort\u0026logo=rust\"\u003e\u003c/a\u003e \u003cimg alt=\"ONNX Runtime\" src=\"https://img.shields.io/badge/onnxruntime-v1.23.1-blue?style=for-the-badge\u0026logo=cplusplus\"\u003e\n\u003c/div\u003e\n\n`ort` is a Rust interface for performing hardware-accelerated inference \u0026 training on machine learning models in the [Open Neural Network Exchange](https://onnx.ai/) (ONNX) format.\n\nBased on the now-inactive [`onnxruntime-rs`](https://github.com/nbigaouette/onnxruntime-rs) crate, `ort` is primarily a wrapper for Microsoft's [ONNX Runtime](https://onnxruntime.ai/) library, but offers support for [other pure-Rust runtimes](https://ort.pyke.io/backends).\n\n`ort` with ONNX Runtime is super quick - and it supports almost [any hardware accelerator](https://ort.pyke.io/perf/execution-providers) you can think of. Even still, it's light enough to run on your users' devices.\n\nWhen you need to deploy a PyTorch/TensorFlow/Keras/scikit-learn/PaddlePaddle model either on-device or in the datacenter, `ort` has you covered.\n\n## 📖 Documentation\n- [Guide](https://ort.pyke.io/)\n- [API reference](https://docs.rs/ort/2.0.0-rc.10/ort/)\n- [Examples](https://github.com/pykeio/ort/tree/main/examples)\n- [Migrating from v1.x to v2.0](https://ort.pyke.io/migrating/v2)\n\n## 🤔 Support\n- [Discord: `#💬｜ort-discussions`](https://discord.gg/uQtsNu2xMa)\n- [GitHub Discussions](https://github.com/pykeio/ort/discussions)\n- [Email](mailto:contact@pyke.io)\n\n## 🌠 Sponsor `ort`\n\u003ca href=\"https://opencollective.com/pyke-osai\"\u003e\n\u003cimg src=\"https://opencollective.com/pyke-osai/sponsors.svg\" height=\"64\" /\u003e\n\u003cbr /\u003e\n\u003cimg src=\"https://opencollective.com/pyke-osai/backers.svg\" height=\"64\" /\u003e\n\u003c/a\u003e\n\n## 💖 FOSS projects using `ort`\n\u003csub\u003e[Open a PR](https://github.com/pykeio/ort/pulls) to add your project here 🌟\u003c/sub\u003e\n\n- **[edge-transformers](https://github.com/npc-engine/edge-transformers)** uses `ort` for accelerated transformer model inference at the edge.\n- **[Ortex](https://github.com/relaypro-open/ortex)** uses `ort` for safe ONNX Runtime bindings in Elixir.\n- **[Lantern](https://github.com/lanterndata/lantern_extras)** uses `ort` to provide embedding model inference inside Postgres.\n- **[Magika](https://github.com/google/magika)** uses `ort` for content type detection.\n- **[`sbv2-api`](https://github.com/neodyland/sbv2-api)** is a fast implementation of Style-BERT-VITS2 text-to-speech using `ort`.\n- **[Ahnlich](https://github.com/deven96/ahnlich)** uses `ort` to power their AI proxy for semantic search applications.\n- **[Spacedrive](https://github.com/spacedriveapp/spacedrive)** is a cross-platform file manager with AI features powered by `ort`.\n- **[BoquilaHUB](https://github.com/boquila/boquilahub/)** uses `ort` for local AI deployment in biodiversity conservation efforts.\n- **[`FastEmbed-rs`](https://github.com/Anush008/fastembed-rs)** uses `ort` for generating vector embeddings, reranking locally.\n- **[Valentinus](https://github.com/kn0sys/valentinus)** uses `ort` to provide embedding model inference inside LMDB.\n- **[retto](https://github.com/NekoImageLand/retto)** uses `ort` for reliable, fast ONNX inference of PaddleOCR models on Desktop and WASM platforms.\n- **[oar-ocr](https://github.com/GreatV/oar-ocr)** A comprehensive OCR library, built in Rust with `ort` for efficient inference.\n- **[Text Embeddings Inference (TEI)](https://github.com/huggingface/text-embeddings-inference)** uses `ort` to deliver high-performance ONNX runtime inference for text embedding models.\n- **[Flow-Like](https://github.com/TM9657/flow-like)** uses `ort` to enable local ML inference inside its typed workflow engine.\n- **[CamTrap Detector](https://github.com/bencevans/camtrap-detector)** uses `ort` to detect animals, humans and vehicles in trail camera imagery\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpykeio%2Fort","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpykeio%2Fort","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpykeio%2Fort/lists"}