[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-70934":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":16,"stars7d":16,"stars30d":16,"stars90d":16,"forks30d":16,"starsTrendScore":16,"compositeScore":17,"rankGlobal":10,"rankLanguage":10,"license":18,"archived":19,"fork":19,"defaultBranch":20,"hasWiki":21,"hasPages":19,"topics":22,"createdAt":10,"pushedAt":10,"updatedAt":28,"readmeContent":29,"aiSummary":30,"trendingCount":16,"starSnapshotCount":16,"syncStatus":31,"lastSyncTime":32,"discoverSource":33},70934,"ivy","unifyai\u002Fivy","unifyai","Convert Machine Learning Code Between Frameworks","https:\u002F\u002Fivy-llc.github.io\u002Fdocs\u002F",null,"Python",14178,5498,63,945,0,70,"Other",false,"main",true,[23,24,25,26,27],"jax","numpy","python","pytorch","tensorflow","2026-06-12 04:00:58","\u003Cdiv style=\"display: block;\" align=\"center\">\r\n    \u003Ca href=\"https:\u002F\u002Fivy-llc.github.io\u002F\u002F\">\r\n        \u003Cimg class=\"dark-light\" width=\"50%\" src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fivy-llc\u002Fassets\u002Frefs\u002Fheads\u002Fmain\u002Fassets\u002Flogos\u002Fivy-long.svg\"\u002F>\r\n    \u003C\u002Fa>\r\n\u003C\u002Fdiv>\r\n\u003Cbr clear=\"all\" \u002F>\r\n\r\n\u003Cdiv style=\"margin-top: 10px; margin-bottom: 10px; display: block;\" align=\"center\">\r\n    \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fivy-llc\u002Fivy\u002Fstargazers\">\r\n        \u003Cimg class=\"dark-light\" style=\"padding-right: 4px; padding-bottom: 4px;\" src=\"https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Fstars\u002Fivy-llc\u002Fivy\">\r\n    \u003C\u002Fa>\r\n    \u003Ca href=\"https:\u002F\u002Fdiscord.gg\u002FuYRmyPxMQq\">\r\n        \u003Cimg class=\"dark-light\" style=\"padding-right: 4px; padding-bottom: 4px;\" src=\"https:\u002F\u002Fimg.shields.io\u002Fdiscord\u002F1220325004013604945?color=blue&label=%20&logo=discord&logoColor=white\">\r\n    \u003C\u002Fa>\r\n    \u003Ca href=\"https:\u002F\u002Fivy-llc.github.io\u002Fdocs\u002F\">\r\n        \u003Cimg class=\"dark-light\" style=\"padding-right: 4px; padding-bottom: 4px;\" src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fdocs-purple\">\r\n    \u003C\u002Fa>\r\n    \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fivy-llc\u002Fivy\u002Factions\u002Fworkflows\u002Ftest-transpiler.yml\">\r\n        \u003Cimg class=\"dark-light\" style=\"padding-right: 4px; padding-bottom: 4px;\" src=\"https:\u002F\u002Fgithub.com\u002Fivy-llc\u002Fivy\u002Factions\u002Fworkflows\u002Ftest-transpiler.yml\u002Fbadge.svg\">\r\n    \u003C\u002Fa>\r\n    \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fivy-llc\u002Fivy\u002Factions\u002Fworkflows\u002Fintegration-tests.yml\">\r\n        \u003Cimg class=\"dark-light\" style=\"padding-right: 4px; padding-bottom: 4px;\" src=\"https:\u002F\u002Fgithub.com\u002Fivy-llc\u002Fivy\u002Factions\u002Fworkflows\u002Fintegration-tests.yml\u002Fbadge.svg\">\r\n    \u003C\u002Fa>\r\n\u003C\u002Fdiv>\r\n\u003Cbr clear=\"all\" \u002F>\r\n\r\n\r\n# Convert Machine Learning Code Between Frameworks\r\n\r\nIvy enables you to convert ML models, tools and libraries between frameworks using `ivy.transpile`\r\n\r\n\u003Cdiv style=\"display: block;\" align=\"center\">\r\n    \u003Cdiv>\r\n    \u003Ca href=\"https:\u002F\u002Fjax.readthedocs.io\">\r\n        \u003Cimg class=\"dark-light\" width=\"100\" height=\"100\" src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fivy-llc\u002Fassets\u002Frefs\u002Fheads\u002Fmain\u002Fassets\u002Flogos\u002Fjax.svg\">\r\n    \u003C\u002Fa>\r\n    \u003Cimg class=\"dark-light\" width=\"5%\" src=\"https:\u002F\u002Fgithub.com\u002Fivy-llc\u002Fassets\u002Fblob\u002Fmain\u002Fassets\u002Fempty.png?raw=true\">\r\n    \u003Cimg class=\"dark-light\" width=\"5%\" src=\"https:\u002F\u002Fgithub.com\u002Fivy-llc\u002Fassets\u002Fblob\u002Fmain\u002Fassets\u002Fempty.png?raw=true\">\r\n    \u003Ca href=\"https:\u002F\u002Fwww.tensorflow.org\">\r\n        \u003Cimg class=\"dark-light\" width=\"100\" height=\"100\" src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fivy-llc\u002Fassets\u002Frefs\u002Fheads\u002Fmain\u002Fassets\u002Flogos\u002Ftensorflow.svg\">\r\n    \u003C\u002Fa>\r\n    \u003Cimg class=\"dark-light\" width=\"5%\" src=\"https:\u002F\u002Fgithub.com\u002Fivy-llc\u002Fassets\u002Fblob\u002Fmain\u002Fassets\u002Fempty.png?raw=true\">\r\n    \u003Cimg class=\"dark-light\" width=\"5%\" src=\"https:\u002F\u002Fgithub.com\u002Fivy-llc\u002Fassets\u002Fblob\u002Fmain\u002Fassets\u002Fempty.png?raw=true\">\r\n    \u003Ca href=\"https:\u002F\u002Fpytorch.org\">\r\n        \u003Cimg class=\"dark-light\" width=\"100\" height=\"100\" src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fivy-llc\u002Fassets\u002Frefs\u002Fheads\u002Fmain\u002Fassets\u002Flogos\u002Fpytorch.svg\">\r\n    \u003C\u002Fa>\r\n    \u003Cimg class=\"dark-light\" width=\"5%\" src=\"https:\u002F\u002Fgithub.com\u002Fivy-llc\u002Fassets\u002Fblob\u002Fmain\u002Fassets\u002Fempty.png?raw=true\">\r\n    \u003Cimg class=\"dark-light\" width=\"5%\" src=\"https:\u002F\u002Fgithub.com\u002Fivy-llc\u002Fassets\u002Fblob\u002Fmain\u002Fassets\u002Fempty.png?raw=true\">\r\n    \u003Ca href=\"https:\u002F\u002Fnumpy.org\">\r\n        \u003Cimg class=\"dark-light\" width=\"100\" height=\"100\" src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fivy-llc\u002Fassets\u002Frefs\u002Fheads\u002Fmain\u002Fassets\u002Flogos\u002Fnumpy.svg\">\r\n    \u003C\u002Fa>\r\n    \u003C\u002Fdiv>\r\n\u003C\u002Fdiv>\r\n\r\n\u003Cbr clear=\"all\" \u002F>\r\n\r\n# Installation\r\n\r\nThe easiest way to install Ivy is using **pip**:\r\n\r\n``` bash\r\npip install ivy\r\n```\r\n\r\n\u003Cdetails>\r\n\u003Csummary>\u003Cb>From Source\u003C\u002Fb>\u003C\u002Fsummary>\r\n\u003Cbr clear=\"all\" \u002F>\r\n\r\nYou can also install Ivy from source if you want to take advantage of\r\nthe latest changes:\r\n\r\n``` bash\r\ngit clone https:\u002F\u002Fgithub.com\u002Fivy-llc\u002Fivy.git\r\ncd ivy\r\npip install --user -e .\r\n```\r\n\r\n\u003C\u002Fdetails>\r\n\r\n\u003Cbr clear=\"all\" \u002F>\r\n\r\n# Supported Frameworks\r\n\r\nThese are the frameworks that `ivy.transpile` currently supports conversions from and to.\r\n\r\n| Framework  | Source | Target |\r\n|------------|:------:|:------:|\r\n| PyTorch    |   ✅   |   🚧   |\r\n| TensorFlow |   🚧   |   ✅   |\r\n| JAX        |   🚧   |   ✅   |\r\n| NumPy      |   🚧   |   ✅   |\r\n\r\n\u003Cbr clear=\"all\" \u002F>\r\n\r\n# Using ivy\r\n\r\nHere's some examples, to help you get started using Ivy! The [examples page](https:\u002F\u002Fivy-llc.github.io\u002Fdocs\u002Fdemos\u002Fexamples_and_demos.html) also features a wide range of\r\ndemos and tutorials showcasing some more use cases for Ivy.\r\n\r\n  \u003Cdetails>\r\n    \u003Csummary>\u003Cb>Transpiling any code from one framework to another\u003C\u002Fb>\u003C\u002Fsummary>\r\n    \u003Cbr clear=\"all\" \u002F>\r\n\r\n   ``` python\r\n   import ivy\r\n   import torch\r\n   import tensorflow as tf\r\n\r\n   def torch_fn(x):\r\n       a = torch.mul(x, x)\r\n       b = torch.mean(x)\r\n       return x * a + b\r\n\r\n   tf_fn = ivy.transpile(torch_fn, source=\"torch\", target=\"tensorflow\")\r\n\r\n   tf_x = tf.convert_to_tensor([1., 2., 3.])\r\n   ret = tf_fn(tf_x)\r\n   ```\r\n\r\n  \u003C\u002Fdetails>\r\n\r\n  \u003Cdetails>\r\n    \u003Csummary>\u003Cb>Tracing a computational graph of any code\u003C\u002Fb>\u003C\u002Fsummary>\r\n    \u003Cbr clear=\"all\" \u002F>\r\n\r\n   ``` python\r\n   import ivy\r\n   import torch\r\n\r\n   def torch_fn(x):\r\n       a = torch.mul(x, x)\r\n       b = torch.mean(x)\r\n       return x * a + b\r\n\r\n   torch_x = torch.tensor([1., 2., 3.])\r\n   graph = ivy.trace_graph(jax_fn, to=\"torch\", args=(torch_x,))\r\n   ret = graph(torch_x)\r\n   ```\r\n\r\n   \u003C\u002Fdetails>\r\n\r\n\u003Cdetails>\r\n\u003Csummary>\u003Cb>How does ivy work?\u003C\u002Fb>\u003C\u002Fsummary>\r\n\u003Cbr clear=\"all\" \u002F>\r\n\r\nIvy\\'s transpiler allows you to use code from any other framework in your own code.\r\nFeel free to head over to the docs for the full API\r\nreference, but the functions you\\'d most likely want to use are:\r\n\r\n``` python\r\n# Converts framework-specific code to a target framework of choice. See usage in the documentation\r\nivy.transpile()\r\n\r\n# Traces an efficient fully-functional graph from a function, removing all wrapping and redundant code. See usage in the documentation\r\nivy.trace_graph()\r\n```\r\n\r\n#### `ivy.transpile` will eagerly transpile if a class or function is provided\r\n\r\n``` python\r\nimport ivy\r\nimport torch\r\nimport tensorflow as tf\r\n\r\ndef torch_fn(x):\r\n    x = torch.abs(x)\r\n    return torch.sum(x)\r\n\r\nx1 = torch.tensor([1., 2.])\r\nx1 = tf.convert_to_tensor([1., 2.])\r\n\r\n# Transpilation happens eagerly\r\ntf_fn = ivy.transpile(test_fn, source=\"torch\", target=\"tensorflow\")\r\n\r\n# tf_fn is now tensorflow code and runs efficiently\r\nret = tf_fn(x1)\r\n```\r\n\r\n#### `ivy.transpile` will lazily transpile if a module (library) is provided\r\n\r\n``` python\r\nimport ivy\r\nimport kornia\r\nimport tensorflow as tf\r\n\r\nx2 = tf.random.normal((5, 3, 4, 4))\r\n\r\n# Module is provided -> transpilation happens lazily\r\ntf_kornia = ivy.transpile(kornia, source=\"torch\", target=\"tensorflow\")\r\n\r\n# The transpilation is initialized here, and this function is converted to tensorflow\r\nret = tf_kornia.color.rgb_to_grayscale(x2)\r\n\r\n# Transpilation has already occurred, the tensorflow function runs efficiently\r\nret = tf_kornia.color.rgb_to_grayscale(x2)\r\n```\r\n\u003C\u002Fdetails>\r\n\r\n\u003Cbr clear=\"all\" \u002F>\r\n\r\n# Contributing\r\n\r\nWe believe that everyone can contribute and make a difference. Whether\r\nit\\'s writing code, fixing bugs, or simply sharing feedback,\r\nyour contributions are definitely welcome and appreciated\"\r\n\r\nCheck out all of our [Open Tasks](https:\u002F\u002Fivy-llc.github.io\u002Fdocs\u002Foverview\u002Fcontributing\u002Fopen_tasks.html),\r\nand find out more info in our [Contributing Guide](https:\u002F\u002Fivy-llc.github.io\u002Fdocs\u002Foverview\u002Fcontributing.html)\r\nin the docs.\r\n\r\n\u003Cbr clear=\"all\" \u002F>\r\n\r\n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fivy-llc\u002Fivy\u002Fgraphs\u002Fcontributors\">\r\n  \u003Cimg class=\"dark-light\" src=\"https:\u002F\u002Fcontrib.rocks\u002Fimage?repo=ivy-llc\u002Fivy&anon=0&columns=20&max=100&r=true\" \u002F>\r\n\u003C\u002Fa>\r\n\r\n\u003Cbr clear=\"all\" \u002F>\r\n\u003Cbr clear=\"all\" \u002F>\r\n\r\n# Citation\r\n\r\n    @article{lenton2021ivy,\r\n      title={Ivy: Templated deep learning for inter-framework portability},\r\n      author={Lenton, Daniel and Pardo, Fabio and Falck, Fabian and James, Stephen and Clark, Ronald},\r\n      journal={arXiv preprint arXiv:2102.02886},\r\n      year={2021}\r\n    }\r\n","Ivy 是一个用于在不同机器学习框架之间转换代码的工具。其核心功能是通过 `ivy.transpile` 方法，实现如 JAX、TensorFlow 和 PyTorch 等主流框架之间的模型和库的无缝迁移。Ivy 采用 Python 编写，支持多种深度学习库，并且具备良好的测试与集成环境以确保转换过程中的准确性和一致性。适用于需要跨框架复用现有机器学习资源或希望减少因更换计算后端而带来的开发成本的场景。",2,"2026-06-11 03:35:01","high_star"]