base on Convert Machine Learning Code Between Frameworks <div style="display: block;" align="center"> <a href="https://ivy.dev/"> <img class="dark-light" width="50%" src="https://raw.githubusercontent.com/ivy-llc/assets/refs/heads/main/assets/logos/ivy-long.svg"/> </a> </div> <br clear="all" /> <div style="margin-top: 10px; margin-bottom: 10px; display: block;" align="center"> <a href="https://github.com/ivy-llc/ivy/stargazers"> <img class="dark-light" style="padding-right: 4px; padding-bottom: 4px;" src="https://img.shields.io/github/stars/ivy-llc/ivy"> </a> <a href="https://discord.gg/uYRmyPxMQq"> <img class="dark-light" style="padding-right: 4px; padding-bottom: 4px;" src="https://img.shields.io/discord/1220325004013604945?color=blue&label=%20&logo=discord&logoColor=white"> </a> <a href="https://ivy-llc.github.io/docs/"> <img class="dark-light" style="padding-right: 4px; padding-bottom: 4px;" src="https://img.shields.io/badge/docs-purple"> </a> <a href="https://github.com/ivy-llc/ivy/actions/workflows/test-transpiler.yml"> <img class="dark-light" style="padding-right: 4px; padding-bottom: 4px;" src="https://github.com/ivy-llc/ivy/actions/workflows/test-transpiler.yml/badge.svg"> </a> <a href="https://github.com/ivy-llc/ivy/actions/workflows/integration-tests.yml"> <img class="dark-light" style="padding-right: 4px; padding-bottom: 4px;" src="https://github.com/ivy-llc/ivy/actions/workflows/integration-tests.yml/badge.svg"> </a> </div> <br clear="all" /> # Convert Machine Learning Code Between Frameworks Ivy enables you to convert ML models, tools and libraries between frameworks using `ivy.transpile` <div style="display: block;" align="center"> <div> <a href="https://jax.readthedocs.io"> <img class="dark-light" width="100" height="100" src="https://raw.githubusercontent.com/ivy-llc/assets/refs/heads/main/assets/logos/jax.svg"> </a> <img class="dark-light" width="5%" src="https://github.com/ivy-llc/assets/blob/main/assets/empty.png?raw=true"> <img class="dark-light" width="5%" src="https://github.com/ivy-llc/assets/blob/main/assets/empty.png?raw=true"> <a href="https://www.tensorflow.org"> <img class="dark-light" width="100" height="100" src="https://raw.githubusercontent.com/ivy-llc/assets/refs/heads/main/assets/logos/tensorflow.svg"> </a> <img class="dark-light" width="5%" src="https://github.com/ivy-llc/assets/blob/main/assets/empty.png?raw=true"> <img class="dark-light" width="5%" src="https://github.com/ivy-llc/assets/blob/main/assets/empty.png?raw=true"> <a href="https://pytorch.org"> <img class="dark-light" width="100" height="100" src="https://raw.githubusercontent.com/ivy-llc/assets/refs/heads/main/assets/logos/pytorch.svg"> </a> <img class="dark-light" width="5%" src="https://github.com/ivy-llc/assets/blob/main/assets/empty.png?raw=true"> <img class="dark-light" width="5%" src="https://github.com/ivy-llc/assets/blob/main/assets/empty.png?raw=true"> <a href="https://numpy.org"> <img class="dark-light" width="100" height="100" src="https://raw.githubusercontent.com/ivy-llc/assets/refs/heads/main/assets/logos/numpy.svg"> </a> </div> </div> <br clear="all" /> # Installation The easiest way to install Ivy is using **pip**: ``` bash pip install ivy ``` <details> <summary><b>From Source</b></summary> <br clear="all" /> You can also install Ivy from source if you want to take advantage of the latest changes: ``` bash git clone https://github.com/ivy-llc/ivy.git cd ivy pip install --user -e . ``` </details> <br clear="all" /> # Supported Frameworks These are the frameworks that `ivy.transpile` currently supports conversions from and to. | Framework | Source | Target | |------------|:------:|:------:| | PyTorch | ✅ | 🚧 | | TensorFlow | 🚧 | ✅ | | JAX | 🚧 | ✅ | | NumPy | 🚧 | ✅ | <br clear="all" /> # Using ivy Here's some examples, to help you get started using Ivy! The [examples page](https://www.docs.ivy.dev/demos/examples_and_demos.html) also features a wide range of demos and tutorials showcasing some more use cases for Ivy. <details> <summary><b>Transpiling any code from one framework to another</b></summary> <br clear="all" /> ``` python import ivy import torch import tensorflow as tf def torch_fn(x): a = torch.mul(x, x) b = torch.mean(x) return x * a + b tf_fn = ivy.transpile(torch_fn, source="torch", target="tensorflow") tf_x = tf.convert_to_tensor([1., 2., 3.]) ret = tf_fn(tf_x) ``` </details> <details> <summary><b>Tracing a computational graph of any code</b></summary> <br clear="all" /> ``` python import ivy import torch def torch_fn(x): a = torch.mul(x, x) b = torch.mean(x) return x * a + b torch_x = torch.tensor([1., 2., 3.]) graph = ivy.trace_graph(jax_fn, to="torch", args=(torch_x,)) ret = graph(torch_x) ``` </details> <details> <summary><b>How does ivy work?</b></summary> <br clear="all" /> Ivy\'s transpiler allows you to use code from any other framework in your own code. Feel free to head over to the docs for the full API reference, but the functions you\'d most likely want to use are: ``` python # Converts framework-specific code to a target framework of choice. See usage in the documentation ivy.transpile() # Traces an efficient fully-functional graph from a function, removing all wrapping and redundant code. See usage in the documentation ivy.trace_graph() ``` #### `ivy.transpile` will eagerly transpile if a class or function is provided ``` python import ivy import torch import tensorflow as tf def torch_fn(x): x = torch.abs(x) return torch.sum(x) x1 = torch.tensor([1., 2.]) x1 = tf.convert_to_tensor([1., 2.]) # Transpilation happens eagerly tf_fn = ivy.transpile(test_fn, source="torch", target="tensorflow") # tf_fn is now tensorflow code and runs efficiently ret = tf_fn(x1) ``` #### `ivy.transpile` will lazily transpile if a module (library) is provided ``` python import ivy import kornia import tensorflow as tf x2 = tf.random.normal((5, 3, 4, 4)) # Module is provided -> transpilation happens lazily tf_kornia = ivy.transpile(kornia, source="torch", target="tensorflow") # The transpilation is initialized here, and this function is converted to tensorflow ret = tf_kornia.color.rgb_to_grayscale(x2) # Transpilation has already occurred, the tensorflow function runs efficiently ret = tf_kornia.color.rgb_to_grayscale(x2) ``` </details> <br clear="all" /> # Contributing We believe that everyone can contribute and make a difference. Whether it\'s writing code, fixing bugs, or simply sharing feedback, your contributions are definitely welcome and appreciated" Check out all of our [Open Tasks](https://docs.ivy.dev/overview/contributing/open_tasks.html), and find out more info in our [Contributing Guide](https://docs.ivy.dev/overview/contributing.html) in the docs. <br clear="all" /> <a href="https://github.com/ivy-llc/ivy/graphs/contributors"> <img class="dark-light" src="https://contrib.rocks/image?repo=ivy-llc/ivy&anon=0&columns=20&max=100&r=true" /> </a> <br clear="all" /> <br clear="all" /> # Citation @article{lenton2021ivy, title={Ivy: Templated deep learning for inter-framework portability}, author={Lenton, Daniel and Pardo, Fabio and Falck, Fabian and James, Stephen and Clark, Ronald}, journal={arXiv preprint arXiv:2102.02886}, year={2021} } ", Assign "at most 3 tags" to the expected json: {"id":"1936","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"