ivy

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  <a href="https://ivy-llc.github.io/docs/">
    <picture>
      <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/unifyai/ivy/main/assets/brand/ivy-docs-banner-dark.png">
      <img src="https://raw.githubusercontent.com/unifyai/ivy/main/assets/brand/ivy-docs-banner-light.png" alt="ivy, a unify project. Convert machine learning code between frameworks." width="100%">
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<p align="center">
  <a href="https://ivy-llc.github.io/docs/">Docs</a> · <a href="https://discord.gg/uYRmyPxMQq">Discord</a> · <a href="LICENSE">License</a>
</p>

# Convert Machine Learning Code Between Frameworks

ivy enables you to convert ML models, tools and libraries between frameworks using `ivy.transpile`

<p align="center">
  <img src="https://raw.githubusercontent.com/unifyai/ivy/main/assets/ivy-frameworks.png" alt="ONNX, PyTorch, NumPy, TensorFlow, JAX and MXNet, frameworks ivy works with" width="100%">
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# Installation

The easiest way to install ivy is using **pip**:

``` bash
pip install ivy
```

<details>
<summary><b>From Source</b></summary>
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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>

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# Supported Frameworks

These are the frameworks that `ivy.transpile` currently supports conversions from and to.

| Framework  | Source | Target |
|------------|:------:|:------:|
| PyTorch    |   ✓   |   planned   |
| TensorFlow |   planned   |   ✓   |
| JAX        |   planned   |   ✓   |
| NumPy      |   planned   |   ✓   |

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# Using ivy

Here's some examples, to help you get started using ivy! The [examples page](https://ivy-llc.github.io/docs/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>
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   ``` 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>
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   ``` 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>
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