ivy

Convert Machine Learning Code Between Frameworks

ivy-llc
14223
5631
Python


Convert Machine Learning Code Between Frameworks

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


Installation

The easiest way to install Ivy is using pip:

pip install ivy
From Source

You can also install Ivy from source if you want to take advantage of
the latest changes:

git clone https://github.com/ivy-llc/ivy.git
cd ivy
pip install --user -e .

Supported Frameworks

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

Framework Source Target
PyTorch ✅ 🚧
TensorFlow 🚧 ✅
JAX 🚧 ✅
NumPy 🚧 ✅

Using ivy

Here’s some examples, to help you get started using Ivy! The examples page also features a wide range of
demos and tutorials showcasing some more use cases for Ivy.

Transpiling any code from one framework to another 
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)
Tracing a computational graph of any code 
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)
How does ivy work?

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:

# 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

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

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)

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,
and find out more info in our Contributing Guide
in the docs.




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}
}