Allensmile / ivy

The Unified Machine Learning Framework

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We’re on a mission to unify all ML frameworks πŸ’₯ + automate code conversions πŸ”„. pip install ivy-core πŸš€, join our growing community 😊, and! 🦾



Ivy is an ML framework which currently supports JAX, TensorFlow, PyTorch, MXNet and Numpy. We’re very excited for you to try it out!

Next on our road-map is to support automatic code conversions between any frameworks πŸ”„, and add instant multi-framework support for all open-source libraries with only a few lines of code changed! Read on to learn more 😊

The docs are split into a number of sub-pages explaining different aspects of why we created Ivy, how to use it, what we’ve got planned on our road-map, and how to contribute! Click on the sub-headings to check out these pages!

We use 🚧 to indicate that the feature being discussed is in development. We use βœ… to indicate that it is already implemented!

Check out the docs for more info, and check out our Google Colabs for some interactive demos!

Quick Start

Ivy can be installed like so: pip install ivy-core You can immediately use Ivy to train a neural network, using your favourite framework in the background, like so:

import ivy

class MyModel(ivy.Module):
    def __init__(self):
        self.linear0 = ivy.Linear(3, 64)
        self.linear1 = ivy.Linear(64, 1)

    def _forward(self, x):
        x = ivy.relu(self.linear0(x))
        return ivy.sigmoid(self.linear1(x))

ivy.set_framework('torch')  # change to any framework!
model = MyModel()
optimizer = ivy.Adam(1e-4)
x_in = ivy.array([1., 2., 3.])
target = ivy.array([0.])

def loss_fn(v):
    out = model(x_in, v=v)
    return ivy.reduce_mean((out - target)**2)[0]

for step in range(100):
    loss, grads = ivy.execute_with_gradients(loss_fn, model.v)
    model.v = optimizer.step(model.v, grads)
    print('step {} loss {}'.format(step, ivy.to_numpy(loss).item()))

print('Finished training!')

This example uses PyTorch as a backend framework, but the backend can easily be changed to your favourite framework, such as TensorFlow, JAX or MXNet.

Framework Agnostic Functions

In the example below we show how Ivy's concatenation function is compatible with tensors from different frameworks. This is the same for ALL Ivy functions. They can accept tensors from any framework and return the correct result.

import jax.numpy as jnp
import tensorflow as tf
import numpy as np
import mxnet as mx
import torch

import ivy

jax_concatted   = ivy.concat((jnp.ones((1,)), jnp.ones((1,))), -1)
tf_concatted    = ivy.concat((tf.ones((1,)), tf.ones((1,))), -1)
np_concatted    = ivy.concat((np.ones((1,)), np.ones((1,))), -1)
mx_concatted    = ivy.concat((mx.nd.ones((1,)), mx.nd.ones((1,))), -1)
torch_concatted = ivy.concat((torch.ones((1,)), torch.ones((1,))), -1)

To see a list of all Ivy methods, type ivy. into a python command prompt and press tab. You should then see output like the following:

ivy.Container(                         ivy.general                               ivy.reduce_min(
ivy.abs(                               ivy.get_device(                           ivy.reduce_prod(
ivy.acos(                              ivy.get_num_dims(                         ivy.reduce_sum(
ivy.acosh(                             ivy.gradient_descent_update(              ivy.reductions
ivy.activations                        ivy.gradient_image(                       ivy.relu(
ivy.arange(                            ivy.gradients                             ivy.reshape(
ivy.argmax(                            ivy.identity(                             ivy.round(
ivy.argmin(                            ivy.image                                 ivy.scatter_nd(
ivy.array(                             ivy.indices_where(                        ivy.seed(
ivy.asin(                              ivy.inv(                                  ivy.shape(
ivy.asinh(                             ivy.layers                                ivy.shuffle(
ivy.atan(                              ivy.leaky_relu(                           ivy.sigmoid(
ivy.atan2(                             ivy.linalg                                ivy.sin(
ivy.atanh(                             ivy.linear(                               ivy.sinh(
ivy.bilinear_resample(                 ivy.linspace(                             ivy.softmax(
ivy.cast(                              ivy.log(                                  ivy.softplus(
ivy.ceil(                              ivy.logic                                 ivy.split(
ivy.clip(                              ivy.logical_and(                          ivy.squeeze(
ivy.concatenate(                       ivy.logical_not(                          ivy.stack(
ivy.container                          ivy.logical_or(                           ivy.stack_images(
ivy.conv2d(                            ivy.math                                  ivy.stop_gradient(
ivy.core                               ivy.matmul(                               ivy.svd(
ivy.cos(                               ivy.maximum(                              ivy.tan(
ivy.cosh(                              ivy.minimum(                              ivy.tanh(
ivy.cross(                             ivy.neural_net                            ivy.tile(
ivy.cumsum(                            ivy.nn                                    ivy.to_list(
ivy.depthwise_conv2d(                  ivy.norm(                                 ivy.to_numpy(
ivy.dtype(                             ivy.one_hot(                              ivy.transpose(
ivy.execute_with_gradients(            ivy.ones(                                 ivy.unstack(
ivy.exp(                               ivy.ones_like(                            ivy.variable(
ivy.expand_dims(                       ivy.pinv(                                 ivy.vector_to_skew_symmetric_matrix(
ivy.flip(                              ivy.randint(                              ivy.verbosity
ivy.floor(                             ivy.random                                ivy.where(
ivy.floormod(                          ivy.random_uniform(                       ivy.zero_pad(
ivy.framework_handler                  ivy.reduce_max(                           ivy.zeros(
ivy.gather_nd(                         ivy.reduce_mean(                          ivy.zeros_like(


A huge number of ML tools have exploded onto the scene!

Why should we try to unify them?

We’re collaborating with The Consortium for Python Data API Standards


Ivy can fulfill two distinct purposes:

1. Serve as a transpiler between frameworks 🚧
2. Serve as a new ML framework with multi-framework support βœ…

The Ivy codebase can then be split into three categories, and can be further split into 8 distinct submodules, each of which fall into one of these three categories as follows:

Back-end functional APIs βœ…
Ivy functional API βœ…
Framework Handler βœ…
Ivy Compiler 🚧

Front-end functional APIs 🚧

Ivy stateful API βœ…
Ivy Container βœ…
Ivy Array 🚧


Ivy libraries in mechanics, vision, robotics, memory and other areas

(b) Builder [page coming soon!] βœ…
ivy.Trainer, ivy.Dataset, ivy.Dataloader and other helpful classes and functions for creating training workflows in only a few lines of code


We strongly welcome and encourage contributions from the community as we take on this important journey towards ML framework unification. These posts will explain exactly how you can get involved πŸ™‚

(a) Standardize [page coming soon!] 🚧

(b) Front-Ends [page coming soon!] 🚧
Create framework-specific front-ends for each supported ML framework

(c) Transpiler [page coming soon!] 🚧
Verify code conversions work for each back-end and front-end combo

(d) Ecosystem [page coming soon!] 🚧
Add multi-framework support to popular repos with a few lines changed


Join our community as a code contributor, and help accelerate our journey to unify all ML frameworks! Find out more in our Contributing guide!


  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},
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The Unified Machine Learning Framework

License:Apache License 2.0


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