You can pull the Docker image for Ivy from:

docker pull ivyllc/ivy:latest

You can also install Ivy from source if you want to take advantage of the latest changes, but we can't ensure everything will work as expected 😅

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

If you want to set up testing and various frameworks it's probably best to check out the Setting Up page, where OS-specific and IDE-specific instructions and video tutorials to do so are available!

import ivy
import torch
import jax

def jax_fn(x):
    a = jax.numpy.dot(x, x)
    b = jax.numpy.mean(x)
    return x * a + b

jax_x = jax.numpy.array([1., 2., 3.])
torch_x = torch.tensor([1., 2., 3.])
torch_fn = ivy.transpile(jax_fn, source="jax", to="torch", args=(jax_x,))
ret = torch_fn(torch_x)

 import ivy
 import torch
 import jax

 ivy.set_backend("jax")

 x = jax.numpy.array([1, 2, 3])
 y = jax.numpy.array([3, 2, 1])
 z = ivy.add(x, y)

 ivy.set_backend('torch')

 x = torch.tensor([1, 2, 3])
 y = torch.tensor([3, 2, 1])
 z = ivy.add(x, y)

I'm using PyTorch 

You can use Ivy to get PyTorch code from:

Any model

From TensorFlow

import ivy
import torch
import tensorflow as tf

# Get a pretrained keras model
eff_encoder = tf.keras.applications.efficientnet_v2.EfficientNetV2B0(
    include_top=False, weights="imagenet", input_shape=(224, 224, 3)
)

# Transpile it into a torch.nn.Module with the corresponding parameters
noise = tf.random.normal(shape=(1, 224, 224, 3))
torch_eff_encoder = ivy.transpile(eff_encoder, source="tensorflow", to="torch", args=(noise,))

# Build a classifier using the transpiled encoder
class Classifier(torch.nn.Module):
    def __init__(self, num_classes=20):
        super().__init__()
        self.encoder = torch_eff_encoder
        self.fc = torch.nn.Linear(1280, num_classes)

    def forward(self, x):
        x = self.encoder(x)
        return self.fc(x)

# Initialize a trainable, customizable, torch.nn.Module
classifier = Classifier()
ret = classifier(torch.rand((1, 244, 244, 3)))

From JAX

import ivy
import jax
import torch

# Get a pretrained haiku model
# https://github.com/unifyai/demos/blob/15c235f/scripts/deepmind_perceiver_io.py
from deepmind_perceiver_io import key, perceiver_backbone

# Transpile it into a torch.nn.Module with the corresponding parameters
dummy_input = jax.random.uniform(key, shape=(1, 3, 224, 224))
params = perceiver_backbone.init(rng=key, images=dummy_input)
ivy.set_backend("jax")
backbone = ivy.transpile(
    perceiver_backbone, source="jax", to="torch", params_v=params, kwargs={"images": dummy_input}
)

# Build a classifier using the transpiled backbone
class PerceiverIOClassifier(torch.nn.Module):
    def __init__(self, num_classes=20):
        super().__init__()
        self.backbone = backbone
        self.max_pool = torch.nn.MaxPool2d((512, 1))
        self.flatten = torch.nn.Flatten()
        self.fc = torch.nn.Linear(1024, num_classes)

    def forward(self, x):
        x = self.backbone(images=x)
        x = self.flatten(self.max_pool(x))
        return self.fc(x)

# Initialize a trainable, customizable, torch.nn.Module
classifier = PerceiverIOClassifier()
ret = classifier(torch.rand((1, 3, 224, 224)))

Any library

From Tensorflow

import ivy
import torch
import os
os.environ["SM_FRAMEWORK"] = "tf.keras"
import segmentation_models as sm

# transpile sm from tensorflow to torch
torch_sm = ivy.transpile(sm, source="tensorflow", to="torch")

# get some image-like arrays
output = torch.rand((1, 3, 512, 512))
target = torch.rand((1, 3, 512, 512))

# and use the transpiled version of any function from the library!
out = torch_sm.metrics.iou_score(output, target)

From JAX

import ivy
import rax
import torch

# transpile rax from jax to torch
torch_rax = ivy.transpile(rax, source="jax", to="torch")

# get some arrays
scores = torch.tensor([2.2, 1.3, 5.4])
labels = torch.tensor([1.0, 0.0, 0.0])

# and use the transpiled version of any function from the library!
out = torch_rax.poly1_softmax_loss(scores, labels)

From NumPy

import ivy
import torch
import madmom

# transpile madmon from numpy to torch
torch_madmom = ivy.transpile(madmom, source="numpy", to="torch")

# get some arrays
freqs = torch.arange(20) * 10

# and use the transpiled version of any function from the library!
out = torch_madmom.audio.filters.hz2midi(freqs)

Any function

From Tensorflow

import ivy
import tensorflow as tf
import torch

def loss(predictions, targets):
    return tf.sqrt(tf.reduce_mean(tf.square(predictions - targets)))

# transpile any function from tf to torch
torch_loss = ivy.transpile(loss, source="tensorflow", to="torch")

# get some arrays
p = torch.tensor([3.0, 2.0, 1.0])
t = torch.tensor([0.0, 0.0, 0.0])

# and use the transpiled version!
out = torch_loss(p, t)

From JAX

import ivy
import jax.numpy as jnp
import torch

def loss(predictions, targets):
    return jnp.sqrt(jnp.mean((predictions - targets) ** 2))

# transpile any function from jax to torch
torch_loss = ivy.transpile(loss, source="jax", to="torch")

# get some arrays
p = torch.tensor([3.0, 2.0, 1.0])
t = torch.tensor([0.0, 0.0, 0.0])

# and use the transpiled version!
out = torch_loss(p, t)

From NumPy

import ivy
import numpy as np
import torch

def loss(predictions, targets):
    return np.sqrt(np.mean((predictions - targets) ** 2))

# transpile any function from numpy to torch
torch_loss = ivy.transpile(loss, source="numpy", to="torch")

# get some arrays
p = torch.tensor([3.0, 2.0, 1.0])
t = torch.tensor([0.0, 0.0, 0.0])

# and use the transpiled version!
out = torch_loss(p, t)

I'm using TensorFlow 

You can use Ivy to get TensorFlow code from:

Any model

From PyTorch

import ivy
import torch
import timm
import tensorflow as tf

# Get a pretrained pytorch model
mlp_encoder = timm.create_model("mixer_b16_224", pretrained=True, num_classes=0)

# Transpile it into a keras.Model with the corresponding parameters
noise = torch.randn(1, 3, 224, 224)
mlp_encoder = ivy.transpile(mlp_encoder, to="tensorflow", args=(noise,))

# Build a classifier using the transpiled encoder
class Classifier(tf.keras.Model):
    def __init__(self):
        super().__init__()
        self.encoder = mlp_encoder
        self.output_dense = tf.keras.layers.Dense(units=1000, activation="softmax")

    def call(self, x):
        x = self.encoder(x)
        return self.output_dense(x)

# Transform the classifier and use it as a standard keras.Model
x = tf.random.normal(shape=(1, 3, 224, 224))
model = Classifier()
ret = model(x)

From JAX

import ivy
import jax
import tensorflow as tf

# Get a pretrained haiku model
# https://ivy.dev/demos/scripts/deepmind_perceiver_io.py
from deepmind_perceiver_io import key, perceiver_backbone

# Transpile it into a tf.keras.Model with the corresponding parameters
dummy_input = jax.random.uniform(key, shape=(1, 3, 224, 224))
params = perceiver_backbone.init(rng=key, images=dummy_input)
backbone = ivy.transpile(
    perceiver_backbone, to="tensorflow", params_v=params, args=(dummy_input,)
)

# Build a classifier using the transpiled backbone
class PerceiverIOClassifier(tf.keras.Model):
    def __init__(self, num_classes=20):
        super().__init__()
        self.backbone = backbone
        self.max_pool = tf.keras.layers.MaxPooling1D(pool_size=512)
        self.flatten = tf.keras.layers.Flatten()
        self.fc = tf.keras.layers.Dense(num_classes)

    def call(self, x):
        x = self.backbone(x)
        x = self.flatten(self.max_pool(x))
        return self.fc(x)

# Initialize a trainable, customizable, tf.keras.Model
x = tf.random.normal(shape=(1, 3, 224, 224))
classifier = PerceiverIOClassifier()
ret = classifier(x)

Any library

From PyTorch

import ivy
import kornia
import requests
import numpy as np
import tensorflow as tf
from PIL import Image

# transpile kornia from torch to tensorflow
tf_kornia = ivy.transpile(kornia, source="torch", to="tensorflow")

# get an image
url = "http://images.cocodataset.org/train2017/000000000034.jpg"
raw_img = Image.open(requests.get(url, stream=True).raw)

# convert it to the format expected by kornia
img = np.array(raw_img)
img = tf.transpose(tf.constant(img), (2, 0, 1))
img = tf.expand_dims(img, 0) / 255

# and use the transpiled version of any function from the library!
out = tf_kornia.enhance.sharpness(img, 5)

From JAX

import ivy
import rax
import tensorflow as tf

# transpile rax from jax to tensorflow
tf_rax = ivy.transpile(rax, source="jax", to="tensorflow")

# get some arrays
scores = tf.constant([2.2, 1.3, 5.4])
labels = tf.constant([1.0, 0.0, 0.0])

# and use the transpiled version of any function from the library!
out = tf_rax.poly1_softmax_loss(scores, labels)

From NumPy

import ivy
import madmom
import tensorflow as tf

# transpile madmom from numpy to tensorflow
tf_madmom = ivy.transpile(madmom, source="numpy", to="tensorflow")

# get some arrays
freqs = tf.range(20) * 10

# and use the transpiled version of any function from the library!
out = tf_madmom.audio.filters.hz2midi(freqs)

Any function

From PyTorch

import ivy
import torch
import tensorflow as tf

def loss(predictions, targets):
    return torch.sqrt(torch.mean((predictions - targets) ** 2))

# transpile any function from torch to tensorflow
tf_loss = ivy.transpile(loss, source="torch", to="tensorflow")

# get some arrays
p = tf.constant([3.0, 2.0, 1.0])
t = tf.constant([0.0, 0.0, 0.0])

# and use the transpiled version!
out = tf_loss(p, t)

From JAX

import ivy
import jax.numpy as jnp
import tensorflow as tf

def loss(predictions, targets):
    return jnp.sqrt(jnp.mean((predictions - targets) ** 2))

# transpile any function from jax to tensorflow
tf_loss = ivy.transpile(loss, source="jax", to="tensorflow")

# get some arrays
p = tf.constant([3.0, 2.0, 1.0])
t = tf.constant([0.0, 0.0, 0.0])

# and use the transpiled version!
out = tf_loss(p, t)

From NumPy

import ivy
import numpy as np
import tensorflow as tf

def loss(predictions, targets):
    return np.sqrt(np.mean((predictions - targets) ** 2))

# transpile any function from numpy to tensorflow
tf_loss = ivy.transpile(loss, source="numpy", to="tensorflow")

# get some arrays
p = tf.constant([3.0, 2.0, 1.0])
t = tf.constant([0.0, 0.0, 0.0])

# and use the transpiled version!
out = tf_loss(p, t)

I'm using Jax 

You can use Ivy to get JAX code from:

Any model

From PyTorch

import ivy
import timm
import torch
import jax
import haiku as hk

# Get a pretrained pytorch model
mlp_encoder = timm.create_model("mixer_b16_224", pretrained=True, num_classes=0)

# Transpile it into a hk.Module with the corresponding parameters
noise = torch.randn(1, 3, 224, 224)
mlp_encoder = ivy.transpile(mlp_encoder, source="torch", to="haiku", args=(noise,))

# Build a classifier using the transpiled encoder
class Classifier(hk.Module):
    def __init__(self, num_classes=1000):
        super().__init__()
        self.encoder = mlp_encoder()
        self.fc = hk.Linear(output_size=num_classes, with_bias=True)

    def __call__(self, x):
        x = self.encoder(x)
        x = self.fc(x)
        return x

def _forward_classifier(x):
    module = Classifier()
    return module(x)

# Transform the classifier and use it as a standard hk.Module
rng_key = jax.random.PRNGKey(42)
x = jax.random.uniform(key=rng_key, shape=(1, 3, 224, 224), dtype=jax.numpy.float32)
forward_classifier = hk.transform(_forward_classifier)
params = forward_classifier.init(rng=rng_key, x=x)

ret = forward_classifier.apply(params, None, x)

From TensorFlow

import ivy
import jax
import haiku as hk
import tensorflow as tf
jax.config.update("jax_enable_x64", True)

# Get a pretrained keras model
eff_encoder = tf.keras.applications.efficientnet_v2.EfficientNetV2B0(
    include_top=False, weights="imagenet", input_shape=(224, 224, 3)
)

# Transpile it into a hk.Module with the corresponding parameters
noise = tf.random.normal(shape=(1, 224, 224, 3))
hk_eff_encoder = ivy.transpile(eff_encoder, source="tensorflow", to="haiku", args=(noise,))

# Build a classifier using the transpiled encoder
class Classifier(hk.Module):
    def __init__(self, num_classes=1000):
        super().__init__()
        self.encoder = hk_eff_encoder()
        self.fc = hk.Linear(output_size=num_classes, with_bias=True)

    def __call__(self, x):
        x = self.encoder(x)
        x = self.fc(x)
        return x

def _forward_classifier(x):
    module = Classifier()
    return module(x)

# Transform the classifier and use it as a standard hk.Module
rng_key = jax.random.PRNGKey(42)
dummy_x = jax.random.uniform(key=rng_key, shape=(1, 224, 224, 3))
forward_classifier = hk.transform(_forward_classifier)
params = forward_classifier.init(rng=rng_key, x=dummy_x)

ret = forward_classifier.apply(params, None, dummy_x)

Any library

From PyTorch

import ivy
import kornia
import requests
import jax.numpy as jnp
from PIL import Image
jax.config.update("jax_enable_x64", True)

# transpile kornia from torch to jax
jax_kornia = ivy.transpile(kornia, source="torch", to="jax")

# get an image
url = "http://images.cocodataset.org/train2017/000000000034.jpg"
raw_img = Image.open(requests.get(url, stream=True).raw)

# convert it to the format expected by kornia
img = jnp.transpose(jnp.array(raw_img), (2, 0, 1))
img = jnp.expand_dims(img, 0) / 255

# and use the transpiled version of any function from the library!
out = jax_kornia.enhance.sharpness(img, 5)

From TensorFlow

import ivy
import jax
import os
os.environ["SM_FRAMEWORK"] = "tf.keras"
import segmentation_models as sm

# transpile sm from tensorflow to jax
jax_sm = ivy.transpile(sm, source="tensorflow", to="jax")

# get some image-like arrays
key = jax.random.PRNGKey(23)
key1, key2 = jax.random.split(key)
output = jax.random.uniform(key1, (1, 3, 512, 512))
target = jax.random.uniform(key2, (1, 3, 512, 512))

# and use the transpiled version of any function from the library!
out = jax_sm.metrics.iou_score(output, target)

From NumPy

import ivy
import madmom
import jax.numpy as jnp

# transpile madmon from numpy to jax
jax_madmom = ivy.transpile(madmom, source="numpy", to="jax")

# get some arrays
freqs = jnp.arange(20) * 10

# and use the transpiled version of any function from the library!
out = jax_madmom.audio.filters.hz2midi(freqs)

Any function

From PyTorch

import ivy
import torch
import jax.numpy as jnp

def loss(predictions, targets):
    return torch.sqrt(torch.mean((predictions - targets) ** 2))

# transpile any function from torch to jax
jax_loss = ivy.transpile(loss, source="torch", to="jax")

# get some arrays
p = jnp.array([3.0, 2.0, 1.0])
t = jnp.array([0.0, 0.0, 0.0])

# and use the transpiled version!
out = jax_loss(p, t)

From TensorFlow

import ivy
import tensorflow as tf
import jax.numpy as jnp

def loss(predictions, targets):
    return tf.sqrt(tf.reduce_mean(tf.square(predictions - targets)))

# transpile any function from tf to jax
jax_loss = ivy.transpile(loss, source="tensorflow", to="jax")

# get some arrays
p = jnp.array([3.0, 2.0, 1.0])
t = jnp.array([0.0, 0.0, 0.0])

# and use the transpiled version!
out = jax_loss(p, t)

From NumPy

import ivy
import numpy as np
import jax
import jax.numpy as jnp
jax.config.update('jax_enable_x64', True)

def loss(predictions, targets):
    return np.sqrt(np.mean((predictions - targets) ** 2))

# transpile any function from numpy to jax
jax_loss = ivy.transpile(loss, source="numpy", to="jax")

# get some arrays
p = jnp.array([3.0, 2.0, 1.0])
t = jnp.array([0.0, 0.0, 0.0])

# and use the transpiled version!
out = jax_loss(p, t)

I'm using NumPy 

You can use Ivy to get NumPy code from:

Any library

From PyTorch

import ivy
import kornia
import requests
import numpy as np
from PIL import Image

# transpile kornia from torch to np
np_kornia = ivy.transpile(kornia, source="torch", to="numpy")

# get an image
url = "http://images.cocodataset.org/train2017/000000000034.jpg"
raw_img = Image.open(requests.get(url, stream=True).raw)

# convert it to the format expected by kornia
img = np.transpose(np.array(raw_img), (2, 0, 1))
img = np.expand_dims(img, 0) / 255

# and use the transpiled version of any function from the library!
out = np_kornia.enhance.sharpness(img, 5)

From TensorFlow

import ivy
import numpy as np
import os
os.environ["SM_FRAMEWORK"] = "tf.keras"
import segmentation_models as sm

# transpile sm from tensorflow to numpy
np_sm = ivy.transpile(sm, source="tensorflow", to="numpy")

# get some image-like arrays
output = np.random.rand(1, 3, 512, 512).astype(dtype=np.float32)
target = np.random.rand(1, 3, 512, 512).astype(dtype=np.float32)

# and use the transpiled version of any function from the library!
out = np_sm.metrics.iou_score(output, target)

From Jax

import ivy
import rax
import numpy as np

# transpile rax from jax to numpy
np_rax = ivy.transpile(rax, source="jax", to="numpy")

# get some arrays
scores = np.array([2.2, 1.3, 5.4])
labels = np.array([1.0, 0.0, 0.0])

# and use the transpiled version of any function from the library!
out = np_rax.poly1_softmax_loss(scores, labels)

Any function

From PyTorch

import ivy
import torch
import numpy as np

def loss(predictions, targets):
    return torch.sqrt(torch.mean((predictions - targets) ** 2))

# transpile any function from torch to numpy
np_loss = ivy.transpile(loss, source="torch", to="numpy")

# get some arrays
p = np.array([3.0, 2.0, 1.0])
t = np.array([0.0, 0.0, 0.0])

# and use the transpiled version!
out = np_loss(p, t)

From TensorFlow

import ivy
import tensorflow as tf
import numpy as np

def loss(predictions, targets):
    return tf.sqrt(tf.reduce_mean(tf.square(predictions - targets)))

# transpile any function from tf to numpy
np_loss = ivy.transpile(loss, source="tensorflow", to="numpy")

# get some arrays
p = np.array([3.0, 2.0, 1.0])
t = np.array([0.0, 0.0, 0.0])

# and use the transpiled version!
out = np_loss(p, t)

From JAX

import ivy
import jax.numpy as jnp
import numpy as np

def loss(predictions, targets):
    return jnp.sqrt(jnp.mean((predictions - targets) ** 2))

# transpile any function from jax to numpy
np_loss = ivy.transpile(loss, source="jax", to="numpy")

# get some arrays
p = np.array([3.0, 2.0, 1.0])
t = np.array([0.0, 0.0, 0.0])

# and use the transpiled version!
out = np_loss(p, t)