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EagerPy
Writing Code That Works Natively with PyTorch, TensorFlow, JAX, and NumPy
Get Started →
/guide/
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Native Performance
EagerPy operations get directly translated into the corresponding native operations.
title details
Fully Chainable
All functionality is available as methods on the tensor objects and as EagerPy functions.
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Type Checking
Catch bugs before running your code thanks to EagerPy's extensive type annotations.
Copyright © 2020 Jonas Rauber

What is EagerPy?

EagerPy is a Python framework that lets you write code that automatically works natively with PyTorch, TensorFlow, JAX, and NumPy.

import eagerpy as ep

def norm(x):
    x = ep.astensor(x)
    result = x.square().sum().sqrt()
    return result.raw

You can now use the norm function with native tensors and arrays from PyTorch, TensorFlow, JAX and NumPy with virtually no overhead compared to native code. Of course, it also works with GPU tensors.

import torch
norm(torch.tensor([1., 2., 3.]))
# tensor(3.7417)
import tensorflow as tf
norm(tf.constant([1., 2., 3.]))
# <tf.Tensor: shape=(), dtype=float32, numpy=3.7416575>
import jax.numpy as np
norm(np.array([1., 2., 3.]))
# DeviceArray(3.7416575, dtype=float32)
import numpy as np
norm(np.array([1., 2., 3.]))
# 3.7416573867739413

Getting Started

You can install the latest release from PyPI using pip:

python3 -m pip install eagerpy

::: warning NOTE EagerPy requires Python 3.6 or newer. :::