EagerPy operations get directly translated into the corresponding native operations.
All functionality is available as methods on the tensor objects and as EagerPy functions.
Catch bugs before running your code thanks to EagerPy's extensive type annotations.
# What is EagerPy?
EagerPy is a Python framework that lets you write code that automatically works natively with PyTorch (opens new window), TensorFlow (opens new window), JAX (opens new window), and NumPy (opens new window).
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 (opens new window) using
python3 -m pip install eagerpy
EagerPy requires Python 3.6 or newer.