Metadata-Version: 2.1
Name: eagerpy
Version: 0.27.0
Summary: EagerPy is a thin wrapper around PyTorch, TensorFlow Eager, JAX and NumPy that unifies their interface and thus allows writing code that works natively across all of them.
Home-page: https://github.com/jonasrauber/eagerpy
Author: Jonas Rauber
Author-email: jonas.rauber@bethgelab.org
License: UNKNOWN
Description: 
        .. image:: https://badge.fury.io/py/eagerpy.svg
           :target: https://badge.fury.io/py/eagerpy
        
        .. image:: https://codecov.io/gh/jonasrauber/eagerpy/branch/master/graph/badge.svg
           :target: https://codecov.io/gh/jonasrauber/eagerpy
        
        .. image:: https://img.shields.io/badge/code%20style-black-000000.svg
           :target: https://github.com/ambv/black
        
        ======================================================================================
        EagerPy: PyTorch, TensorFlow, JAX and NumPy — all of them natively using the same code
        ======================================================================================
        
        `EagerPy <https://eagerpy.jonasrauber.de>`_ is a **Python framework** that let's you write code that automatically works natively with `PyTorch <https://pytorch.org>`_, `TensorFlow <https://www.tensorflow.org>`_, `JAX <https://github.com/google/jax>`_, and `NumPy <https://numpy.org>`_. EagerPy is **also great when you work with just one framework** but prefer a clean and consistent API that is fully chainable, provides extensive type annotions and let's you write beautiful code.
        
        
        🔥 Design goals
        ----------------
        
        - **Native Performance**: EagerPy operations get directly translated into the corresponding native operations.
        - **Fully Chainable**: All functionality is available as methods on the tensor objects and as EagerPy functions.
        - **Type Checking**: Catch bugs before running your code thanks to EagerPy's extensive type annotations.
        
        
        📖 Documentation
        -----------------
        
        Learn more about in the `documentation <https://eagerpy.jonasrauber.de>`_.
        
        
        🚀 Quickstart
        --------------
        
        .. code-block:: bash
        
           pip install eagerpy
        
        
        🎉 Example
        -----------
        
        .. code-block:: python
        
           import torch
           x = torch.tensor([1., 2., 3., 4., 5., 6.])
        
           import tensorflow as tf
           x = tf.constant([1., 2., 3., 4., 5., 6.])
        
           import jax.numpy as np
           x = np.array([1., 2., 3., 4., 5., 6.])
        
           import numpy as np
           x = np.array([1., 2., 3., 4., 5., 6.])
        
           # No matter which framwork you use, you can use the same code
           import eagerpy as ep
        
           # Just wrap a native tensor using EagerPy
           x = ep.astensor(x)
        
           # All of EagerPy's functionality is available as methods
           x = x.reshape((2, 3))
           x.flatten(start=1).square().sum(axis=-1).sqrt()
           # or just: x.flatten(1).norms.l2()
        
           # and as functions (yes, we gradients are also supported!)
           loss, grad = ep.value_and_grad(loss_fn, x)
           ep.clip(x + eps * grad, 0, 1)
        
           # You can even write functions that work transparently with
           # Pytorch tensors, TensorFlow tensors, JAX arrays, NumPy arrays
        
           def my_universal_function(a, b, c):
               # Convert all inputs to EagerPy tensors
               a, b, c = ep.astensors(a, b, c)
        
               # performs some computations
               result = (a + b * c).square()
        
               # and return a native tensor
               return result.raw
        
        
        🗺 Use cases
        ------------
        
        `Foolbox Native <https://github.com/bethgelab/foolbox>`_, the latest version of
        Foolbox, a popular adversarial attacks library, has been rewritten from scratch
        using EagerPy instead of NumPy to achieve native performance on models
        developed in PyTorch, TensorFlow and JAX, all with one code base.
        
        
        🐍 Compatibility
        -----------------
        
        We currently test with the following versions:
        
        * PyTorch 1.4.0
        * TensorFlow 2.1.0
        * JAX 0.1.57
        * NumPy 1.18.1
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Description-Content-Type: text/x-rst
Provides-Extra: testing
