Version 0.1.0

README.rst

@@ -2,9 +2,13 @@
 EagerPy
 =======
 
-EagerPy is a thin wrapper around **PyTorch** and **TensorFlow Eager** that unifies their interface and thus allows writing code that works with both.
+EagerPy is a thin wrapper around **PyTorch**, **TensorFlow Eager** and
+**NumPy** that unifies their interface and thus allows writing code that
+works natively across all of them.
 
-Warning: this is work in progress; the tests should run through just fine, but lot's of features are still missing. Let me know if this project is useful to you and which features are needed.
+Warning: this is work in progress; the tests should run through just fine,
+but lot's of features are still missing. Let me know if this project is useful
+to you and which features are needed.
 
 Installation
 ------------
@@ -29,9 +33,13 @@ Example
    x = tf.constant([1., 2., 3.])
    x = ep.TensorFlowTensor(x)
 
-   # In both cases, the resulting EagerPy tensor provides the same
-   # interface and a library build on top of the interface provided
-   # by EagerPy will work with both PyTorch and TensorFlow tensors.
+   import numpy as np
+   x = np.array([1., 2., 3.])
+   x = ep.NumPyTensor(x)
+
+   # In all cases, the resulting EagerPy tensor provides the same
+   # interface. This makes it possible to write code that works natively
+   # independent of the underlying framework.
 
    # EagerPy tensors provide a lot of functionality through methods, e.g.
    x.sum()
@@ -42,4 +50,4 @@ Example
    ep.sum(x)
    ep.sqrt(x)
    ep.clip(x, 0, 1)
-   ep.uniform(x, (3, 3), low=-1., high=1.)
+   ep.uniform(x, (3, 3), low=-1., high=1.)  # x is needed to infer the framework

eagerpy/VERSION

@@ -1 +1 @@
-0.0.3
+0.1.0

setup.py

@@ -14,13 +14,13 @@
     README = ""
 
 
-install_requires = []
+install_requires = ["numpy"]
 tests_require = ["pytest", "pytest-cov"]
 
 setup(
     name="eagerpy",
     version=version,
-    description="EagerPy is a thin wrapper around PyTorch and TensorFlow Eager that unifies their interface and thus allows writing code that works with both.",
+    description="EagerPy is a thin wrapper around PyTorch, TensorFlow Eager and NumPy that unifies their interface and thus allows writing code that works natively across all of them.",
     long_description=README,
     long_description_content_type="text/x-rst",
     classifiers=[