Metadata-Version: 2.1
Name: aesara-nightly
Version: 2.3.4.dev20220117
Summary: Optimizing compiler for evaluating mathematical expressions on CPUs and GPUs.
Home-page: https://github.com/aesara-devs/aesara
Author: aesara-devs
Author-email: aesara.devs@gmail.com
License: BSD
Keywords: aesara math numerical symbolic blas numpy gpu autodiff differentiation
Platform: Windows
Platform: Linux
Platform: Solaris
Platform: Mac OS-X
Platform: Unix
Classifier: Development Status :: 6 - Mature
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development :: Code Generators
Classifier: Topic :: Software Development :: Compilers
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Description-Content-Type: text/x-rst
License-File: LICENSE.txt

Aesara is a Python library that allows you to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays. It is built on top of NumPy_. Aesara features:

 * **tight integration with NumPy:** a similar interface to NumPy's. numpy.ndarrays are also used internally in Aesara-compiled functions.
 * **transparent use of a GPU:** perform data-intensive computations up to 140x faster than on a CPU (support for float32 only).
 * **efficient symbolic differentiation:** Aesara can compute derivatives for functions of one or many inputs.
 * **speed and stability optimizations:** avoid nasty bugs when computing expressions such as log(1 + exp(x)) for large values of x.
 * **dynamic C code generation:** evaluate expressions faster.
 * **extensive unit-testing and self-verification:** includes tools for detecting and diagnosing bugs and/or potential problems.

.. _NumPy: http://numpy.scipy.org/


