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https://github.com/aesara-devs/aesara

Aesara is a Python library for defining, optimizing, and efficiently evaluating mathematical expressions involving multi-dimensional arrays.
https://github.com/aesara-devs/aesara

aesara automatic-differentiation optimizing-compiler optimizing-compilers symbolic-computation tensors term-rewriting-system theano transpiler

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Aesara is a Python library for defining, optimizing, and efficiently evaluating mathematical expressions involving multi-dimensional arrays.

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README 

        



logo



[![Pypi][pypi-badge]][pypi]

[![Downloads][downloads-badge]][releases]

[![Contributors][contributors-badge]][contributors]

 

[![Gitter][gitter-badge]][gitter]

[![Discord][discord-badge]][discord]

[![Twitter][twitter-badge]][twitter]



Aesara is a Python library that allows one to define, optimize, and

efficiently evaluate mathematical expressions involving multi-dimensional

arrays.



*A fast and hackable meta-tensor library in Python*



[Features](#features) •

[Get Started](#get-started) •

[Install](#install) •

[Get help](#get-help) •

[Contribute](#contribute)



[Roadmap](https://github.com/orgs/aesara-devs/projects/3) •

[Docs](https://aesara.readthedocs.io/en/latest/)






> [!IMPORTANT]

> This project is no longer actively maintained.  See https://github.com/aesara-devs/aesara/discussions/1521#discussioncomment-7687831 and https://x.com/BrandonTWillard/status/1729350499793588249 for more details.



## Features



- A hackable, pure-Python codebase

- Extensible graph framework suitable for rapid development of custom operators and symbolic optimizations

- Implements an extensible graph transpilation framework that currently provides

  compilation via C, [JAX](https://github.com/google/jax), and [Numba](https://github.com/numba/numba).

- Based on one of the most widely-used Python tensor libraries: [Theano](https://github.com/Theano/Theano).



Aesara Overview Diagram: A graph linking the different components of Aesara. From left to right: Numpy API->Symbolic Graph<->Rewrites->Optimize/Stabilize->[C, Jax, Numba]



## Get started



``` python

import aesara

from aesara import tensor as at



# Declare two symbolic floating-point scalars

a = at.dscalar("a")

b = at.dscalar("b")



# Create a simple example expression

c = a + b



# Convert the expression into a callable object that takes `(a, b)`

# values as input and computes the value of `c`.

f_c = aesara.function([a, b], c)



assert f_c(1.5, 2.5) == 4.0



# Compute the gradient of the example expression with respect to `a`

dc = aesara.grad(c, a)



f_dc = aesara.function([a, b], dc)



assert f_dc(1.5, 2.5) == 1.0



# Compiling functions with `aesara.function` also optimizes

# expression graphs by removing unnecessary operations and

# replacing computations with more efficient ones.



v = at.vector("v")

M = at.matrix("M")



d = a/a + (M + a).dot(v)



aesara.dprint(d)

# Elemwise{add,no_inplace} [id A] ''

#  |InplaceDimShuffle{x} [id B] ''

#  | |Elemwise{true_divide,no_inplace} [id C] ''

#  |   |a [id D]

#  |   |a [id D]

#  |dot [id E] ''

#    |Elemwise{add,no_inplace} [id F] ''

#    | |M [id G]

#    | |InplaceDimShuffle{x,x} [id H] ''

#    |   |a [id D]

#    |v [id I]



f_d = aesara.function([a, v, M], d)



# `a/a` -> `1` and the dot product is replaced with a BLAS function

# (i.e. CGemv)

aesara.dprint(f_d)

# Elemwise{Add}[(0, 1)] [id A] ''   5

#  |TensorConstant{(1,) of 1.0} [id B]

#  |CGemv{inplace} [id C] ''   4

#    |AllocEmpty{dtype='float64'} [id D] ''   3

#    | |Shape_i{0} [id E] ''   2

#    |   |M [id F]

#    |TensorConstant{1.0} [id G]

#    |Elemwise{add,no_inplace} [id H] ''   1

#    | |M [id F]

#    | |InplaceDimShuffle{x,x} [id I] ''   0

#    |   |a [id J]

#    |v [id K]

#    |TensorConstant{0.0} [id L]



```



See [the Aesara documentation][documentation] for in-depth tutorials.



## Install



The latest release of Aesara can be installed from PyPI using ``pip``:



``` python

pip install aesara

```



Or via conda-forge:



``` python

conda install -c conda-forge aesara

```



The current development branch of Aesara can be installed from GitHub, also using ``pip``:



``` python

pip install git+https://github.com/aesara-devs/aesara

```



## Get help



Report bugs by opening an [issue][issues]. If you have a question regarding the usage of Aesara, start a [discussion][discussions]. For real-time feedback or more general chat about Aesara use our [Discord server][discord], or [Gitter][gitter].



## Contribute



We welcome bug reports and fixes and improvements to the documentation.



For more information on contributing, please see the

[contributing guide](https://github.com/aesara-devs/aesara/blob/main/.github/CONTRIBUTING.md)

and the Aesara [Mission Statement](https://github.com/aesara-devs/aesara/blob/main/doc/mission.rst).



A good place to start contributing is by looking through [the issues][issues].



## Support



Special thanks to [Bram Timmer](http://beside.ca) for the logo.



[contributors]: https://github.com/aesara-devs/aesara/graphs/contributors

[contributors-badge]: https://img.shields.io/github/contributors/aesara-devs/aesara?style=flat-square&logo=github&logoColor=white&color=ECEFF4

[discussions]: https://github.com/aesara-devs/aesara/discussions

[documentation]: https://aesara.readthedocs.io/en/latest

[downloads-badge]: https://img.shields.io/pypi/dm/aesara?style=flat-square&logo=pypi&logoColor=white&color=8FBCBB

[discord]: https://discord.gg/h3sjmPYuGJ

[discord-badge]: https://img.shields.io/discord/1072170173785723041?color=81A1C1&logo=discord&logoColor=white&style=flat-square

[gitter]: https://gitter.im/aesara-devs/aesara

[gitter-badge]: https://img.shields.io/gitter/room/aesara-devs/aesara?color=81A1C1&logo=matrix&logoColor=white&style=flat-square

[issues]: https://github.com/aesara-devs/aesara/issues

[releases]: https://github.com/aesara-devs/aesara/releases

[twitter]: https://twitter.com/AesaraDevs

[twitter-badge]: https://img.shields.io/twitter/follow/AesaraDevs?style=social

[pypi]: https://pypi.org/project/aesara/

[pypi-badge]: https://img.shields.io/pypi/v/aesara?color=ECEFF4&logo=python&logoColor=white&style=flat-square