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Aesara is a Python library that allows one to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays.


  • 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, and Numba
  • Based on one of the most widely-used Python tensor libraries: Theano

Getting started

import aesara
from aesara import tensor as aet

# Declare two symbolic floating-point scalars
a = aet.dscalar("a")
b = aet.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 = aet.vector("v")
M = aet.matrix("M")

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

# Elemwise{add,no_inplace} [id A] ''
#  |InplaceDimShuffle{x} [id B] ''
#  | |Elemwise{true_div,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)
# 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 for in-depth tutorials.


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

pip install aesara

Or via conda-forge:

conda install -c conda-forge aesara

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

pip install git+


Many Aesara developers are also PyMC developers, and, since the PyMC developers operate under the NumFOCUS umbrella, if you want to support them financially, consider donating here.

Special thanks to Bram Timmer for the logo.