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align=\"center\"\u003e\n\n\u003cimg src=\"./doc/images/aesara_logo_2400.png\" alt=\"logo\"\u003e\u003c/img\u003e\n\n[![Pypi][pypi-badge]][pypi]\n[![Downloads][downloads-badge]][releases]\n[![Contributors][contributors-badge]][contributors]\n \u003c/br\u003e\n[![Gitter][gitter-badge]][gitter]\n[![Discord][discord-badge]][discord]\n[![Twitter][twitter-badge]][twitter]\n\nAesara is a Python library that allows one to define, optimize, and\nefficiently evaluate mathematical expressions involving multi-dimensional\narrays.\n\n*A fast and hackable meta-tensor library in Python*\n\n[Features](#features) •\n[Get Started](#get-started) •\n[Install](#install) •\n[Get help](#get-help) •\n[Contribute](#contribute)\n\n[Roadmap](https://github.com/orgs/aesara-devs/projects/3) •\n[Docs](https://aesara.readthedocs.io/en/latest/)\n\n\u003c/div\u003e\n\n\u003e [!IMPORTANT]\n\u003e This project is no longer actively maintained.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.\n\n## Features\n\n- A hackable, pure-Python codebase\n- Extensible graph framework suitable for rapid development of custom operators and symbolic optimizations\n- Implements an extensible graph transpilation framework that currently provides\n  compilation via C, [JAX](https://github.com/google/jax), and [Numba](https://github.com/numba/numba).\n- Based on one of the most widely-used Python tensor libraries: [Theano](https://github.com/Theano/Theano).\n\n\u003cimg src=\"./doc/images/aesara_overview_diagram.png\" alt=\"Aesara Overview Diagram: A graph linking the different components of Aesara. From left to right: Numpy API-\u003eSymbolic Graph\u003c-\u003eRewrites-\u003eOptimize/Stabilize-\u003e[C, Jax, Numba]\"\u003e\u003c/img\u003e\n\n## Get started\n\n``` python\nimport aesara\nfrom aesara import tensor as at\n\n# Declare two symbolic floating-point scalars\na = at.dscalar(\"a\")\nb = at.dscalar(\"b\")\n\n# Create a simple example expression\nc = a + b\n\n# Convert the expression into a callable object that takes `(a, b)`\n# values as input and computes the value of `c`.\nf_c = aesara.function([a, b], c)\n\nassert f_c(1.5, 2.5) == 4.0\n\n# Compute the gradient of the example expression with respect to `a`\ndc = aesara.grad(c, a)\n\nf_dc = aesara.function([a, b], dc)\n\nassert f_dc(1.5, 2.5) == 1.0\n\n# Compiling functions with `aesara.function` also optimizes\n# expression graphs by removing unnecessary operations and\n# replacing computations with more efficient ones.\n\nv = at.vector(\"v\")\nM = at.matrix(\"M\")\n\nd = a/a + (M + a).dot(v)\n\naesara.dprint(d)\n# Elemwise{add,no_inplace} [id A] ''\n#  |InplaceDimShuffle{x} [id B] ''\n#  | |Elemwise{true_divide,no_inplace} [id C] ''\n#  |   |a [id D]\n#  |   |a [id D]\n#  |dot [id E] ''\n#    |Elemwise{add,no_inplace} [id F] ''\n#    | |M [id G]\n#    | |InplaceDimShuffle{x,x} [id H] ''\n#    |   |a [id D]\n#    |v [id I]\n\nf_d = aesara.function([a, v, M], d)\n\n# `a/a` -\u003e `1` and the dot product is replaced with a BLAS function\n# (i.e. CGemv)\naesara.dprint(f_d)\n# Elemwise{Add}[(0, 1)] [id A] ''   5\n#  |TensorConstant{(1,) of 1.0} [id B]\n#  |CGemv{inplace} [id C] ''   4\n#    |AllocEmpty{dtype='float64'} [id D] ''   3\n#    | |Shape_i{0} [id E] ''   2\n#    |   |M [id F]\n#    |TensorConstant{1.0} [id G]\n#    |Elemwise{add,no_inplace} [id H] ''   1\n#    | |M [id F]\n#    | |InplaceDimShuffle{x,x} [id I] ''   0\n#    |   |a [id J]\n#    |v [id K]\n#    |TensorConstant{0.0} [id L]\n\n```\n\nSee [the Aesara documentation][documentation] for in-depth tutorials.\n\n## Install\n\nThe latest release of Aesara can be installed from PyPI using ``pip``:\n\n``` python\npip install aesara\n```\n\nOr via conda-forge:\n\n``` python\nconda install -c conda-forge aesara\n```\n\n\nThe current development branch of Aesara can be installed from GitHub, also using ``pip``:\n\n``` python\npip install git+https://github.com/aesara-devs/aesara\n```\n\n\n## Get help\n\nReport 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].\n\n## Contribute\n\nWe welcome bug reports and fixes and improvements to the documentation.\n\nFor more information on contributing, please see the\n[contributing guide](https://github.com/aesara-devs/aesara/blob/main/.github/CONTRIBUTING.md)\nand the Aesara [Mission Statement](https://github.com/aesara-devs/aesara/blob/main/doc/mission.rst).\n\nA good place to start contributing is by looking through [the issues][issues].\n\n## Support\n\nSpecial thanks to [Bram Timmer](http://beside.ca) for the logo.\n\n[contributors]: https://github.com/aesara-devs/aesara/graphs/contributors\n[contributors-badge]: https://img.shields.io/github/contributors/aesara-devs/aesara?style=flat-square\u0026logo=github\u0026logoColor=white\u0026color=ECEFF4\n[discussions]: https://github.com/aesara-devs/aesara/discussions\n[documentation]: https://aesara.readthedocs.io/en/latest\n[downloads-badge]: https://img.shields.io/pypi/dm/aesara?style=flat-square\u0026logo=pypi\u0026logoColor=white\u0026color=8FBCBB\n[discord]: https://discord.gg/h3sjmPYuGJ\n[discord-badge]: https://img.shields.io/discord/1072170173785723041?color=81A1C1\u0026logo=discord\u0026logoColor=white\u0026style=flat-square\n[gitter]: https://gitter.im/aesara-devs/aesara\n[gitter-badge]: https://img.shields.io/gitter/room/aesara-devs/aesara?color=81A1C1\u0026logo=matrix\u0026logoColor=white\u0026style=flat-square\n[issues]: https://github.com/aesara-devs/aesara/issues\n[releases]: https://github.com/aesara-devs/aesara/releases\n[twitter]: https://twitter.com/AesaraDevs\n[twitter-badge]: https://img.shields.io/twitter/follow/AesaraDevs?style=social\n[pypi]: https://pypi.org/project/aesara/\n[pypi-badge]: https://img.shields.io/pypi/v/aesara?color=ECEFF4\u0026logo=python\u0026logoColor=white\u0026style=flat-square\n","funding_links":[],"categories":["Python","Linear Algebra / Statistics Toolkit"],"sub_categories":["General Purpose Tensor Library"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faesara-devs%2Faesara","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Faesara-devs%2Faesara","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faesara-devs%2Faesara/lists"}