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Copy file name to clipboardexpand all lines: .github/ISSUE_TEMPLATE.md
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## Description of your problem or feature request
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First, carefully read the following to determine whether or not you have a valid Aesara issue:
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First, carefully read the following to determine whether or not you have a valid Pytensor issue:
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- Does your issue only arise in a library that uses Aesara (e.g. PyMC)? If so, submit your issue to that library's issue tracker. From there, the Aesara-specific details can be worked out and a valid Aesara issue can be identified. **Issues framed primarily in third-party libraries are liable to being marked as invalid and closed.**
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- Does your issue involve OS and/or environment-specific settings (e.g. installation and/or compilation issues)? If so, create a [Discussion](https://github.com/aesara-devs/aesara/discussions) instead. From there, we can help determine whether or not the issue is due to faulty logic in Aesara (i.e. a valid issue) or something specific to your local setup.
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- Does your issue only arise in a library that uses Pytensor (e.g. PyMC)? If so, submit your issue to that library's issue tracker. From there, the Pytensor-specific details can be worked out and a valid Pytensor issue can be identified. **Issues framed primarily in third-party libraries are liable to being marked as invalid and closed.**
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- Does your issue involve OS and/or environment-specific settings (e.g. installation and/or compilation issues)? If so, create a [Discussion](https://github.com/pymc-devs/pytensor/discussions) instead. From there, we can help determine whether or not the issue is due to faulty logic in Pytensor (i.e. a valid issue) or something specific to your local setup.
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If the above does not apply, and you have an issue or feature request that's specific to Aesara, provide a minimal, self-contained, and reproducible example (i.e. an [MWE](https://en.wikipedia.org/wiki/Minimal_reproducible_example)):
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If the above does not apply, and you have an issue or feature request that's specific to Pytensor, provide a minimal, self-contained, and reproducible example (i.e. an [MWE](https://en.wikipedia.org/wiki/Minimal_reproducible_example)):
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```python
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[Your code here]
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## Versions and main components
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*Aesara version:
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*Pytensor version:
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* Python version:
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* Operating system:
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* How did you install Aesara: (conda/pip)
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* How did you install Pytensor: (conda/pip)
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<details> <summary> Aesara config: </summary>
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<details> <summary> Pytensor config: </summary>
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Place the results of `python -c "import aesara; print(aesara.config)"` here.
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Place the results of `python -c "import pytensor; print(pytensor.config)"` here.
Copy file name to clipboardexpand all lines: DESCRIPTION.txt
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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:
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Pytensor 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_. Pytensor features:
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* **tight integration with NumPy:** a similar interface to NumPy's. numpy.ndarrays are also used internally in Aesara-compiled functions.
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* **efficient symbolic differentiation:** Aesara can compute derivatives for functions of one or many inputs.
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* **tight integration with NumPy:** a similar interface to NumPy's. numpy.ndarrays are also used internally in Pytensor-compiled functions.
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* **efficient symbolic differentiation:** Pytensor can compute derivatives for functions of one or many inputs.
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* **speed and stability optimizations:** avoid nasty bugs when computing expressions such as log(1 + exp(x)) for large values of x.
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* **dynamic C code generation:** evaluate expressions faster.
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* **extensive unit-testing and self-verification:** includes tools for detecting and diagnosing bugs and/or potential problems.
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