Welcome

PyTensor is a Python library that allows you to define, optimize/rewrite, and evaluate mathematical expressions involving multi-dimensional arrays efficiently.

Some of PyTensor’s features are:

• Tight integration with NumPy - Use numpy.ndarray in PyTensor-compiled functions

• Efficient symbolic differentiation - PyTensor efficiently computes your derivatives for functions with one or many inputs

• Speed and stability optimizations - Get the right answer for log(1 + x) even when x is near zero

• Dynamic C/JAX/Numba code generation - Evaluate expressions faster

PyTensor is based on Theano, which has been powering large-scale computationally intensive scientific investigations since 2007.

Warning

Much of the documentation hasn’t been updated and is simply the old Theano documentation.

Download

PyTensor is available on PyPI, and can be installed via pip install PyTensor.

Those interested in bleeding-edge features should obtain the latest development version, available via:

git clone git://github.com/pymc-devs/pytensor.git

You can then place the checkout directory on your $PYTHONPATH or use python setup.py develop to install a .pth into your site-packages directory, so that when you pull updates via Git, they will be automatically reflected the “installed” version. For more information about installation and configuration, see installing PyTensor.

Documentation

Roughly in order of what you’ll want to check out:

• Installing PyTensor – How to install PyTensor.

• Getting Started – What is PyTensor?

• Tutorial – Learn the basics.

• Troubleshooting – Tips and tricks for common debugging.

• API Documentation – PyTensor’s functionality, module by module.

• Frequently Asked Questions – A set of commonly asked questions.

• Optimizations – Guide to PyTensor’s graph optimizations.

• Extending PyTensor – Learn to add a Type, Op, or graph optimization.

• Developer Start Guide – How to contribute code to PyTensor.

• Internal Documentation – How to maintain PyTensor and more…

• Acknowledgements – What we took from other projects.

• Related Projects – link to other projects that implement new functionalities on top of PyTensor

Community

• Visit pytensor-users to discuss the general use of PyTensor with developers and other users

• We use GitHub issues to keep track of issues and GitHub Discussions to discuss feature additions and design changes