Welcome — PyTensor dev documentation (original) (raw)

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 usepython setup.py develop to install a .pth into your site-packagesdirectory, 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