Pytato: Get Descriptions of Array Computations via Lazy Evaluation
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Imagine TensorFlow, but aimed at HPC. Produces a data flow graph, where the
edges carry arrays and the nodes are (give or take) static-control programs
that compute array outputs from inputs, possibly (but not necessarily)
expressed in `Loopy <https://github.com/inducer/loopy>`__. A core assumption is
that the graph represents a computation that's being repeated often enough that
it is worthwhile to do expensive processing on it (code generation, fusion,
OpenCL compilation, etc).

* `Documentation <https://documen.tician.de/pytato>`__ (read how things work, see an example)
* `Github <https://github.com/inducer/pytato>`__ (get latest source code, file bugs)

Pytato is licensed to you under the MIT/X Consortium license. See
the `documentation <https://documen.tician.de/pytato/misc.html>`__
for further details.

Numpy compatibility
-------------------

Pytato is written to pose no particular restrictions on the version of numpy
used for execution. To use mypy-based type checking on Pytato itself or
packages using Pytato, numpy 1.20 or newer is required, due to the
typing-based changes to numpy in that release. Furthermore, pytato
now uses type promotion rules aiming to match those in
`numpy 2 <https://numpy.org/devdocs/numpy_2_0_migration_guide.html#changes-to-numpy-data-type-promotion>`__.
This will not break compatibility with older numpy versions, but may
result in differing data types between computations carried out in
numpy and pytato.