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sage.tensor.modules: Add backends using TensorFlow Core, PyTorch, SymPy #30096
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Setting new milestone based on a cursory review of ticket status, priority, and last modification date. |
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@mkoeppe, can you please let us know more about this? |
The tensors in Sage can be defined with different element types (this is what is called a "base ring" in Sage). When talking about using Pytorch or Tensorflow as the backend, the most natural thing to consider is the element type being a floating-point number of some precision / format. When talking about SymPy as the backend, the most natural thing to consider is symbolic expressions. |
Tensors from
sage.tensor
are stored assage.tensor.modules.Components
, which is a dictionary with index tuples as keys.We propose to create additional backends for numerical coordinate rings:
They provide efficient storage and GPU-accelerated computations for numerical tensors.
For SymPy, see #31946
CC: @egourgoulhon
Component: linear algebra
Issue created by migration from https://trac.sagemath.org/ticket/30096
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