Open mkoeppe opened 4 years ago
Setting new milestone based on a cursory review of ticket status, priority, and last modification date.
Description changed:
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@@ -1,9 +1,10 @@
Tensors from `sage.tensor` are stored as `sage.tensor.modules.Components`, which is a dictionary with index tuples as keys.
-We propose to create additional backends.
+We propose to create additional backends for numerical coordinate rings:
- https://www.tensorflow.org/guide/tensor
- https://pytorch.org/docs/stable/tensors.html
-They provide efficient storage and GPU-accelerated computations.
+They provide efficient storage and GPU-accelerated computations for numerical tensors.
+For SymPy, see #31946
@mkoeppe, can you please let us know more about this? Do we just have to implement all the function in Component class in Pytorch / Tensorflow? What i am understanding is we have to impliment symbolic expressions in tensors, am i mistaken?
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