Skip to content

Facilitate gradient interoperability between other ml frameworks (TensorFlow, Torch, etc) and jax #3

Description

@phinate

To use the setup as it is now, one would need to define their neural network (or whatever parametric observable you like that yields histograms) using only jax, which allows the tracing of gradients. This is generally seen as a limitation for two reasons:

  • New users will not want to learn yet another way to do neural networks
  • Model implementations that are non-trivially portable between frameworks have no way to link with the functionality of the library

This issue outlines the kind of syntax and operations needed to enable this, but jax is still in it's infancy, so there is no good user-facing API for this yet. It seems like it could be possible! (:

Activity

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment

Metadata

Metadata

Assignees

No one assigned

    Labels

    enhancementNew feature or requesthelp wantedExtra attention is needed

    Type

    No type

    Projects

    No projects

      Milestone

      No milestone

      Relationships

      None yet

      Development

      No branches or pull requests

      Issue actions