Submitted by jkterry1 t3_11twq6s in MachineLearning

Jumpy 1.0 is now live, and the project is stable and mature.

Jumpy is a lightweight project for easily switching between Jax and Numpy functions that can serve as a drop-in replacement for Jax. This allows for writing one codebase that can use either backend, allowing for creating codebases that work with either data structure type or easier debugging of code. This project is already being used in Gymnasium to create environment wrappers that can support both Numpy and Jax-based hardware accelerated environments. We plan to continue improving the project with support for PyTorch functions, all Numpy functions and more functionality to support enabling or disabling different backends

You can read the full release notes here: https://github.com/Farama-Foundation/Jumpy/releases/tag/1.0.0

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limpbizkit4prez t1_jclabza wrote

What's value in using this instead of "jax.numpy as np"?

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r_linux_mod_isahoe t1_jclc6rp wrote

porting an existing codebase to jax? Using any existing algorithm that's implemented in numpy but on jax backend? The opportunities are massive

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limpbizkit4prez t1_jcld70o wrote

Yeah, but if I have a code base written in numpy and want to use jax, wouldn't I need to do the same amount of refactoring to integrate this as I would with regular jax? Are there a lot of functions in numpy that don't exist in jax.numpy?

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LappenX t1_jcln1pc wrote

You don't want to use jax without jit.

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jkterry1 OP t1_jcm0qqj wrote

What do you mean? This allows you do use it if you want

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