jax-md VS jax-experiments

Compare jax-md vs jax-experiments and see what are their differences.

jax-md

Differentiable, Hardware Accelerated, Molecular Dynamics (by jax-md)
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jax-experiments

By PWhiddy
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jax-md jax-experiments
2 1
1,093 2
- -
7.5 3.5
17 days ago 8 months ago
Jupyter Notebook Jupyter Notebook
Apache License 2.0 MIT License
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jax-md

Posts with mentions or reviews of jax-md. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-09-28.

jax-experiments

Posts with mentions or reviews of jax-experiments. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-09-28.
  • JAX – NumPy on the CPU, GPU, and TPU, with great automatic differentiation
    12 projects | news.ycombinator.com | 28 Sep 2023
    Jax is super useful for scientific computing. Although nbody sims might not be the best application. A naive nbody sim is very easy to implement and accelerate in jax (here’s my version: https://github.com/PWhiddy/jax-experiments/blob/main/nbody.i...), but it can be tricky to scale it. This is because efficient nbody sims usually either rely on trees or spatial hashing/sorting which are tricky to efficiently implement with jax.

What are some alternatives?

When comparing jax-md and jax-experiments you can also consider the following projects:

torchmd - End-To-End Molecular Dynamics (MD) Engine using PyTorch

jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

jaxonnxruntime - A user-friendly tool chain that enables the seamless execution of ONNX models using JAX as the backend.

thinc - 🔮 A refreshing functional take on deep learning, compatible with your favorite libraries

tinygrad - You like pytorch? You like micrograd? You love tinygrad! ❤️ [Moved to: https://github.com/tinygrad/tinygrad]

equinox - Elegant easy-to-use neural networks + scientific computing in JAX. https://docs.kidger.site/equinox/

diffrax - Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/

autograd - Efficiently computes derivatives of numpy code.

jax-md vs torchmd jax-experiments vs jax jax-md vs jaxonnxruntime jax-experiments vs thinc jax-md vs tinygrad jax-experiments vs jaxonnxruntime jax-md vs thinc jax-experiments vs equinox jax-md vs diffrax jax-experiments vs autograd jax-md vs equinox jax-md vs jax