new_user55

11 ms·

1. 1.▲
   by new_user55 2mo ago

   You can write the cuda kernels and directly differentiate them using Enzyme. Last time I checked KOKKOS was still WIP: https://github.com/EnzymeAD/Enzyme/issues?q=kokkos
2. 2.▲
   by new_user55 2mo ago

   Really nice work. I love Enzyme, and used it in my project about differentiable atomic descriptors. Idea was that I can quickly gobble up existing C++ and fortran codes alike for atomic descriptors and create a encompassing library what dif
3. 3.▲
   by new_user55 5mo ago

   I will guess for linux. Most out of the box linux laptops I saw were intel based. I guess open source support of intel is best among others in the industry. Even in my current thinkpad first thing I did was to replace its wifi module from r
4. 4.▲
   by new_user55 6mo ago

   Its really cool! If you don't mind me asking, does it support variable size inputs? I am bit confused about JAX in that regards. I am trying for long to run JAX stablehlo models in C++ for inference. However dynamic shapes were still a
5. 5.▲
   by new_user55 3y ago

   We wanted to use ONNX runtime for a "model driver" for MD simulations, where any ML model can be used for molecular dynamics simulations. Problem was it was way too immature. Like ceiling function will only work with single precis
6. 6.▲
   by new_user55 3y ago

   Tinygrad is python only right? Can it provide gradients during C++ runtime as well? ONNX runtime have multiple language backends for inference.
7. 7.▲
   by new_user55 4y ago

   There is [torch-md]( https://github.com/torchmd/torchmd )