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Very curious how this compares to JAX [1]. JAX lets you write Python code that executes on Nvidia, but also GPUs of other brands (support varies). It similarly
by crazygringo 2y ago
Very curious how this compares to JAX [1].
JAX lets you write Python code that executes on Nvidia, but also GPUs of other brands (support varies). It similarly has drop-in replacements for NumPy functions.
This only supports Nvidia. But can it do things JAX can't? It is easier to use? Is it less fixed-size-array-oriented? Is it worth locking yourself into one brand of GPU?
[1] https://github.com/jax-ml/jax https://github.com/jax-ml/jax
- odo1242 2y agoWell, the idea is that you’d be writing low level CUDA kernels that implement operations not already implemented by JAX/CUDA and integrate them into existing projects. Numba[1] is probably the closest thing I can think of that currently exists. (In fact, looking at it right now, it seems this effort from Nvidia is actually based on Numba) [1]: https://numba.readthedocs.io/en/stable/cuda/overview.html https://numba.readthedocs.io/en/stable/cuda/overview.html