I've been using JAX (https://jax.readthedocs.io/en/latest/) for scientific computing in general (in particular MCMC algorithms), as it's really fast. Even on a CPU you get massive speedups compared to numpy (can be up to 2 or 3 orders of magnitude faster in some cases).
The main selling point of the library is automatic differential and compilation to XLA, but I've been using it even when I don't need gradients, as it's really fast (due to compilation). I also really like the random number generator as it's very good for reproducibility.
I've played around with Julia in the past and really liked it, but in terms speed Jax has pretty much solved that problem for me
The main selling point of the library is automatic differential and compilation to XLA, but I've been using it even when I don't need gradients, as it's really fast (due to compilation). I also really like the random number generator as it's very good for reproducibility.
I've played around with Julia in the past and really liked it, but in terms speed Jax has pretty much solved that problem for me