Numba caching

Enabling caching for numba functions to reduce cold-starts

statsforecast makes heavy use of numba to speed up several critical functions that estimate model parameters. This comes at a cost though, which is that the functions have to be JIT compiled the first time they’re run, which can be expensive. Once a function has ben JIT compiled, subsequent calls are significantly faster. One problem is that this compilation is saved (by default) on a per-session basis.

In order to mitigate the compilation overhead numba offers the option to cache the function compiled code to a file, which can be then reused across sessions, and even copied over to different machines that share the same CPU characteristics (more information).

To leverage caching, you can set the NIXTLA_NUMBA_CACHE environment variable (e.g. NIXTLA_NUMBA_CACHE=1), which will enable caching for all functions. By default the cache is saved to the __pycache__ directory, but you can override this with the NUMBA_CACHE_DIR environment variable to save it to a different path (e.g. NUMBA_CACHE_DIR=numba_cache), you can find more information in the docs.

If you want to have this enabled for all your sessions, we suggest adding export NIXTLA_NUMBA_CACHE=1 to your profile files, such as .bashrc, .zshrc, etc.