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I’m only really thinking about ML/scientific computing workflows where all the heavy lifting happens in jax/torch/polars.


right and in those cases, a python JIT will do nothing for your performance because all the computation is happening in C/CUDA anyway.


It depends on whether you’re transforming data out of files or whatever to get it into these libraries to start with to be fair. Overall I wouldn’t expect that to have an effect on a long-running computation but when starting up a project it can be a bit slow.




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