PyCon 2024 showcased a number of ways to speed the pokey Python programming language including sub-interpreters, immortal objects, just-in-time compilation and more.
I’d really like to see Rust fit in where C(++) does now for Python. I know some libraties do it (e.g. Pydantic), but it really should be more common. It should work really well with the GIL… (or the TIL or whatever the new one is)
Well, it is happening, I just don’t know how “blessed” it is by Python maintainers (i.e. are Python releases blocked by Rust binding updates?). It’s 100% possible today and there are projects that use Rust bindings, I just don’t know how that fits in with Python development vs the C++ API.
Or you could use cython, which is much easier to integrate with a python project. It is only marginally slower than Rust but a little less safe. Numpy libraries are usually the fast. Numba is a little clunky, but can also speed up code. There’s lots of options to speed up python code.
You can also use numba if you just need to accelerate one part of the app. We did that with a heavy part of the app and our naïve Python (using numpy) was about as fast as our naïve Rust, but only when wr turned on parallel processing in numba (I could’ve easily beat it with parallel Rust, but that requires extra work and wouldn’t fit as nicely into the rest of the app).
I’d really like to see Rust fit in where C(++) does now for Python. I know some libraties do it (e.g. Pydantic), but it really should be more common. It should work really well with the GIL… (or the TIL or whatever the new one is)
Sounds like an excellent idea, I’d be surprised if it isn’t happening.
Well, it is happening, I just don’t know how “blessed” it is by Python maintainers (i.e. are Python releases blocked by Rust binding updates?). It’s 100% possible today and there are projects that use Rust bindings, I just don’t know how that fits in with Python development vs the C++ API.
Or you could use cython, which is much easier to integrate with a python project. It is only marginally slower than Rust but a little less safe. Numpy libraries are usually the fast. Numba is a little clunky, but can also speed up code. There’s lots of options to speed up python code.
Yup, Cython rocks.
You can also use numba if you just need to accelerate one part of the app. We did that with a heavy part of the app and our naïve Python (using numpy) was about as fast as our naïve Rust, but only when wr turned on parallel processing in numba (I could’ve easily beat it with parallel Rust, but that requires extra work and wouldn’t fit as nicely into the rest of the app).