It’s very hard to profile or debug the deployment version of the code, and there’s no guarantee it will even run identically to the python version. But these deployment approaches can’t support all of Python’s features, so Python programmers have to learn to use a subset of the language that matches their deployment target. For instance, AI models often have to be converted from Python into a faster implementation, such as ONNX or torchscript. Nearly all AI models today are developed in Python, thanks to the flexible and elegant programming language, fantastic tools and ecosystem, and high performance compiled libraries.īut this “two-language” approach has serious downsides. Libraries like Numpy and PyTorch provide “pythonic” interfaces to high performance code, allowing Python programmers to feel right at home, even as they’re using highly optimised numeric libraries. So Python programmers learn to avoid using Python for the implementation of performance-critical sections, instead using Python wrappers over C, FORTRAN, Rust, etc code. However, Python has a trick up its sleeve: it can call out to code written in fast languages. This makes it impractical to use Python for the performance-sensitive parts of code – the inner loops where performance is critical. But Python is many thousands of times slower than languages like C++. But it comes with a downside: performance.Ī few percent here or there doesn’t matter. This approach means that Python can (and does) do absolutely anything. It has an elegant core, on which everything else is built. Python is the language that I have used for nearly all my work over the last few years. Before I explain why I’m so excited about Mojo, I first need to say a few things about Python.

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