12/27/2023 0 Comments Google swift converterPython actually acts as a sort of metaprogramming language for the underlying Tensorflow graph. For example, in Tensorflow graph mode, which is the only performant mode in the library, your Python code doesn't generally run when you think it would. The unfortunate reality is that the creators of machine learning libraries have had to make certain development choices in the name of performance, and that has complicated matters a bit. Having your Python code call lower level code is not as easy as mapping Python's functions to C functions. This is the opposite of what's desirable in an area as dynamic as machine learning, where so much is still not settled, and new ideas are very much needed. This leads to the unfortunate situation in which programmers are motivated to write the least amount of sophisticated code they can, and default to calling external library operations. Most programmers choose not to do so, either because they have no experience with writing low level performant code, or because jumping back and forth between Python's development environment and some low level language's environment becomes too cumbersome to justify. Writing a custom way to perform convolutions, for example, becomes off limits unless the developer is willing to step down into a language like C. External binariesĬalling external binaries for every compute-intensive operation limits developers to working on a small portion of the algorithm's surface area. For the most part, this has worked really well, but as with all abstractions, it can create some problems. To get around these facts, most machine learning projects run their compute-intensive algorithms via libraries written in C/C++/Fortran/CUDA, and use Python to glue the different low-level operations together. Also, Python is not great for parallelism. Python is by far the most used language in machine learning, and Google has a ton of machine learning libraries and tools written in it. Check it out What is wrong with you, Python?! We expanded on this blog post, and showed how building a real model looks like. We hosted a Swift for Machine Learning live webinar Furthermore, even though we mainly work with Python at Tryolabs, we think that choosing Swift was a superb idea, and decided to write this short™ post to help spread the word about Google's plans.īut before we get into Swift and what the term differentiable programming actually means, we should first review the current state of affairs. This is unfortunate though, as even a cursory glance at Google's project will show that it's a massive and ambitious undertaking, which could establish Swift as a key player in the area. This can be attributed in part to the choice of language, which has largely been met with confusion and indifference, as Swift has almost no presence in the data science ecosystem and has mainly been used for building iOS apps. The scope and initial results of the project have been remarkable, and general public usability is not very far off.ĭespite this, the project hasn't received a lot of interest in the machine learning community and remains unknown to most practitioners. Two years ago, a small team at Google started working on making Swift the first mainstream language with first-class language-integrated differentiable programming capabilities.
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