Python has, for many years been the de facto language when it comes to machine learning in general because of its simplicity as well as having a nice ecosystem of libraries like TensorFlow and PyTorch. But what's now becoming an equally prominent player in the programming world - and widely lauded for performance, among other qualities-is Rust. As machine learning projects are getting complex and performance-sensitive, many people wonder if Rust can beat Python on these tasks. This paper outlines strengths and weaknesses in usage of Rust and Python about ML tasks and whether Rust will or not substitute Python on the ML tasks that shall come after this period.
The greatest advantage Rust has over Python is that Rust is much faster. Rust is a systems programming language designed to be as fast as C or C++. It compiles into machine code, so Rust programs execute much more quickly than the scripts used by Python, which rely on an interpreter. Rust can be very important for big datasets or for heavy computational tasks in machine learning.
While slower, Python has managed to remain relevant in machine learning by interfacing with faster languages, such as C and C++. Those libraries, NumPy and PyTorch, use those languages behind the scenes to handle the computation-intensive parts of machine learning, so, despite its inherent speed limitations, the lead that Python enjoys is more or less maintained. Where Rust wins is that it gives higher performance throughout the stack, hence being more efficient for systems that must have speed from all levels.
The second big strength of Rust is its strong memory safety guarantees without sacrificing performance. Rust, with its borrow checker and ownership model, prevents null pointer dereferencing or data races that are very common memory issues. Especially for machine learning models, where often huge parallel data is operated upon, Rust's concurrency with no risk of memory errors definitely places it on a different pedestal for building reliable, scalable ML applications.
In contrast, Python really doesn't support parallelism much due to the Global Interpreter Lock, GIL. While Python is said to support multi-threading, it does not allow multiple threads to execute Python bytecode at the same time because of the GIL. This may cause performance bottlenecks in multi-core systems. Most developers use external libraries or tools like multiprocessing to overcome this, whereas Rust's inherent concurrency model gives a much smoother and safer way to parallel computing.
One of the largest factors why Python is so prevalent in machine learning is its very rich ecosystem of libraries. There are well-established ML frameworks for Python, such as TensorFlow, PyTorch, and scikit-learn, that have drastically reduced development time. They have been optimized and fine-tuned for many years to become suitable for both beginners and experts on a wide range of machine learning tasks, from deep learning to data preprocessing.
Comparison wise, the ecosystem of Rust remains tiny and holds several very promising machine learning libraries; for example, Linfa and Tch (a Rust binding for PyTorch). This one is less mature or at least, is not that widespread as the equivalent Python versions. That creates obstacles for practicing machine learning: robust ready-to-use libraries just cannot be so readily available here.
But the Rust ecosystem is growing, and if the performance benefits push developers into building more powerful ML libraries in the future, there might be a decrease in the gap between Python and Rust over time as more developers embrace Rust for machine learning.
This is why Python is learned by such a huge population of developers, starting with novices and going on to experts. High readability and vast documentation for Python, making it one of the most sought-after languages for many beginners. There are many tutorials, resources, and support that the active community of Python can offer to one who is getting started with ML very quickly.
Rust, however, has a steeper learning curve. Its ownership model and memory management can be challenging for developers unfamiliar with systems programming. However, Rust's growing popularity and strong community support are gradually making it more accessible. As more educational resources become available, Rust's adoption in fields like machine learning could increase.
Rust has the potential to surpass Python for machine learning projects, especially because of its performance, memory safety, and efficient parallelism. It is an attractive option for high-performance machine learning systems due to its speed advantage and ability to handle complex, multi-threaded applications without the risk of memory issues. However, Python remains the dominant language in the ML space because of its rich ecosystem of libraries and ease of use.
While Rust cannot replace Python in the very near future, it certainly is gaining momentum. Where specific machine learning tasks have high performance and concurrency requirements, Rust might become a strong candidate. As the Rust ecosystem grows and more developers take advantage of the benefits, Rust will likely become a more prominent language in machine learning.