A Data Scientist’s Perspective: Rust vs Python
Rust has been gaining traction in the data science field. So should you, as a data scientist start paying attention to it? Short answer: as an individual data scientist keeping up with the field — Yes. As a data scientist working in the industry — Not Yet.
While Rust is a powerful system programming language with numerous advantages, there are several factors to consider when comparing it to Python in the context of machine learning. Here’s a case for and against using Rust for ML:
Case for using Rust
1. Performance: Rust’s main advantage lies in its performance and memory safety guarantees. It offers low-level control over system resources, allowing for efficient memory management and reduced runtime overhead. This can be beneficial when dealing with large-scale or computationally intensive ML models, where performance optimizations are crucial.
2. Concurrency and parallelism: Rust’s ownership system and built-in concurrency features make it well-suited for leveraging multiple CPU cores and executing tasks in parallel. This can be advantageous when dealing with complex ML workflows that involve parallel processing, such as distributed training or inference on large datasets.
3. Safety and reliability: Rust’s strict compile-time checks and memory safety features make it less prone to common programming errors like null pointer dereferences, buffer overflows, and data races. This level of safety can be crucial when deploying ML models in production, ensuring stability and minimizing the risk of runtime errors or crashes.
4. Ecosystem and integration: Although Rust’s machine learning ecosystem is still evolving, it offers libraries like `rustlearn` and `tch-rs` that provide bindings to popular ML frameworks such as TensorFlow and PyTorch. Rust’s interoperability with C and Python also enables seamless integration with existing Python codebases.
Case against using Rust
1. Development speed and ease: Python’s extensive ecosystem, rich libraries (e.g., NumPy, Pandas, scikit-learn), and mature ML frameworks (e.g., TensorFlow, PyTorch) make it an incredibly productive language for developing machine learning models. Python’s readability and ease of use contribute to faster prototyping, experimentation, and iteration, which are crucial during the early stages of ML model development.
2. ML-specific tooling and community support: Python has established itself as the go-to language for machine learning due to its vast range of ML-specific libraries, tools, and frameworks. The Python community offers a wealth of resources, tutorials, and forums dedicated to ML, making it easier to find solutions to common challenges and collaborate with other practitioners.
3. Deployment and serving infrastructure: Python excels in deploying and serving ML models, thanks to frameworks like Flask, Django, FastAPI, and cloud platforms that provide seamless integration and scalability. These tools, coupled with containerization technologies like Docker, simplify the process of deploying ML models to production environments and building robust serving systems.
4. Team expertise and maintenance: Considering the popularity of Python in the ML community, it is likely that your team members are more experienced with Python than Rust. Choosing Python ensures easier knowledge sharing, code reviews, and maintenance, as well as the availability of skilled Python developers who can contribute to ML projects.
Final Verdict
In conclusion, while Rust offers compelling advantages in terms of performance, safety, and concurrency, Python’s extensive ecosystem, ease of use, ML-specific tooling, and community support make it a strong choice for building, deploying, and serving production ML models.
For Teams
Rust may be a good option for teams to experiment with from time to time. However, unless you are confident that Rust’s low-level control is needed in your ML systems, it may not be a good significant time investment
For the Individual Data Scientist
Rust has been gaining significant momentum. So it may be a good use of your time to spend a weekend getting comfortable with the language, and some Rust specific packages for ML development.
This post was created with help from ChatGPT
