Rust for Machine Learning

[PrinceSajjadHussain]

What are the biggest challenges and opportunities when using Rust for ML development?

[XavierRolex]

Dear @PrinceSajjadHussain,

Rust has great potential for ML, especially for performance-critical or edge deployments. The biggest challenge right now is the lack of mature libraries compared to Python — you won't find the same level of support as with TensorFlow or PyTorch. Data handling (like what pandas does) is also more manual in Rust.

That said, Rust shines when it comes to safety, speed, and deploying models in production, especially in systems where reliability matters. If you're okay with a bit more setup and lower-level control, it's a great tool to explore, and the ecosystem is growing fast.

[PrinceSajjadHussain]

Thanks a lot for your thoughtful insight! 🙌
You’re absolutely right — the maturity gap in libraries is a real bottleneck right now, especially when you’re coming from the Python ecosystem. But I’m genuinely excited about how Rust is evolving in the ML space — especially with projects like burn, ndarray, and tch-rs gaining traction.

I agree 100% on Rust’s strengths in performance and safety — it feels like the ideal language for deploying compact, fast, and reliable ML models at the edge or in production-critical environments. I’m currently experimenting with building lightweight inference pipelines in Rust, and while the road’s a bit rougher, the control and speed are worth it.

Would love to stay connected and exchange ideas as the Rust ML ecosystem matures! 🚀
Are there any specific projects or crates you’ve found especially promising?

[PrinceSajjadHussain]

You’re right that Rust holds great promise for machine learning, especially where performance, safety, and reliability are critical, such as in production or edge deployments. While it currently lacks the mature, extensive libraries that Python enjoys with TensorFlow or PyTorch, the Rust ML ecosystem is rapidly growing with notable libraries like SmartCore, Linfa, RustLearn, and tch-rs (Rust bindings for PyTorch), which cover a broad range of traditional ML and deep learning needs. Data handling in Rust is more manual compared to Python’s pandas, but libraries like ndarray provide powerful numerical computing capabilities akin to NumPy. Rust excels in performance, often delivering 10-100x speedups over Python in compute-intensive scenarios, with the added benefits of memory safety and concurrency without locks. If you are comfortable with more setup and lower-level control, Rust is an excellent choice for building scalable, safe, and fast ML systems, especially for production environments where deployment simplicity and reliability matter. The community and tooling are evolving quickly, making it a promising space to explore alongside Python for ML workflows. In summary, Rust complements Python well: Python remains the leader for rapid prototyping and research with mature APIs, while Rust shines in production-grade, performance-critical ML applications. Hope this helps!

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On Thu, May 29, 2025 at 02:40 Xavier Rolex ***@***.***> wrote: Dear @PrinceSajjadHussain <https://github.com/PrinceSajjadHussain>, Rust has great potential for ML, especially for performance-critical or edge deployments. The biggest challenge right now is the lack of mature libraries compared to Python — you won't find the same level of support as with TensorFlow or PyTorch. Data handling (like what pandas does) is also more manual in Rust. That said, Rust shines when it comes to safety, speed, and deploying models in production, especially in systems where reliability matters. If you're okay with a bit more setup and lower-level control, it's a great tool to explore, and the ecosystem is growing fast. — Reply to this email directly, view it on GitHub <#3098 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/APZBBEUJBSHZDPEUQGEQERL3AYULHAVCNFSM6AAAAAB37GFQVKVHI2DSMVQWIX3LMV43URDJONRXK43TNFXW4Q3PNVWWK3TUHMYTGMZQGI2TGMQ> . You are receiving this because you were mentioned.Message ID: ***@***.***>

[PrinceSajjadHussain]

Thank you for your insightful overview of Rust’s potential in machine learning. I completely agree that while Rust’s ecosystem isn’t as mature as Python’s, its strengths in safety, speed, and reliability make it a compelling choice for production and edge deployments. I’m particularly interested in exploring Rust’s growing libraries like Linfa and tch-rs, and I appreciate the point about data handling being more manual — it’s a trade-off I’m willing to navigate for the performance benefits. Your perspective on Rust complementing Python for ML workflows resonates with me, as I see Rust as a powerful tool to optimize and deploy models once prototyping is done. Looking forward to diving deeper into Rust’s ML ecosystem and contributing to its growth!

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On Thu, May 29, 2025 at 02:40 Xavier Rolex ***@***.***> wrote: Dear @PrinceSajjadHussain <https://github.com/PrinceSajjadHussain>, Rust has great potential for ML, especially for performance-critical or edge deployments. The biggest challenge right now is the lack of mature libraries compared to Python — you won't find the same level of support as with TensorFlow or PyTorch. Data handling (like what pandas does) is also more manual in Rust. That said, Rust shines when it comes to safety, speed, and deploying models in production, especially in systems where reliability matters. If you're okay with a bit more setup and lower-level control, it's a great tool to explore, and the ecosystem is growing fast. — Reply to this email directly, view it on GitHub <#3098 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/APZBBEUJBSHZDPEUQGEQERL3AYULHAVCNFSM6AAAAAB37GFQVKVHI2DSMVQWIX3LMV43URDJONRXK43TNFXW4Q3PNVWWK3TUHMYTGMZQGI2TGMQ> . You are receiving this because you were mentioned.Message ID: ***@***.***>