ML in Rust

12 pages in this section.

How Rust's ML crates share a tensor/computation-graph execution model, and why the ecosystem's goals - inference, embedding, safety - differ from Python's research-first design.

Explainer Rust Machine Learning Tensors

ML in Rust Basics

Explore 10 Rust machine learning examples, from loading ONNX models with ort to tokenizing text for LLMs with Hugging Face.

Rust Ml In Rust Basics Machine Learning

candle

Explore Candle, Hugging Face's Rust deep-learning framework. Learn to use tensors, neural network modules, and run transformer inference without Python.

Rust Ml In Rust Candle Deep Learning

burn

Explore Burn, a deep-learning framework for Rust. Learn to train and infer models with pluggable backends and strong type safety.

Rust Ml In Rust Burn Training

ONNX Runtime (ort)

Run ONNX models in Rust with ONNX Runtime. Learn to deploy models from Python, standardize inference, and use CUDA/TensorRT execution providers.

Rust Ml In Rust Onnx Runtime

Tokenizers

Learn to use the Hugging Face tokenizers crate in Rust for fast BPE/WordPiece pipelines, including padding, truncation, and batch encoding.

Rust Ml In Rust Tokenizers Nlp

LLM Inference & Serving

Rust Ml In Rust Advanced Llm

Embeddings & Vector Search

Rust Ml In Rust Advanced Embeddings

GPU & Acceleration

Rust Ml In Rust Advanced Gpu

Serving Models with Axum

Serve ML models over HTTP with Axum 0.8. Learn to manage shared model state, validate requests, and ensure non-blocking execution.

Rust Ml In Rust Serving Models

ML in Rust Best Practices

Achieve reproducible, fast, and memory-safe ML inference and serving in Rust. Learn best practices for model lifecycle, correctness, and performance.

Rust Ml In Rust Best Practices List

ML in Rust Highlights Summary

A single-page roundup of every highlight bullet from the 11 pages in the ML in Rust section, grouped by source page so you can scan all 55 takeaways without opening each article individually.

Highlights Summary Ml In Rust

ML in Rust

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ML in Rust