domain-ml

Running ONNX inference in Rust with GPU support involves loading the model and executing tensors across GPU backends. This workflow applies design patterns like lazy initialization and batching to ensure memory-safe, portable inference.

Building a scalable ML inference server in Rust requires modular data paths, batching, and streaming. Using crates like candle or tch-rs, you can manage memory efficiently while serving predictions across CPU and GPU backends.

Choosing between candle, burn, and tch-rs depends on your backend needs and memory constraints. Candle offers lightweight Rust-native inference, burn provides a flexible neural network framework, and tch-rs binds to PyTorch C++ APIs for training and inference.

Rust supports both ML training pipelines and inference. By leveraging crates like burn and tch-rs alongside deterministic seeds and streaming data paths, you can build robust training pilots and data processing tools.

Memory-safe tensor processing in Rust requires lazy initialization, batching, and streaming design patterns. Enforcing modular data paths and deterministic seeds ensures safe memory management during model loading and inference.

Tract supports ONNX models for CPU-based inference in Rust. It allows you to load and run models without external dependencies, fitting into modular data paths for portable, memory-efficient execution.