Does this skill support GPU acceleration?

Yes, it includes specific constraints and design patterns for utilizing CUDA and Metal via frameworks like candle and tch-rs, including async data loading to keep the GPU busy.

How should I handle model loading in a web server context?

It implements the singleton pattern using OnceLock, ensuring that heavy ML models are loaded into memory only once and reused across all incoming requests.

Which Rust ML framework is best for inference-only projects?

For lightweight and portable inference, the skill recommends using 'tract' with ONNX models as it is highly optimized for production environments.

How do I prevent memory issues when handling large tensors in Rust?

The skill provides patterns for using ndarray views and zero-copy operations to avoid cloning large datasets, which is the primary cause of memory bandwidth bottlenecks.

Rust Machine Learning & AI

Name: Rust Machine Learning & AI
Author: lywa1998

bylywa1998

0•

Data Science & ML

Guides the development of high-performance ML and AI applications in Rust using memory-efficient patterns and GPU acceleration.

The domain-ml skill provides specialized expertise for building production-grade machine learning systems within the Rust ecosystem. It focuses on solving common domain challenges like memory bandwidth bottlenecks, GPU utilization, and model portability. By providing implementation patterns for frameworks such as Candle, Burn, and Tract, the skill ensures developers follow best practices for zero-copy tensor operations, singleton model loading, and efficient batch processing to maximize performance and reliability in AI-driven applications.

Key Features

010 GitHub stars

02Framework selection guidance for Tract, Candle, Burn, and tch-rs

03Production-ready patterns for singleton model loading and batch inference

04Memory-efficient design patterns using zero-copy and tensor views

05Standardized ONNX model integration and portability workflows

06GPU acceleration strategies for CUDA and Metal backends

Use Cases

01Building high-throughput inference servers for ONNX-based models

02Developing native Rust neural networks with GPU-accelerated training

03Creating low-latency data preprocessing pipelines for large-scale datasets

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add lywa1998/self-host-claude-marketplace domain-ml

For use in Claude.ai and ChatGPT

Download Skill