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onnx-inference

Name: onnx-inference
Availability: InStock
Author: JNZader-Vault

Deploys ML models across Python, Rust, and Go using ONNX Runtime with quantization and graph transformations for edge scenarios.

Updated Feb 2, 2026

Search Tags:#python #go #rust #inference #model optimization #onnx #ml deployment #edge ml

Ready to run

npx skills add https://github.com/JNZader-Vault/project-starter-framework --skill onnx-inference

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: onnx-inference
Source: https://github.com/JNZader-Vault/project-starter-framework/tree/main/.ai-config/skills/data-ai/onnx-inference
Command: npx skills add https://github.com/JNZader-Vault/project-starter-framework --skill onnx-inference

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill streamlines the deployment of machine learning models by leveraging ONNX Runtime for efficient, cross-platform inference, reducing complexity and improving performance.

Core Features & Use Cases

Cross-Platform Deployment: Deploy models to Python, Rust, and Go environments seamlessly.
Optimized Inference: Utilizes ONNX Runtime for high-performance execution on various hardware.
Model Optimization: Includes quantization and optimization techniques for edge deployment.
Use Case: Deploy a trained PyTorch image classification model to a Rust application for real-time analysis on an edge device.

Quick Start

Use the onnx-inference skill to deploy the model located at 'models/image_classifier.onnx' for Python inference.

Frequently Asked Questions about onnx-inference

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I deploy ML models for inference across Python, Go, and Rust?▼

You can deploy ML models for cross-platform inference using ONNX Runtime, which enables high-performance execution of trained models seamlessly across Python, Go, and Rust environments.

What is the best way to optimize ML models for edge computing?▼

Optimizing ML models for edge computing involves applying quantization and graph transformations to reduce model size and latency. ONNX Runtime facilitates these optimizations for high-performance execution on edge hardware.

Can I run real-time inference on an edge device using ONNX?▼

Yes, you can run real-time inference on edge devices using ONNX. Exporting trained models to ONNX format enables high-performance execution and hardware optimization for edge computing scenarios.

Do I need to export my PyTorch model to ONNX format before deployment?▼

You need to export your trained model to ONNX format before deployment. This Skill requires ONNX model export compatibility and ONNX Runtime libraries to facilitate cross-platform inference in Python, Rust, or Go.

How does ONNX Runtime improve model inference performance?▼

ONNX Runtime improves model inference performance by utilizing hardware-specific accelerators and applying graph optimizations. It streamlines execution across various platforms, reducing complexity during machine learning deployment.