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AdrianEddy
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burn-tracing-backend

Profile tensor operations and generate interactive HTML performance visualizations.

8|1|Updated Mar 24, 2026
Search Tags:#performance#profiling#visualization#tensor#ML optimization#fusion
One-click install
npx skills add https://github.com/AdrianEddy/burn-tracing-backend --skill burn-tracing-backend
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: burn-tracing-backend
Source: https://github.com/AdrianEddy/burn-tracing-backend/tree/main
Command: npx skills add https://github.com/AdrianEddy/burn-tracing-backend --skill burn-tracing-backend

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires burn-tracing-backend, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides detailed tracing and visualization of tensor operations, helping developers identify performance bottlenecks and fusion efficiency issues in machine learning models.

Core Features & Use Cases

  • Operation profiling: Records CPU timing, fusion events, and synchronization points during tensor computations.
  • Interactive visualizations: Generates HTML-based timelines, memory usage charts, and operation lists for performance analysis.
  • Use Case: Optimize a deep learning training pipeline by pinpointing slow fusion kernels and excessive syncs, then refining the model code accordingly.

Quick Start

Use the burn-tracing backend to trace a model inference and generate an HTML report for performance review.

Frequently Asked Questions about burn-tracing-backend

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

FAQPage Schema
How do I identify performance bottlenecks in tensor operations during deep learning training?▼

To identify performance bottlenecks in tensor operations, you can profile CPU timing, fusion events, and synchronization points to pinpoint slow kernels and excessive syncs for model optimization.

What is the best way to visualize GPU and CPU performance analysis for machine learning models?▼

The best way to visualize GPU and CPU performance analysis is by generating interactive HTML visualizations that display timelines, memory usage charts, and operation lists for your ML workflows.

How do I trace model inference to review fusion efficiency issues?▼

You trace model inference to review fusion efficiency by recording operation profiling data during tensor computations and generating an interactive HTML report for performance review.

Do I need the burn-tracing-backend to debug and optimize deep learning workflows?▼

Yes, you need the burn-tracing-backend to debug and optimize deep learning workflows, as it is required to generate the interactive HTML visualizations for detailed GPU and CPU performance analysis.

Can I generate memory usage charts for tensor computations using this profiling approach?▼

Yes, you can generate memory usage charts for tensor computations alongside HTML-based timelines and operation lists to analyze performance bottlenecks and fusion events in your models.

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