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architecture-analysis

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Analyze PyTorch models for Ascend NPU readiness.

Software Engineering#architecture#code analysis#PyTorch#distributed training#Ascend NPU#CUDA migration#NPU compatibility
AuthorFeRhodium
Version1.0.0
Installs0

System Documentation

What problem does it solve?

Architecture Analysis helps identify CUDA patterns, model structure, and distributed training setups that complicate or block migration to Ascend NPU. It provides a clear view of migration requirements and potential blockers early in the process.

Core Features & Use Cases

  • CUDA pattern detection: finds device placements, CUDA APIs, and custom kernels that need to be migrated.
  • Model structure assessment: evaluates layers, attention blocks, normalization, and activation usage for Ascend compatibility.
  • Distributed training analysis: inspects DDP/FSDP usage, process groups, and data parallel patterns to map to NPU-friendly equivalents.
  • Migration readiness planning: surfaces required API changes, data movement strategies, and potential performance implications.

Quick Start

Analyze a PyTorch project to generate a migration plan for Ascend NPU readiness. Example: analyze /path/to/repo for Ascend NPU compatibility.

Dependency Matrix

Required Modules

None required

Components

Standard package

💻 Claude Code Installation

Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.

Please help me install this Skill:
Name: architecture-analysis
Download link: https://github.com/FeRhodium/ascend-migration/archive/main.zip#architecture-analysis

Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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