dist-op-dev
OfficialEnd-to-end dist-op development workflow.
Software Engineering#git#workflow#analysis#unit-testing#integration-testing#distributed-operators#yaml-registration
Authormindspore-ai
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This workflow accelerates the end-to-end development of HyperParallel distributed operators by providing a structured plan that covers analysis, implementation, testing, and Git flow.
Core Features & Use Cases
- Automated operator analysis to derive interface and distribution strategies.
- Python implementation scaffolding with infer_layout and get_expand_impl (when needed).
- YAML registration generation to expose the distributed operator across MindSpore and PyTorch.
- UT and ST test scaffolding to validate layout inference and distribution behavior.
- Git workflow integration for feature branches and PRs.
Quick Start
Invoke this SKILL to drive the complete dist-op development workflow for a given operator.
Dependency Matrix
Required Modules
None requiredComponents
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: dist-op-dev Download link: https://github.com/mindspore-ai/hyper-parallel/archive/main.zip#dist-op-dev Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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