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training-migration

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Migrate PyTorch training to Ascend NPU for speed.

Software Engineering#PyTorch#checkpointing#distributed-training#AMP#NPU#training-migration#Ascend
AuthorFeRhodium
Version1.0.0
Installs0

System Documentation

What problem does it solve?

This Skill guides the migration of CUDA-based PyTorch training code to Ascend NPU, enabling optimized training with AMP, distributed execution, and robust checkpointing.

Core Features & Use Cases

  • Training loop migration for NPU-accelerated training
  • Mixed-precision training with torch_npu AMP
  • Data loading and preprocessing optimizations for NPU
  • Distributed training setup using HCCL
  • Gradient accumulation and clipping for large batches
  • Checkpointing and resume across runs
  • Performance monitoring and profiling for Ascend

Quick Start

Begin by scanning your PyTorch project for training scripts, configure the migration workflow, and execute the migration. After migration, run the produced training code with AMP enabled, verify results with a lightweight test run, and use profiling hooks to validate performance improvements.

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: training-migration
Download link: https://github.com/FeRhodium/ascend-migration/archive/main.zip#training-migration

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