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Ascend Model Migration

Organization
Ascend
ascend-model-migration

Ascend NPU model migration suite. Invoke when user wants to migrate/train models on Ascend NPU, setup environment, or deploy models from open-source repositories.

Overview

PublisherAscend
Repositoryagent-skills
Skill nameascend-model-migration
Stars
54
Forks
4
Bundled files
13
LicenseMulanPSL-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 13 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Ascend on GitHub. Read the source before you install it.

Installation

Install the Ascend Model Migration AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Ascend/agent-skills/tree/master/skills/drivingsdk-ascend-model-migration
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Ascend/agent-skills.git /tmp/agent-skills
mkdir -p .claude/skills
cp -r /tmp/agent-skills/skills/drivingsdk-ascend-model-migration .claude/skills/ascend-model-migration
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ascend Model Migration in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ascend Model Migration on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ascend Model Migration is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Ascend Model Migration Suite

Complete model migration and training suite for Ascend NPU environment. This suite provides end-to-end workflow from SSH connection to model training.

When to Invoke

  • User wants to migrate open-source models to Ascend NPU
  • User needs to setup training environment on Ascend NPU
  • User wants to train models (BEVFormer, etc.) on Ascend NPU
  • User asks about model deployment on Ascend platform

Suite Structure

ascend-model-migration/
├── SKILL.md              # Main skill (this file)
├── ssh-connection/       # SSH connection skill (from ssh-dev-suite)
│   └── SKILL.md
├── model-migration/      # Model migration skill
│   └── SKILL.md
└── model-training/       # Model training skill
    └── SKILL.md

# External Dependencies (in AgentSkills/skills/)
└── ascend-mmlab-install-suite/  # MMLab environment setup
    └── SKILL.md

Workflow Overview

┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│  ssh-connection │ ──► │ mmlab-install   │ ──► │ model-migration │ ──► │ model-training  │
│                 │     │    -suite       │     │                 │     │                 │
│  Connect to     │     │  Install mmcv,  │     │  Clone repo,    │     │  Execute        │
│  Ascend server  │     │  mmdet, mmdet3d │     │  apply patches  │     │  training script│
└─────────────────┘     └─────────────────┘     └─────────────────┘     └─────────────────┘

Complete Workflow

Phase 0: DrivingSDK Preparation

DrivingSDK仓库地址:https://gitcode.com/Ascend/DrivingSDK

在开始任何工作前,确认DrivingSDK已克隆到工作目录:

bash
cd <working_directory>
git clone https://gitcode.com/Ascend/DrivingSDK

重要:DrivingSDK包含所有必要的patch文件和训练脚本,无需向用户询问patch位置。

Phase 1: Environment Information Collection

Ask user for the following information:

1. Server IP address (e.g., 192.168.13.151)
2. SSH username (e.g., root)
3. Docker container name (if applicable, e.g., qianqian_0312)
4. Conda environment name (e.g., qianqian_2)
5. Working directory path (e.g., /home/zqq/0312_trae_new)

Use ssh-connection skill (this suite) to establish connection.

Phase 2: Environment Setup

Use AgentSkills/skills/ascend-mmlab-install-suite/ to install dependencies:

  1. Verify base environment (torch, torch_npu, CANN)
  2. Install MMLab dependencies:
    • mmcv-full (with NPU operators)
    • mmdetection
    • mmsegmentation
    • mmdetection3d
    • detectron2

Phase 3: Model Migration

Use model-migration skill (this suite) to prepare model code:

  1. Clone open-source repository
  2. Apply NPU adaptation patches
  3. Setup dataset and weight links

Phase 4: Model Training

Use model-training skill (this suite) to launch training:

  1. Select training mode (performance/accuracy)
  2. Execute training script
  3. Monitor training progress

Supported Models

ModelStatusDrivingSDK Path
BEVFormer✅ Supportedmodel_examples/BEVFormer
More models🔄 Coming soon-

Quick Start Example

User: "I want to train BEVFormer on Ascend NPU"

Agent workflow:
1. [ssh-connection] Ask for server info → Connect
2. [ascend-mmlab-install-suite] Check environment → Install MMLab deps
3. [model-migration] Clone BEVFormer → Apply patch → Link data
4. [model-training] Launch training → Monitor progress

Key Principles

NPU Compatibility Check

After installation, verify NPU compatibility:

bash
python -c "import site; print([p for p in site.getsitepackages() if 'cuda' in p.lower()])"

Should return empty list [] for NPU environment.

Network Strategy

SituationStrategy
Server can access GitHubDirect git clone on server
Server network timeoutConfigure proxy or clone locally → scp to server
Proxy availableConfigure proxy: export http_proxy=http://proxy:port

重要:所有GitHub仓库克隆都应使用GitHub官方链接,不使用镜像站点。如果网络超时,优先配置代理。

Error Handling

Each sub-skill handles its own errors and provides recovery suggestions. Common issues:

  1. SSH connection failed: Check IP, username, password/key
  2. NPU device not found: Check environment variables
  3. CUDA paths conflict: Reinstall with NPU support
  4. Patch application fails: Check code version

Environment Variables Reference

Common Ascend NPU environment variables:

bash
export ASCEND_SLOG_PRINT_TO_STDOUT=0
export ASCEND_GLOBAL_LOG_LEVEL=3
export TASK_QUEUE_ENABLE=2
export COMBINED_ENABLE=1
export HCCL_WHITELIST_DISABLE=1
export HCCL_CONNECT_TIMEOUT=1200

Note: ASCEND_RT_VISIBLE_DEVICES may be required in specific environments.

Reference Files

  • SSH connection: ssh-connection/SKILL.md (this suite)
  • MMLab install suite: AgentSkills/skills/ascend-mmlab-install-suite/SKILL.md
  • DrivingSDK path: DrivingSDK/model_examples/
  • Model migration: model-migration/SKILL.md (this suite)
  • Model training: model-training/SKILL.md (this suite)

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Ascend Model Migration AI skill do?

Ascend NPU model migration suite. Invoke when user wants to migrate/train models on Ascend NPU, setup environment, or deploy models from open-source repositories.

Why use Ascend Model Migration on TypingMind?

Because you install it once and use it with any model. Ascend Model Migration is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ascend Model Migration in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Ascend/agent-skills/tree/master/skills/drivingsdk-ascend-model-migration. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Ascend Model Migration?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ascend Model Migration?

As many as you like. As long as a model supports skills, you can use Ascend Model Migration with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ascend Model Migration AI skill free?

Yes. It is published on GitHub by Ascend under the MulanPSL-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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