OpenClaw

luban-cli

Development and management of the Luban CLI for MLOps. Use this skill when building or using the Luban CLI to manage experiment environments, training tasks, and online services.

guunergooner0 来源安装数☆ 0 仓库 Stars

查看来源 ↗安装与使用 SKILL.md ↗

安装与使用

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求，再添加到工作流。

SkillHub CLI

skillhub install guunergooner/luban-cli

打开来源仓库，查看说明和兼容环境后，按来源提供的命令或步骤安装。

将来源链接提供给你的 Agent，让它先读取说明、核对依赖，并向你说明将执行的安装步骤。

查看来源 ↗使用指南

技能说明

Luban CLI Skill

This skill provides a structured framework for developing and using the Luban CLI, a specialized tool for MLOps management.

Core Functionality

The Luban CLI focuses on three primary MLOps pillars:

Experiment Environments (env): Management of development workspaces.
Training Tasks (job): Orchestration of model training workloads.
Online Services (svc): Deployment and scaling of inference services.

Development Workflow

When developing or extending the Luban CLI, follow these steps:

Initialize Project: Use the boilerplate in templates/cli_boilerplate.py as a starting point for the CLI structure.
Define Commands: Refer to references/mlops_guide.md for the standard command patterns and required attributes for each entity.
Implement CRUD: Ensure every entity (env, job, svc) supports the full lifecycle:
- Create: Provisioning new resources.
- Read: Listing and describing existing resources.
- Update: Modifying configurations or scaling.
- Delete: Cleaning up resources.

Usage Patterns

Managing Environments

luban env list
luban env create --name research-v1 --image pytorch:2.0

Managing Training Jobs

luban job create --script train.py --gpu 1
luban job status --id job_001

Managing Online Services

luban svc create --model-path ./models/v1 --replicas 3
luban svc scale --id my-service --replicas 5

Resources

templates/cli_boilerplate.py: A Python-based CLI structure using argparse.
references/mlops_guide.md: Detailed specifications for MLOps entities and operations.

来源信息

Source: github
Repository: openclaw/skills
Path: skills/guunergooner/luban-cli
Version: —
Commit: ffb0017b3ce63e709639f4802d2018b432ce8956
分类来源: rules
状态: active
可见性: public

这些信息来自收录时的来源分析，可能滞后；使用前请核对最新来源文件。

来源内容

SKILL.md

# Luban CLI Skill

This skill provides a structured framework for developing and using the **Luban CLI**, a specialized tool for MLOps management.

## Core Functionality

The Luban CLI focuses on three primary MLOps pillars:
1. **Experiment Environments (`env`)**: Management of development workspaces.
2. **Training Tasks (`job`)**: Orchestration of model training workloads.
3. **Online Services (`svc`)**: Deployment and scaling of inference services.

## Development Workflow

When developing or extending the Luban CLI, follow these steps:

1. **Initialize Project**: Use the boilerplate in `templates/cli_boilerplate.py` as a starting point for the CLI structure.
2. **Define Commands**: Refer to `references/mlops_guide.md` for the standard command patterns and required attributes for each entity.
3. **Implement CRUD**: Ensure every entity (`env`, `job`, `svc`) supports the full lifecycle:
- **Create**: Provisioning new resources.
- **Read**: Listing and describing existing resources.
- **Update**: Modifying configurations or scaling.
- **Delete**: Cleaning up resources.

## Usage Patterns

### Managing Environments
```bash
luban env list
luban env create --name research-v1 --image pytorch:2.0
```

### Managing Training Jobs
```bash
luban job create --script train.py --gpu 1
luban job status --id job_001
```

### Managing Online Services
```bash
luban svc create --model-path ./models/v1 --replicas 3
luban svc scale --id my-service --replicas 5
```

## Resources
- `templates/cli_boilerplate.py`: A Python-based CLI structure using `argparse`.
- `references/mlops_guide.md`: Detailed specifications for MLOps entities and operations.

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