---

name: luban-cli description: 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.

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

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.