| 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:
- 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.
