trainwave-cli 0.4.1

Trainwave CLI - A command-line interface for managing Trainwave resources

Trainwave CLI

A command-line interface for interacting with Trainwave's machine learning platform. This CLI tool provides functionality for managing training jobs, handling authentication, managing secrets, and configuring your Trainwave environment.

Features

• Job Management: Create, monitor, and manage training jobs
• Authentication: Secure authentication with Trainwave's platform
• Secrets Management: Handle sensitive information for your jobs
• Configuration: Set up and manage your Trainwave environment

Installation

Using pip

pip install trainwave-cli

From Source

1. Clone the repository
2. Install using uv:

uv sync

Usage

The CLI provides several main commands:

wave jobs     # Manage training jobs
wave auth     # Authenticate with Trainwave
wave config   # Configure your Trainwave environment
wave secrets  # Manage job secrets

For detailed help on any command:

wave --help
wave <command> --help

Development

This project uses uv for dependency management and packaging. Here's how to set up your development environment:

Prerequisites

• Python 3.10 or higher
• uv
• Make (for development commands)

Setting Up Development Environment

1. Clone the repository
2. Install development dependencies:

make install-dev

Development Commands

The project includes several Make targets to help with development:

• make install: Install production dependencies
• make install-dev: Install development dependencies
• make clean: Remove temporary files and build artifacts
• make ruff: Run Ruff linter
• make format: Format code using Black and Ruff
• make check: Run all code quality checks
• make test: Run the test suite
• make help: Show available make commands

Code Style

The project uses:

• Ruff for linting
• Black for code formatting
• Type hints are required for all functions

Running Tests

make test

Or directly with pytest:

pytest tests/

Project Structure

trainwave-cli/
├── trainwave_cli/     # Main package directory
├── tests/            # Test files
├── example/          # Example configurations and usage
├── pyproject.toml    # Project metadata and dependencies
└── Makefile         # Development automation

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Run tests and linting: make check
5. Submit a pull request

License

[License information here]