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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]

Metadata

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