Kaggle/kaggle-cli
Kaggle Cli
The Kaggle API command line interface is a suite of utilities for managing datasets, machine learning models, and competition entries from a terminal. It functions as a command line wrapper that translates user input into API calls to control remote cloud resources.
The project differentiates itself by providing specialized tools for automating the execution of notebook kernels and managing the lifecycle of machine learning models, including version iteration and performance tracking. It also includes a utility for executing evaluation tasks against large language models and downloading the resulting performance metrics.
The tool covers several broad capability areas, including dataset management for uploading and downloading data collections, competition entry management for submitting and tracking contest results, and programmatic browsing of community discussion forums.
User identity is managed through token-based client authentication using API keys stored in local configuration files or via a web-based authorization flow.
Features
- Command Line Interfaces - Provides a comprehensive command line tool suite for managing remote cloud resources and Kaggle assets.
- Kaggle API Clients - Provides a comprehensive command line interface for managing datasets, models, and competitions on the Kaggle platform.
- Machine Learning Workflow Libraries - Automates the execution of notebook kernels and the orchestration of machine learning workflow cycles.
- Machine Learning Datasets - Manages the downloading and updating of large data collections used for training and evaluating ML models.
- Model Lifecycle Managers - Provides tools to create, update, download, and delete machine learning models and their versions.
- Competition Submission Tools - Provides a dedicated command line interface for submitting and tracking entries in data science competitions.
- Dataset Management Tools - Offers a suite of tools to create, update, download, and delete data collections for machine learning.
- Notebook Automation Tools - Automates the running and management of code notebooks by downloading source and processing output.
- Competition Management Systems - Provides tools for submitting and tracking entries in both standard and code-based data science contests.
- Kernel Management - Enables the remote management and automated execution of notebook kernels from the terminal.
- Token-Based Authentication - Uses secret API keys stored in local configuration files to authenticate requests to the backend.
- User Authentication Flows - Facilitates user identity verification through API token files and web-based authorization flows.
- Data Science Competitions - Enables programmatic participation in data science challenges through entry submission and result tracking.
- LLM Benchmarking - Provides utilities to execute evaluation tasks against large language models and retrieve performance metrics.
- Model Versioning Systems - Tracks and organizes different iterations of machine learning models to manage performance and deployment versions.
