FLAML is a Python library designed for efficient AutoML and hyperparameter tuning, targeting data scientists and ML engineers. It automates machine learning workflows and optimizes AI operations, particularly for LLM-based applications, offering a fast and resource-light approach to finding quality models for common tasks.

How It Works

FLAML automates machine learning and AI operations by orchestrating, automating, and optimizing complex workflows, especially those involving large language models (LLMs). It leverages a cost-effective hyperparameter optimization engine, enabling users to quickly find optimal configurations for LLM inference, MLOps/LMOps pipelines, and traditional ML models. Its approach supports large search spaces with heterogeneous evaluation costs and complex constraints, making it adaptable for various tuning needs.

Quick Start & Requirements

• Install via pip: pip install flaml
• For AutoGen features: pip install "flaml[autogen]"
• Requires Python >= 3.8.
• Documentation: https://microsoft.github.io/FLAML/docs/
• Discord: https://discord.gg/Cppx2vSPVP

Highlighted Details

• Supports automated multi-agent chat frameworks for GPT-X applications.
• Offers drop-in replacements for OpenAI API calls with tuning, caching, and templating.
• Integrates with MLflow and provides zero-shot AutoML for popular estimators like LightGBM and XGBoost.
• Featured in OpenAI's cookbook for cost-effective hyperparameter optimization of LLM inference.

Maintenance & Community

FLAML is a Microsoft-backed project with contributions from academic institutions. It has a dedicated Discord server for community interaction and support.

Licensing & Compatibility

FLAML is released under the MIT License, permitting commercial use and integration with closed-source projects.

Limitations & Caveats

The AutoGen multi-agent chat framework is in preview. While FLAML supports Python 3.11, the core library requires Python >= 3.8.