A Fast Library for Automated Machine Learning & Tuning

🔥 The automated multi-agent chat framework in autogen is in preview from v2.0.0.

🔥 FLAML is highlighted in OpenAI's cookbook.

🔥 autogen is released with support for ChatGPT and GPT-4, based on Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference.

🔥 FLAML supports Code-First AutoML & Tuning – Private Preview in Microsoft Fabric Data Science.

What is FLAML

FLAML is a lightweight Python library for efficient automation of machine learning and AI operations. It automates workflow based on large language models, machine learning models, etc. and optimizes their performance.

• FLAML enables building next-gen GPT-X applications based on multi-agent conversations with minimal effort. It simplifies the orchestration, automation and optimization of a complex GPT-X workflow. It maximizes the performance of GPT-X models and augments their weakness.
• For common machine learning tasks like classification and regression, it quickly finds quality models for user-provided data with low computational resources. It is easy to customize or extend. Users can find their desired customizability from a smooth range.
• It supports fast and economical automatic tuning (e.g., inference hyperparameters for foundation models, configurations in MLOps/LMOps workflows, pipelines, mathematical/statistical models, algorithms, computing experiments, software configurations), capable of handling large search space with heterogeneous evaluation cost and complex constraints/guidance/early stopping.

FLAML is powered by a series of research studies from Microsoft Research and collaborators such as Penn State University, Stevens Institute of Technology, University of Washington, and University of Waterloo.

FLAML has a .NET implementation in ML.NET, an open-source, cross-platform machine learning framework for .NET.

Installation

FLAML requires Python version >= 3.8. It can be installed from pip:

pip install flaml

Minimal dependencies are installed without extra options. You can install extra options based on the feature you need. For example, use the following to install the dependencies needed by the autogen package.

pip install "flaml[autogen]"

Find more options in Installation. Each of the notebook examples may require a specific option to be installed.

Quickstart

• (New) The autogen package enables the next-gen GPT-X applications with a generic multi-agent conversation framework. It offers customizable and conversable agents which integrate LLMs, tools and human. By automating chat among multiple capable agents, one can easily make them collectively perform tasks autonomously or with human feedback, including tasks that require using tools via code. For example,

from flaml import autogen
assistant = autogen.AssistantAgent("assistant")
user_proxy = autogen.UserProxyAgent("user_proxy")
user_proxy.initiate_chat(assistant, message="Show me the YTD gain of 10 largest technology companies as of today.")
# This initiates an automated chat between the two agents to solve the task

Autogen also helps maximize the utility out of the expensive LLMs such as ChatGPT and GPT-4. It offers a drop-in replacement of openai.Completion or openai.ChatCompletion with powerful functionalites like tuning, caching, templating, filtering. For example, you can optimize generations by LLM with your own tuning data, success metrics and budgets.

# perform tuning
config, analysis = autogen.Completion.tune(
    data=tune_data,
    metric="success",
    mode="max",
    eval_func=eval_func,
    inference_budget=0.05,
    optimization_budget=3,
    num_samples=-1,
)
# perform inference for a test instance
response = autogen.Completion.create(context=test_instance, **config)

• With three lines of code, you can start using this economical and fast AutoML engine as a scikit-learn style estimator.

from flaml import AutoML
automl = AutoML()
automl.fit(X_train, y_train, task="classification")

• You can restrict the learners and use FLAML as a fast hyperparameter tuning tool for XGBoost, LightGBM, Random Forest etc. or a customized learner.

automl.fit(X_train, y_train, task="classification", estimator_list=["lgbm"])

• You can also run generic hyperparameter tuning for a custom function.

from flaml import tune
tune.run(evaluation_function, config={…}, low_cost_partial_config={…}, time_budget_s=3600)

• Zero-shot AutoML allows using the existing training API from lightgbm, xgboost etc. while getting the benefit of AutoML in choosing high-performance hyperparameter configurations per task.

from flaml.default import LGBMRegressor

# Use LGBMRegressor in the same way as you use lightgbm.LGBMRegressor.
estimator = LGBMRegressor()
# The hyperparameters are automatically set according to the training data.
estimator.fit(X_train, y_train)

Documentation

You can find a detailed documentation about FLAML here.

In addition, you can find:

• Research and blogposts around FLAML.

• Discord.

• Contributing guide.

• ML.NET documentation and tutorials for Model Builder, ML.NET CLI, and AutoML API.

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

If you are new to GitHub here is a detailed help source on getting involved with development on GitHub.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.