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FLAML

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What FLAML is

FLAML is a free, MIT-licensed library for automated machine learning and tuning. It aims to find accurate models or configurations for common ML and AI tasks using low computational resources, and can adapt large language models to applications to reduce monetary costs. Its AutoML tasks include classification, regression, time-series and panel forecasting, learning to rank, and sequence classification. The flaml.default package recommends data-dependent hyperparameter defaults without runtime tuning. Tuning tools handle large search spaces, varied evaluation costs, constraints, guidance and early stopping; users can restrict the learners tried or tune a customized learner. Python installation requires version 3.10 or later, and the latest version requires earlier than 3.14. Optional integrations include OpenAI models, CatBoost, Prophet, Hugging Face Transformers, Ray and Spark, among others. Ray or Spark can be used for parallel tuning, but not together in one job; GPU training is not supported with Spark as the parallel backend. FLAML also has a .NET implementation through ML.NET.

Who it suits

FLAML suits developers and machine-learning practitioners who want automated model selection or tuning, including for forecasting and ranking tasks.

What it does well

MIT-licensed and free to use.
Supports classification, regression, forecasting and ranking.
Can recommend defaults without runtime tuning.
Supports parallel tuning with Ray or Spark.
Offers a .NET implementation through ML.NET.

What to know first

Python installation requires version 3.10 or newer.
GPU training is unsupported with Spark as the backend.
A tuning job cannot use Ray and Spark together.

Getting started

Open the FLAML website.
Use Python 3.10 or later and earlier than 3.14 for the latest version.
Install the Python library with pip or conda.
Add optional packages for features such as AutoML, Hugging Face Transformers, Ray, or Spark.
Choose Ray or Spark if you need parallel tuning; a job cannot use both.

Verdict

FLAML offers automated model selection and tuning across several ML tasks, with options for constrained searches and distributed tuning. Check its Python version requirement and Spark GPU limitation before choosing a setup.

Questions

How much does FLAML cost?

FLAML is free, and a free plan is listed.

Is FLAML open source?

Yes. It is distributed under the MIT License, subject to that license’s conditions.

Which machine learning tasks does it support?

Its listed AutoML tasks include classification, regression, time-series forecasting, panel forecasting, learning to rank, and sequence classification.

What Python version does it require?

Installation requires Python 3.10 or newer. The latest version requires a Python version earlier than 3.14.

Can it use Ray and Spark together for parallel tuning?

No. A tuning job can use Ray or Spark, but not both; GPU training is unsupported when Spark is the parallel backend.

Does FLAML have a .NET implementation?

Yes. A .NET implementation is available through the cross-platform ML.NET framework, including Model Builder, the ML.NET CLI, and Microsoft.ML.AutoML.

Facts from FLAML's own pages

Auto-tuning: Its tuning approach handles large search spaces, heterogeneous evaluation costs, complex constraints, guidance, and early stopping. · microsoft.github.io, 1 Oct 2026
Availability: The repository identifies FLAML as MIT-licensed open-source software. · github.com, 2 Oct 2026
Community support: The project provides community support through Discord, issues, discussions, and contributions. · microsoft.github.io, 1 Oct 2026
Customization: Users can restrict the learners FLAML tries or tune a customized learner. · github.com, 2 Oct 2026
Distributed integrations: FLAML provides optional Ray and Spark distributed tuning, plus NNI, BlendSearch, and Azure Synapse options. · microsoft.github.io, 1 Oct 2026
Installation: The Python library can be installed with pip or conda, and optional packages enable features such as AutoML, Hugging Face Transformers, Ray, and Spark. · microsoft.github.io, 2 Oct 2026
Integrations: The documentation describes MLflow logging and integration with Azure Machine Learning. · microsoft.github.io, 2 Oct 2026
License: The repository is distributed under the MIT License, permitting users to use, copy, modify, publish, distribute, sublicense, and sell the software subject to its conditions. · github.com, 1 Oct 2026
LLM adaptation: FLAML automatically adapts large language models to applications to reduce monetary costs. · microsoft.github.io, 1 Oct 2026
Microsoft Fabric: The repository says FLAML supports AutoML and hyperparameter tuning in Microsoft Fabric Data Science. · github.com, 2 Oct 2026
Model integrations: Optional installations include OpenAI models, CatBoost, Vowpal Wabbit, Prophet, statsmodels, and Hugging Face Transformers. · microsoft.github.io, 1 Oct 2026
.NET support: FLAML has a .NET implementation in the cross-platform ML.NET framework, including Model Builder, the ML.NET CLI, and Microsoft.ML.AutoML. · microsoft.github.io, 1 Oct 2026
Parallel tuning: FLAML supports Ray and Spark backends for parallel tuning, but a tuning job cannot use both. · microsoft.github.io, 2 Oct 2026
Purpose: FLAML automates machine learning model selection and hyperparameter tuning with low computational resources. · microsoft.github.io, 2 Oct 2026
Python requirement: The installation documentation requires Python version 3.10 or newer. · microsoft.github.io, 1 Oct 2026
Resource efficiency: It finds accurate models or configurations for common ML/AI tasks with low computational resources. · microsoft.github.io, 1 Oct 2026
Runtime requirement: The repository specifies Python 3.10 or later and earlier than 3.14 for its latest version. · github.com, 2 Oct 2026
Security reporting: The project directs security vulnerability reports to Microsoft Security Response Center for coordinated disclosure rather than public issues. · github.com, 1 Oct 2026
Spark limitation: GPU training is not supported when Spark is used as the parallel backend. · microsoft.github.io, 1 Oct 2026
Support: The official site links to a Discord community. · microsoft.github.io, 2 Oct 2026
Supported tasks: Task-oriented AutoML supports classification, regression, time-series forecasting, panel forecasting, learning to rank, and sequence classification. · microsoft.github.io, 1 Oct 2026
Tasks: The documentation lists classification, regression, forecasting, and ranking among the supported AutoML tasks. · microsoft.github.io, 2 Oct 2026
Tuning: Its tuning tool handles large search spaces with varied evaluation costs, constraints, guidance, and early stopping. · microsoft.github.io, 2 Oct 2026
Zero-shot AutoML: The flaml.default package recommends data-dependent hyperparameter defaults without runtime tuning. · microsoft.github.io, 1 Oct 2026

Where FLAML stands on Freedom251

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