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127 lines
7.1 KiB
Markdown
127 lines
7.1 KiB
Markdown
[](https://badge.fury.io/py/FLAML)
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[](https://github.com/microsoft/FLAML/actions/workflows/python-package.yml)
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[](https://pypi.org/project/FLAML/)
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[](https://pepy.tech/project/flaml)
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[](https://discord.gg/Cppx2vSPVP)
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<!-- [](https://gitter.im/FLAMLer/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) -->
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# A Fast Library for Automated Machine Learning & Tuning
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<p align="center">
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<img src="https://github.com/microsoft/FLAML/blob/main/website/static/img/flaml.svg" width=200>
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<br>
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</p>
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:fire: FLAML supports AutoML and Hyperparameter Tuning in [Microsoft Fabric Data Science](https://learn.microsoft.com/en-us/fabric/data-science/automated-machine-learning-fabric). In addition, we've introduced Python 3.11 and 3.12 support, along with a range of new estimators, and comprehensive integration with MLflow—thanks to contributions from the Microsoft Fabric product team.
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:fire: Heads-up: [AutoGen](https://microsoft.github.io/autogen/) has moved to a dedicated [GitHub repository](https://github.com/microsoft/autogen). FLAML no longer includes the `autogen` module—please use AutoGen directly.
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## What is FLAML
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FLAML is a lightweight Python library for efficient automation of machine
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learning and AI operations. It automates workflow based on large language models, machine learning models, etc.
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and optimizes their performance.
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- FLAML enables economical automation and tuning for ML/AI workflows, including model selection and hyperparameter optimization under resource constraints.
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- 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.
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- 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.
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FLAML is powered by a series of [research studies](https://microsoft.github.io/FLAML/docs/Research/) from Microsoft Research and collaborators such as Penn State University, Stevens Institute of Technology, University of Washington, and University of Waterloo.
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FLAML has a .NET implementation in [ML.NET](http://dot.net/ml), an open-source, cross-platform machine learning framework for .NET.
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## Installation
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FLAML requires **Python version >= 3.9**. It can be installed from pip:
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```bash
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pip install flaml
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```
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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 [`automl`](https://microsoft.github.io/FLAML/docs/Use-Cases/Task-Oriented-AutoML) module.
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```bash
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pip install "flaml[automl]"
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```
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Find more options in [Installation](https://microsoft.github.io/FLAML/docs/Installation).
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Each of the [`notebook examples`](https://github.com/microsoft/FLAML/tree/main/notebook) may require a specific option to be installed.
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## Quickstart
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- With three lines of code, you can start using this economical and fast
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AutoML engine as a [scikit-learn style estimator](https://microsoft.github.io/FLAML/docs/Use-Cases/Task-Oriented-AutoML).
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```python
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from flaml import AutoML
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automl = AutoML()
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automl.fit(X_train, y_train, task="classification")
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```
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- You can restrict the learners and use FLAML as a fast hyperparameter tuning
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tool for XGBoost, LightGBM, Random Forest etc. or a [customized learner](https://microsoft.github.io/FLAML/docs/Use-Cases/Task-Oriented-AutoML#estimator-and-search-space).
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```python
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automl.fit(X_train, y_train, task="classification", estimator_list=["lgbm"])
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```
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- You can also run generic hyperparameter tuning for a [custom function](https://microsoft.github.io/FLAML/docs/Use-Cases/Tune-User-Defined-Function).
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```python
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from flaml import tune
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tune.run(
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evaluation_function, config={…}, low_cost_partial_config={…}, time_budget_s=3600
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)
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```
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- [Zero-shot AutoML](https://microsoft.github.io/FLAML/docs/Use-Cases/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.
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```python
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from flaml.default import LGBMRegressor
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# Use LGBMRegressor in the same way as you use lightgbm.LGBMRegressor.
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estimator = LGBMRegressor()
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# The hyperparameters are automatically set according to the training data.
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estimator.fit(X_train, y_train)
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```
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## Documentation
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You can find a detailed documentation about FLAML [here](https://microsoft.github.io/FLAML/).
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In addition, you can find:
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- [Research](https://microsoft.github.io/FLAML/docs/Research) and [blogposts](https://microsoft.github.io/FLAML/blog) around FLAML.
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- [Discord](https://discord.gg/Cppx2vSPVP).
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- [Contributing guide](https://microsoft.github.io/FLAML/docs/Contribute).
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- ML.NET documentation and tutorials for [Model Builder](https://learn.microsoft.com/dotnet/machine-learning/tutorials/predict-prices-with-model-builder), [ML.NET CLI](https://learn.microsoft.com/dotnet/machine-learning/tutorials/sentiment-analysis-cli), and [AutoML API](https://learn.microsoft.com/dotnet/machine-learning/how-to-guides/how-to-use-the-automl-api).
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## Contributing
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This project welcomes contributions and suggestions. Most contributions require you to agree to a
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Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
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the rights to use your contribution. For details, visit <https://cla.opensource.microsoft.com>.
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If you are new to GitHub [here](https://help.github.com/categories/collaborating-with-issues-and-pull-requests/) is a detailed help source on getting involved with development on GitHub.
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When you submit a pull request, a CLA bot will automatically determine whether you need to provide
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a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
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provided by the bot. You will only need to do this once across all repos using our CLA.
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This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
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For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
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contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.
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## Contributors Wall
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<a href="https://github.com/microsoft/flaml/graphs/contributors">
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<img src="https://contrib.rocks/image?repo=microsoft/flaml&max=204" />
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</a>
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