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* add readme * migration headsup * remove move date * Update README.md Co-authored-by: Chi Wang <wang.chi@microsoft.com> --------- Co-authored-by: Chi Wang <wang.chi@microsoft.com>
148 lines
8.8 KiB
Markdown
148 lines
8.8 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://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: Heads-up: We're preparing to migrate [autogen](https://microsoft.github.io/FLAML/docs/Use-Cases/Autogen) into a dedicated github repository. Alongside this move, we'll also launch a dedicated Discord server and a website for comprehensive documentation.
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:fire: The automated multi-agent chat framework in [autogen](https://microsoft.github.io/FLAML/docs/Use-Cases/Autogen) is in preview from v2.0.0.
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:fire: FLAML is highlighted in OpenAI's [cookbook](https://github.com/openai/openai-cookbook#related-resources-from-around-the-web).
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:fire: [autogen](https://microsoft.github.io/FLAML/docs/Use-Cases/Autogen) is released with support for ChatGPT and GPT-4, based on [Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference](https://arxiv.org/abs/2303.04673).
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:fire: FLAML supports Code-First AutoML & Tuning – Private Preview in [Microsoft Fabric Data Science](https://learn.microsoft.com/en-us/fabric/data-science/).
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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 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.
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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](/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 >= 3.8**. 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 [`autogen`](https://microsoft.github.io/FLAML/docs/Use-Cases/Autogen) package.
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```bash
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pip install "flaml[autogen]"
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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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* (New) The [autogen](https://microsoft.github.io/FLAML/docs/Use-Cases/Autogen) package enables the next-gen GPT-X applications with a generic multi-agent conversation framework.
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It offers customizable and conversable agents which integrate LLMs, tools and human.
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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,
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```python
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from flaml import autogen
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assistant = autogen.AssistantAgent("assistant")
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user_proxy = autogen.UserProxyAgent("user_proxy")
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user_proxy.initiate_chat(assistant, message="Show me the YTD gain of 10 largest technology companies as of today.")
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# This initiates an automated chat between the two agents to solve the task
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```
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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.
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```python
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# perform tuning
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config, analysis = autogen.Completion.tune(
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data=tune_data,
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metric="success",
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mode="max",
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eval_func=eval_func,
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inference_budget=0.05,
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optimization_budget=3,
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num_samples=-1,
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)
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# perform inference for a test instance
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response = autogen.Completion.create(context=test_instance, **config)
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```
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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(evaluation_function, config={…}, low_cost_partial_config={…}, time_budget_s=3600)
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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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