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https://github.com/microsoft/flaml
A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
https://github.com/microsoft/flaml
automated-machine-learning automl classification data-science deep-learning finetuning hyperparam hyperparameter-optimization jupyter-notebook machine-learning natural-language-generation natural-language-processing python random-forest regression scikit-learn tabular-data timeseries-forecasting tuning
Last synced: about 2 hours ago
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A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
- Host: GitHub
- URL: https://github.com/microsoft/flaml
- Owner: microsoft
- License: mit
- Created: 2020-08-20T20:46:11.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2024-09-22T03:08:44.000Z (3 days ago)
- Last Synced: 2024-09-22T06:31:04.582Z (2 days ago)
- Topics: automated-machine-learning, automl, classification, data-science, deep-learning, finetuning, hyperparam, hyperparameter-optimization, jupyter-notebook, machine-learning, natural-language-generation, natural-language-processing, python, random-forest, regression, scikit-learn, tabular-data, timeseries-forecasting, tuning
- Language: Jupyter Notebook
- Homepage: https://microsoft.github.io/FLAML/
- Size: 196 MB
- Stars: 3,858
- Watchers: 60
- Forks: 506
- Open Issues: 228
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATION.cff
- Security: SECURITY.md
Awesome Lists containing this project
README
[](https://badge.fury.io/py/FLAML)
[](https://github.com/microsoft/FLAML/actions/workflows/python-package.yml)
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[](https://pepy.tech/project/flaml)
[](https://discord.gg/Cppx2vSPVP)
# A Fast Library for Automated Machine Learning & Tuning
: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 support, along with a range of new estimators, and comprehensive integration with MLflow—thanks to contributions from the Microsoft Fabric product team.
:fire: Heads-up: We have migrated [AutoGen](https://microsoft.github.io/autogen/) into a dedicated [github repository](https://github.com/microsoft/autogen). Alongside this move, we have also launched a dedicated [Discord](https://discord.gg/pAbnFJrkgZ) server and a [website](https://microsoft.github.io/autogen/) for comprehensive documentation.
:fire: The automated multi-agent chat framework in [AutoGen](https://microsoft.github.io/autogen/) is in preview from v2.0.0.
:fire: FLAML is highlighted in OpenAI's [cookbook](https://github.com/openai/openai-cookbook#related-resources-from-around-the-web).
:fire: [autogen](https://microsoft.github.io/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).
## 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](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.
FLAML has a .NET implementation in [ML.NET](http://dot.net/ml), an open-source, cross-platform machine learning framework for .NET.
## Installation
FLAML requires **Python version >= 3.8**. It can be installed from pip:
```bash
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`](https://microsoft.github.io/autogen/) package.
```bash
pip install "flaml[autogen]"
```
Find more options in [Installation](https://microsoft.github.io/FLAML/docs/Installation).
Each of the [`notebook examples`](https://github.com/microsoft/FLAML/tree/main/notebook) may require a specific option to be installed.
## Quickstart
- (New) The [autogen](https://microsoft.github.io/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,
```python
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.
```python
# 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](https://microsoft.github.io/FLAML/docs/Use-Cases/Task-Oriented-AutoML).
```python
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](https://microsoft.github.io/FLAML/docs/Use-Cases/Task-Oriented-AutoML#estimator-and-search-space).
```python
automl.fit(X_train, y_train, task="classification", estimator_list=["lgbm"])
```
- You can also run generic hyperparameter tuning for a [custom function](https://microsoft.github.io/FLAML/docs/Use-Cases/Tune-User-Defined-Function).
```python
from flaml import tune
tune.run(evaluation_function, config={…}, low_cost_partial_config={…}, time_budget_s=3600)
```
- [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.
```python
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](https://microsoft.github.io/FLAML/).
In addition, you can find:
- [Research](https://microsoft.github.io/FLAML/docs/Research) and [blogposts](https://microsoft.github.io/FLAML/blog) around FLAML.
- [Discord](https://discord.gg/Cppx2vSPVP).
- [Contributing guide](https://microsoft.github.io/FLAML/docs/Contribute).
- 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).
## 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 .
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.
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](https://opensource.microsoft.com/codeofconduct/).
For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
contact [[email protected]](mailto:[email protected]) with any additional questions or comments.
## Contributors Wall
