A fast library for automated machine learning and tuning
Project description
A Fast Library for Automated Machine Learning & Tuning
:fire: v1.2.0 is released with support for ChatGPT and GPT-4.
What is FLAML
FLAML is a lightweight Python library for efficient automation of machine learning, including selection of models, hyperparameters, and other tunable choices of an application (e.g., inference hyperparameters for foundation models, configurations in MLOps/LMOps workflows, pipelines, mathematical/statistical models, algorithms, computing experiments, software configurations).
- For foundation models like the GPT series, it automates the experimentation and optimization of their inference performance to maximize the effectiveness for downstream applications and minimize the inference cost.
- 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: minimal customization (computational resource budget), medium customization (e.g., scikit-style learner, search space and metric), or full customization (arbitrary training/inference/evaluation code).
- It supports fast automatic tuning, capable of handling complex constraints/guidance/early stopping. FLAML is powered by a cost-effective hyperparameter optimization and model selection method invented by Microsoft Research, and many followup research studies.
FLAML has a .NET implementation in ML.NET, an open-source, cross-platform machine learning framework for .NET. In ML.NET, you can use FLAML via low-code solutions like Model Builder Visual Studio extension and the cross-platform ML.NET CLI. Alternatively, you can use the ML.NET AutoML API for a code-first experience.
Installation
Python
FLAML requires Python version >= 3.7. It can be installed from pip:
pip install flaml
To run the notebook examples,
install flaml with the [notebook] option:
pip install flaml[notebook]
.NET
Use the following guides to get started with FLAML in .NET:
Quickstart
- (New) You can optimize generations by ChatGPT or GPT-4 etc. with your own tuning data, success metrics and budgets.
from flaml import oai
config, analysis = oai.Completion.tune(
data=tune_data,
metric="success",
mode="max",
eval_func=eval_func,
inference_budget=0.05,
optimization_budget=3,
num_samples=-1,
)
The automated experimentation and optimization can help you maximize the utility out of these expensive models. A suite of utilities such as caching and templating are offered to accelerate the experimentation and application development.
- 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 where you can find the API documentation, use cases and examples.
In addition, you can find:
-
Research around FLAML here.
-
Discord here.
-
Contributing guide here.
-
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.
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