FLAML is a lightweight Python library that finds accurate machine learning models automatically, efficiently and economically. It frees users from selecting learners and hyperparameters for each learner.
- For common machine learning tasks like classification and regression, it quickly finds quality models for user-provided data with low computational resources. It supports both classifcal machine learning models and deep neural networks.
- 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 and evaluation code).
- It supports fast automatic tuning, capable of handling complex constraints/guidance/early stopping. FLAML is powered by a new, cost-effective hyperparameter optimization and learner selection method invented by Microsoft Research.
FLAML has a .NET implementation as well from ML.NET Model Builder in Visual Studio 2022. This ML.NET blog describes the improvement brought by FLAML.
FLAML requires Python version >= 3.7. It can be installed from pip:
pip install flamlTo run the notebook examples,
install flaml with the [notebook] option:
pip install flaml[notebook]- 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)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:
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