# Getting Started 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. ### Main Features - 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. - It supports fast and economical automatic tuning, 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](/docs/Research) from Microsoft Research and collaborators such as Penn State University, Stevens Institute of Technology, University of Washington, and University of Waterloo. ### Quickstart Install FLAML from pip: `pip install flaml` (**requires Python >= 3.10**). Find more options in [Installation](/docs/Installation). There are several ways of using flaml: #### [Task-oriented AutoML](/docs/Use-Cases/task-oriented-automl) With three lines of code, you can start using this economical and fast AutoML engine as a scikit-learn style estimator. ```python from flaml import AutoML automl = AutoML() automl.fit(X_train, y_train, task="classification", time_budget=60) ``` It automatically tunes the hyperparameters and selects the best model from default learners such as LightGBM, XGBoost, random forest etc. for the specified time budget 60 seconds. [Customizing](/docs/Use-Cases/task-oriented-automl#customize-automlfit) the optimization metrics, learners and search spaces etc. is very easy. For example, ```python automl.add_learner("mylgbm", MyLGBMEstimator) automl.fit( X_train, y_train, task="classification", metric=custom_metric, estimator_list=["mylgbm"], time_budget=60, ) ``` #### [Tune user-defined function](/docs/Use-Cases/Tune-User-Defined-Function) You can run generic hyperparameter tuning for a custom function (machine learning or beyond). For example, ```python from flaml import tune from flaml.automl.model import LGBMEstimator def train_lgbm(config: dict) -> dict: # convert config dict to lgbm params params = LGBMEstimator(**config).params # train the model train_set = lightgbm.Dataset(csv_file_name) model = lightgbm.train(params, train_set) # evaluate the model pred = model.predict(X_test) mse = mean_squared_error(y_test, pred) # return eval results as a dictionary return {"mse": mse} # load a built-in search space from flaml flaml_lgbm_search_space = LGBMEstimator.search_space(X_train.shape) # specify the search space as a dict from hp name to domain; you can define your own search space same way config_search_space = { hp: space["domain"] for hp, space in flaml_lgbm_search_space.items() } # give guidance about hp values corresponding to low training cost, i.e., {"n_estimators": 4, "num_leaves": 4} low_cost_partial_config = { hp: space["low_cost_init_value"] for hp, space in flaml_lgbm_search_space.items() if "low_cost_init_value" in space } # run the tuning, minimizing mse, with total time budget 3 seconds analysis = tune.run( train_lgbm, metric="mse", mode="min", config=config_search_space, low_cost_partial_config=low_cost_partial_config, time_budget_s=3, num_samples=-1, ) ``` Please see this [script](https://github.com/microsoft/FLAML/blob/main/test/tune_example.py) for the complete version of the above example. #### [Zero-shot AutoML](/docs/Use-Cases/Zero-Shot-AutoML) FLAML offers a unique, seamless and effortless way to leverage AutoML for the commonly used classifiers and regressors such as LightGBM and XGBoost. For example, if you are using `lightgbm.LGBMClassifier` as your current learner, all you need to do is to replace `from lightgbm import LGBMClassifier` by: ```python from flaml.default import LGBMClassifier ``` Then, you can use it just like you use the original `LGMBClassifier`. Your other code can remain unchanged. When you call the `fit()` function from `flaml.default.LGBMClassifier`, it will automatically instantiate a good data-dependent hyperparameter configuration for your dataset, which is expected to work better than the default configuration. ### Where to Go Next? - Understand the use cases for [Task-oriented AutoML](/docs/Use-Cases/Task-Oriented-Automl), [Tune user-defined function](/docs/Use-Cases/Tune-User-Defined-Function) and [Zero-shot AutoML](/docs/Use-Cases/Zero-Shot-AutoML). - Find code examples under "Examples": from [AutoML - Classification](/docs/Examples/AutoML-Classification) to [Tune - PyTorch](/docs/Examples/Tune-PyTorch). - Learn about [research](/docs/Research) around FLAML and check [blogposts](/blog). - Apply practical guidance in [Best Practices](/docs/Best-Practices). - Chat on [Discord](https://discord.gg/Cppx2vSPVP). If you like our project, please give it a [star](https://github.com/microsoft/FLAML/stargazers) on GitHub. If you are interested in contributing, please read [Contributor's Guide](/docs/Contribute).