autogen/website/docs/Getting-Started.md

# Getting Started

<!-- ### Welcome to FLAML, a Fast Library for Automated Machine Learning & Tuning! -->

FLAML is a lightweight Python library for efficient automation of machine
learning, including selection of
models, hyperparameters, and other tunable choices of an application.

### Main Features

* 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). Users can customize only when and what they need to, and leave the rest to the library.
* 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 [cost-effective
hyperparameter optimization](Use-Cases/Tune-User-Defined-Function#hyperparameter-optimization-algorithm)
and model selection method invented by Microsoft Research, and many followup [research studies](Research).

### Quickstart

Install FLAML from pip: `pip install flaml`. Find more options in [Installation](Installation).

There are several ways of using flaml:

#### (New) [Auto Generation](Use-Cases/Auto-Generation)

For example, you can optimize generations by ChatGPT or GPT-4 etc. with your own tuning data, success metrics and budgets.

```python
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.

#### [Task-oriented AutoML](Use-Cases/task-oriented-automl)

For example, 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](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](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](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 [Auto Generation](Use-Cases/Auto-Generation), [Task-oriented AutoML](Use-Cases/Task-Oriented-Automl), [Tune user-defined function](Use-Cases/Tune-User-Defined-Function) and [Zero-shot AutoML](Use-Cases/Zero-Shot-AutoML).
* Find code examples under "Examples": from [AutoGen - OpenAI](Examples/AutoGen-OpenAI) to [Tune - PyTorch](Examples/Tune-PyTorch).
* Learn about [research](Research) around FLAML.
* 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](Contribute).

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