autogen/website/docs/Getting-Started.md

• Getting Started
• • • Main Features
    • Quickstart
    • • (New) Auto Generation
      • Task-oriented AutoML
      • Tune user-defined function
      • Zero-shot AutoML
    • Where to Go Next?

Getting Started

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

Main Features

• For foundation models like the GPT models, it automates the experimentation and optimization of their performance to maximize the effectiveness for applications and minimize the inference cost. FLAML enables users to build and use adaptive AI agents with minimal effort.
• 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., search space and metric), or full customization (arbitrary training/inference/evaluation code).
• 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
  and model selection method invented by Microsoft Research, and many followup research studies.

Quickstart

Install FLAML from pip: pip install flaml. Find more options in Installation.

There are several ways of using flaml:

(New) Auto Generation

Maximize the utility out of the expensive LLMs such as ChatGPT and GPT-4, including:

• 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.

from flaml import oai

# perform tuning
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,
)

# perform inference for a test instance
response = oai.Completion.create(context=test_instance, **config)

• LLM-driven intelligent agents which can perform tasks autonomously or with human feedback, including tasks that require using tools via code. For example,

assistant = AssistantAgent("assistant")
user = UserProxyAgent("user", human_input_mode="TERMINATE")
assistant.receive("Draw a rocket and save to a file named 'rocket.svg'")

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.

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 the optimization metrics, learners and search spaces etc. is very easy. For example,

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

You can run generic hyperparameter tuning for a custom function (machine learning or beyond). For example,

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 for the complete version of the above example.

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:

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, Task-oriented AutoML, Tune user-defined function and Zero-shot AutoML.
• Find code examples under "Examples": from AutoGen - OpenAI to Tune - PyTorch.
• Learn about research around FLAML and check blogposts.
• Chat on Discord.

If you like our project, please give it a star on GitHub. If you are interested in contributing, please read Contributor's Guide.