FLAML: A Fast Library for Automated Machine Learning and Tuning

2.5.0 · active · verified Tue Apr 14

FLAML (Fast Library for Automated Machine Learning) is an open-source Python library developed by Microsoft for efficient automation of machine learning and AI operations. It streamlines tasks such as model selection and hyperparameter optimization, and supports a wide range of models including classical machine learning algorithms, deep neural networks, and large language models. Currently at version 2.5.0, FLAML maintains an active development cycle, regularly releasing updates that include expanded Python version compatibility (e.g., Python 3.13 support), performance enhancements, and comprehensive documentation improvements.

Warnings

breaking FLAML's Python version requirements have become stricter. As of version 2.5.0, it requires Python >= 3.10 and < 3.14. Using older Python versions will result in installation failures or runtime errors.
Fix: Ensure your Python environment is version 3.10 or newer (e.g., 3.10, 3.11, 3.12, 3.13) and less than 3.14. Upgrade your Python installation if necessary.
breaking The `autogen` module, previously bundled with FLAML, has been moved to its own independent `autogen` library. Attempting to import `flaml.autogen` will fail.
Fix: Install the `autogen` library separately (`pip install autogen`) and import directly from `autogen` (e.g., `from autogen import ...`).
gotcha When integrating with Ray Tune, the import path for `tune.report` might cause issues if not updated for `ray>=2`. Older `ray` versions or inconsistent imports can lead to errors.
Fix: Ensure `ray` is installed as version 2 or higher (`pip install ray[tune]`). If explicitly using `tune.report`, use `from flaml import tune; tune.report(...)` or `from ray import tune; tune.report(...)` consistently within your training functions.
gotcha When using `flaml.tune.run` for hyperparameter optimization, a warning might appear regarding missing 'low_cost_partial_config'. This parameter is crucial for cost-frugal search, and omitting it can lead to less efficient tuning.
Fix: Provide a `low_cost_partial_config` argument to `tune.run` that specifies hyperparameter values corresponding to low training costs. Refer to FLAML's documentation for examples.
gotcha In specific deployment environments (e.g., Snowflake Snowpark), the default log file path set in `automl_settings` might not be writable or accessible, causing failures. The path `iris.log` in the quickstart is relative to the current working directory, which might not be appropriate in sandboxed environments.
Fix: Explicitly set `log_file_name` in your `automl_settings` to a known writable and accessible path within the environment, such as `/tmp/your_custom_log.log` on Linux-based systems or cloud environments.

Install

pip install flaml Base Installation
pip install "flaml[automl]" With core AutoML dependencies (recommended)

Imports

AutoML
```
from flaml import AutoML
```
tune
```
from flaml import tune
```
Used for generic hyperparameter tuning of user-defined functions.
LGBMRegressor
```
from flaml.default import LGBMRegressor
```
For Zero-shot AutoML, enabling automatic hyperparameter selection for LightGBM with existing API.
autogen
```
import autogen
```
The `autogen` module has been moved to its own dedicated `autogen` library. Install it separately (`pip install autogen`) and import directly.

Quickstart

This quickstart demonstrates how to perform a basic classification task using FLAML's AutoML functionality. It loads the Iris dataset, splits it into training and testing sets, initializes an `AutoML` instance, and trains a model within a specified time budget. The best model and its performance metric are then printed, followed by sample predictions.

from flaml import AutoML
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

# Load a sample dataset
X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Initialize AutoML
automl = AutoML()

# Define settings for AutoML
automl_settings = {
    "time_budget": 10,  # in seconds
    "metric": "accuracy",
    "task": "classification",
    "log_file_name": "flaml_iris.log", # Optional: logs will be saved here
}

# Train the AutoML model
print("Starting AutoML training...")
automl.fit(X_train=X_train, y_train=y_train, **automl_settings)
print("AutoML training finished.")

# Best model details
print(f"Best estimator: {automl.model.estimator}")
print(f"Best metric: {automl.best_result['accuracy']}")

# Make predictions
predictions = automl.predict(X_test)
print(f"Sample predictions: {predictions[:5]}")

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